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      "refetch_note": "Live post may have changed since retrieval; the SHA-256 is of raw HTML bytes and may not reproduce for dynamic Substack markup.",
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      "refetch_note": "Live post may have changed since retrieval; the SHA-256 is of raw HTML bytes and may not reproduce for dynamic Substack markup.",
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      "refetch_note": "Live post may have changed since retrieval; the SHA-256 is of raw HTML bytes and may not reproduce for dynamic Substack markup.",
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   {
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    "hash": "d019d2477c32d933cf332d3b93bb0203f13b3ef67c24a2d7657b53e1f81585c8",
    "payload": {
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       "Dylan Patel"
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      "bytes": 191405,
      "canonical_url": "https://newsletter.semianalysis.com/p/from-tokens-to-burgers-a-water-footprint",
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      "paywalled": true,
      "preview_note": "Preview text truncated to 600 chars for this packet. The lane extractor captured page-head markup (CSS/JSON-LD) ahead of article prose, so the excerpt and free_preview_word_count are extractor output, not a clean count of article words.",
      "published": "2026-01-15T18:54:49+00:00",
      "refetch_note": "Live post may have changed since retrieval; the SHA-256 is of raw HTML bytes and may not reproduce for dynamic Substack markup.",
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     "deps": [],
     "disposition": "observed",
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   {
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    "payload": {
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      "canonical_url": "https://newsletter.semianalysis.com/p/fungible-dpus-are-dead-carcass-acquired",
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      "paywalled": true,
      "preview_note": "Preview text truncated to 600 chars for this packet. The lane extractor captured page-head markup (CSS/JSON-LD) ahead of article prose, so the excerpt and free_preview_word_count are extractor output, not a clean count of article words.",
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      "refetch_note": "Live post may have changed since retrieval; the SHA-256 is of raw HTML bytes and may not reproduce for dynamic Substack markup.",
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     "disposition": "observed",
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     "revision": 1,
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   {
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    "payload": {
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      "companies_mentioned_in_free_text": [
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      "preview_note": "Preview text truncated to 600 chars for this packet. The lane extractor captured page-head markup (CSS/JSON-LD) ahead of article prose, so the excerpt and free_preview_word_count are extractor output, not a clean count of article words.",
      "published": "2024-07-17T05:07:55+00:00",
      "refetch_note": "Live post may have changed since retrieval; the SHA-256 is of raw HTML bytes and may not reproduce for dynamic Substack markup.",
      "retrieved_utc": "2026-09-29T20:09:52.755152Z",
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      "slug": "gb200-hardware-architecture-and-component",
      "subtitle": "UNVERIFIED",
      "url": "https://newsletter.semianalysis.com/p/gb200-hardware-architecture-and-component"
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     "deps": [],
     "disposition": "observed",
     "id": "sa-post-gb200-hardware-architecture-and-component",
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     "revision": 1,
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     "tier": "public_observation",
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   {
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    "payload": {
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       "Doug O'Laughlin",
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      "bytes": 198705,
      "canonical_url": "https://newsletter.semianalysis.com/p/gemini-is-cooked-but-gcp-is-cooking",
      "companies_mentioned_in_free_text": [
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      "duplicate_extractions": null,
      "free_preview_excerpt": "Gemini is Cooked but GCP is Cooking @layer legacy, pencraftReset, pencraft; @font-face{font-family:'Spectral';font-style:italic;font-weight:400;font-display:fallback;src:url(https://fonts.gstatic.com/s/spectral/v13/rnCt-xNNww_2s0amA9M8on7mTNmnUHowCw.woff2) format('woff2');unicode-range:U+0301,U+0400-045F,U+0490-0491,U+04B0-04B1,U+2116}@font-face{font-family:'Spectral';font-style:italic;font-weight:400;font-display:fallback;src:url(https://fonts.gstatic.com/s/spectral/v13/rnCt-xNNww_2s0amA9M8onXmTNmnUHowCw.woff2) format('woff2');unicode-range:U+0102-0103,U+0110-0111,U+0128-0129,U+0168-0169,U+01",
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      "paywalled": true,
      "preview_note": "Preview text truncated to 600 chars for this packet. The lane extractor captured page-head markup (CSS/JSON-LD) ahead of article prose, so the excerpt and free_preview_word_count are extractor output, not a clean count of article words.",
      "published": "2026-08-07T02:32:44+00:00",
      "refetch_note": "Live post may have changed since retrieval; the SHA-256 is of raw HTML bytes and may not reproduce for dynamic Substack markup.",
      "retrieved_utc": "2026-09-29T20:07:55.229380Z",
      "sha256": "f09fb5baa5a56c26b67d6168b8363d4a1276994f07460bb4bf99529c3f3b43d9",
      "sha256_of": "raw HTML of the post as fetched (GET https://newsletter.semianalysis.com/p/gemini-is-cooked-but-gcp-is-cooking)",
      "slug": "gemini-is-cooked-but-gcp-is-cooking",
      "subtitle": "UNVERIFIED",
      "url": "https://newsletter.semianalysis.com/p/gemini-is-cooked-but-gcp-is-cooking"
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     "deps": [],
     "disposition": "observed",
     "id": "sa-post-gemini-is-cooked-but-gcp-is-cooking",
     "kind": "source",
     "revision": 1,
     "summary": "Paywalled SemiAnalysis newsletter post, published 2026-08-07. Free preview word count: 1830. UNVERIFIED",
     "tier": "public_observation",
     "title": "UNVERIFIED"
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    "prev": "d45841158a4961150ec5c791b3198f1eac922f56652f67f1e480ea405a584ab2",
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   {
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    "payload": {
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      "bytes": 235321,
      "canonical_url": "https://newsletter.semianalysis.com/p/globalfoundries-fotonix-the-leading",
      "companies_mentioned_in_free_text": [
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       "Nvidia",
       "AMD",
       "TSMC"
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      "duplicate_extractions": null,
      "free_preview_excerpt": "GlobalFoundries Fotonix, The Leading Silicon Photonics Foundry For Co-packaged Optics And Processing @layer legacy, pencraftReset, pencraft; @font-face{font-family:'Spectral';font-style:italic;font-weight:400;font-display:fallback;src:url(https://fonts.gstatic.com/s/spectral/v13/rnCt-xNNww_2s0amA9M8on7mTNmnUHowCw.woff2) format('woff2');unicode-range:U+0301,U+0400-045F,U+0490-0491,U+04B0-04B1,U+2116}@font-face{font-family:'Spectral';font-style:italic;font-weight:400;font-display:fallback;src:url(https://fonts.gstatic.com/s/spectral/v13/rnCt-xNNww_2s0amA9M8onXmTNmnUHowCw.woff2) format('woff2');u",
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      "paywalled": true,
      "preview_note": "Preview text truncated to 600 chars for this packet. The lane extractor captured page-head markup (CSS/JSON-LD) ahead of article prose, so the excerpt and free_preview_word_count are extractor output, not a clean count of article words.",
      "published": "2022-03-09T02:11:05+00:00",
      "refetch_note": "Live post may have changed since retrieval; the SHA-256 is of raw HTML bytes and may not reproduce for dynamic Substack markup.",
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      "sha256_of": "raw HTML of the post as fetched (GET https://newsletter.semianalysis.com/p/globalfoundries-fotonix-the-leading)",
      "slug": "globalfoundries-fotonix-the-leading",
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     "disposition": "observed",
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      "preview_note": "Preview text truncated to 600 chars for this packet. The lane extractor captured page-head markup (CSS/JSON-LD) ahead of article prose, so the excerpt and free_preview_word_count are extractor output, not a clean count of article words.",
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      "refetch_note": "Live post may have changed since retrieval; the SHA-256 is of raw HTML bytes and may not reproduce for dynamic Substack markup.",
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   {
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      "preview_note": "Preview text truncated to 600 chars for this packet. The lane extractor captured page-head markup (CSS/JSON-LD) ahead of article prose, so the excerpt and free_preview_word_count are extractor output, not a clean count of article words.",
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      "refetch_note": "Live post may have changed since retrieval; the SHA-256 is of raw HTML bytes and may not reproduce for dynamic Substack markup.",
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      "refetch_note": "Live post may have changed since retrieval; the SHA-256 is of raw HTML bytes and may not reproduce for dynamic Substack markup.",
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      "preview_note": "Preview text truncated to 600 chars for this packet. The lane extractor captured page-head markup (CSS/JSON-LD) ahead of article prose, so the excerpt and free_preview_word_count are extractor output, not a clean count of article words.",
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      "refetch_note": "Live post may have changed since retrieval; the SHA-256 is of raw HTML bytes and may not reproduce for dynamic Substack markup.",
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      "refetch_note": "Live post may have changed since retrieval; the SHA-256 is of raw HTML bytes and may not reproduce for dynamic Substack markup.",
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      "preview_note": "Preview text truncated to 600 chars for this packet. The lane extractor captured page-head markup (CSS/JSON-LD) ahead of article prose, so the excerpt and free_preview_word_count are extractor output, not a clean count of article words.",
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      "refetch_note": "Live post may have changed since retrieval; the SHA-256 is of raw HTML bytes and may not reproduce for dynamic Substack markup.",
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      "refetch_note": "Live post may have changed since retrieval; the SHA-256 is of raw HTML bytes and may not reproduce for dynamic Substack markup.",
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      "refetch_note": "Live post may have changed since retrieval; the SHA-256 is of raw HTML bytes and may not reproduce for dynamic Substack markup.",
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      "refetch_note": "Live post may have changed since retrieval; the SHA-256 is of raw HTML bytes and may not reproduce for dynamic Substack markup.",
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    "payload": {
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      "preview_note": "Preview text truncated to 600 chars for this packet. The lane extractor captured page-head markup (CSS/JSON-LD) ahead of article prose, so the excerpt and free_preview_word_count are extractor output, not a clean count of article words.",
      "published": "2021-10-14T21:57:33+00:00",
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   {
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    "payload": {
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      "canonical_url": "https://newsletter.semianalysis.com/p/tsmc-overseas-fabs-a-success",
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      "preview_note": "Preview text truncated to 600 chars for this packet. The lane extractor captured page-head markup (CSS/JSON-LD) ahead of article prose, so the excerpt and free_preview_word_count are extractor output, not a clean count of article words.",
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      "refetch_note": "Live post may have changed since retrieval; the SHA-256 is of raw HTML bytes and may not reproduce for dynamic Substack markup.",
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    "payload": {
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      "preview_note": "Preview text truncated to 600 chars for this packet. The lane extractor captured page-head markup (CSS/JSON-LD) ahead of article prose, so the excerpt and free_preview_word_count are extractor output, not a clean count of article words.",
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      "refetch_note": "Live post may have changed since retrieval; the SHA-256 is of raw HTML bytes and may not reproduce for dynamic Substack markup.",
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     "tier": "public_observation",
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      "preview_note": "Preview text truncated to 600 chars for this packet. The lane extractor captured page-head markup (CSS/JSON-LD) ahead of article prose, so the excerpt and free_preview_word_count are extractor output, not a clean count of article words.",
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      "refetch_note": "Live post may have changed since retrieval; the SHA-256 is of raw HTML bytes and may not reproduce for dynamic Substack markup.",
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     "disposition": "observed",
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      "preview_note": "Preview text truncated to 600 chars for this packet. The lane extractor captured page-head markup (CSS/JSON-LD) ahead of article prose, so the excerpt and free_preview_word_count are extractor output, not a clean count of article words.",
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      "refetch_note": "Live post may have changed since retrieval; the SHA-256 is of raw HTML bytes and may not reproduce for dynamic Substack markup.",
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     "tier": "public_observation",
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      "preview_note": "Preview text truncated to 600 chars for this packet. The lane extractor captured page-head markup (CSS/JSON-LD) ahead of article prose, so the excerpt and free_preview_word_count are extractor output, not a clean count of article words.",
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      "refetch_note": "Live post may have changed since retrieval; the SHA-256 is of raw HTML bytes and may not reproduce for dynamic Substack markup.",
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      "preview_note": "Preview text truncated to 600 chars for this packet. The lane extractor captured page-head markup (CSS/JSON-LD) ahead of article prose, so the excerpt and free_preview_word_count are extractor output, not a clean count of article words.",
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      "engineering_salary_raw": "2e5",
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      "line_item_id": "public_ip",
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      "note": "A calculator default in a public web app, not a market price observation. Non-numeric price values (for example \"included\") are kept as strings.",
      "prices": {
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     "title": "TCO assumption [Example]: Network / Public IP"
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     "title": "TCO assumption [Example]: Network / NAT gateway"
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     "title": "TCO assumption [Example]: Network / NAT processing"
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      "network_unit": "GiB",
      "note": "A calculator default in a public web app, not a market price observation. Non-numeric price values (for example \"included\") are kept as strings.",
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      "note": "A calculator default in a public web app, not a market price observation. Non-numeric price values (for example \"included\") are kept as strings.",
      "prices": {
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      "note": "A calculator default in a public web app, not a market price observation. Non-numeric price values (for example \"included\") are kept as strings.",
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      "note": "A calculator default in a public web app, not a market price observation. Non-numeric price values (for example \"included\") are kept as strings.",
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      "note": "A calculator default in a public web app, not a market price observation. Non-numeric price values (for example \"included\") are kept as strings.",
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     "revision": 1,
     "summary": "SemiAnalysis TCO calculator assumes Setup line \"Setup (POC)\" at 0/2/2 (weeks) gold/hyperscaler/silver (scenario \"Example\", quantity 0, calc poc_weeks).",
     "tier": "public_observation",
     "title": "TCO assumption [Example]: Setup / Setup (POC)"
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    "payload": {
     "data": {
      "assertion_type": "calculator_assumption",
      "calc": "eng_per_month",
      "description": "Lost productivity due to downtime; assume no credits from provider if SLA breached",
      "engineering_salary_raw": "2e5",
      "label": "Debugging",
      "line_item_id": "debugging",
      "network_unit": null,
      "note": "A calculator default in a public web app, not a market price observation. Non-numeric price values (for example \"included\") are kept as strings.",
      "prices": {
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       "hyperscaler": 1,
       "silver": 1
      },
      "quantity": 0,
      "retrieved_date": "2026-09-29",
      "scenario": "Example",
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      "storage_unit": null,
      "tier_order": [
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       "hyperscaler",
       "silver"
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      "units": "eng/mo",
      "url": "https://www.clustermax.ai/tco"
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       "revision": 1
      }
     ],
     "disposition": "open",
     "id": "tco-example-debugging",
     "kind": "claim",
     "revision": 1,
     "summary": "SemiAnalysis TCO calculator assumes Debugging line \"Debugging\" at 0/1/1 (eng/mo) gold/hyperscaler/silver (scenario \"Example\", quantity 0, calc eng_per_month).",
     "tier": "public_observation",
     "title": "TCO assumption [Example]: Debugging / Debugging"
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     "data": {
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      "calc": "qty_x_price_x_hrs",
      "description": "Equal pricing across providers; ~80% of cluster runs one large pretrain job",
      "engineering_salary_raw": "2e5",
      "label": "GB300 NVL72",
      "line_item_id": "gb300",
      "network_unit": null,
      "note": "A calculator default in a public web app, not a market price observation. Non-numeric price values (for example \"included\") are kept as strings.",
      "prices": {
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       "hyperscaler": 4,
       "silver": 4
      },
      "quantity": 5184,
      "retrieved_date": "2026-09-29",
      "scenario": "Large LLM Pretrain (GB300 NVL72)",
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      "sha256": "e54ecff3cdd5856a012718a0a351488a94a2e97536906bd02fd87a6569e4b5d1",
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      "storage_unit": null,
      "tier_order": [
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       "hyperscaler",
       "silver"
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      "units": "$/GPU/hr",
      "url": "https://www.clustermax.ai/tco"
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     ],
     "disposition": "open",
     "id": "tco-large-llm-pretrain-gb300-nvl72-gb300",
     "kind": "claim",
     "revision": 1,
     "summary": "SemiAnalysis TCO calculator assumes GB300 NVL72 $4.00/4.00/4.00 per GPU-hr gold/hyperscaler/silver (scenario \"Large LLM Pretrain (GB300 NVL72)\", quantity 5184).",
     "tier": "public_observation",
     "title": "TCO assumption [Large LLM Pretrain (GB300 NVL72)]: GPU / GB300 NVL72"
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     "data": {
      "assertion_type": "calculator_assumption",
      "calc": "pct_of_gpu",
      "description": "SageMaker HyperPod premium vs standard EC2 instance type",
      "engineering_salary_raw": "2e5",
      "label": "Orchestration",
      "line_item_id": "orchestration",
      "network_unit": null,
      "note": "A calculator default in a public web app, not a market price observation. Non-numeric price values (for example \"included\") are kept as strings.",
      "prices": {
       "gold": "included",
       "hyperscaler": 0,
       "silver": "included"
      },
      "quantity": 0,
      "retrieved_date": "2026-09-29",
      "scenario": "Large LLM Pretrain (GB300 NVL72)",
      "section": "GPU",
      "sha256": "e54ecff3cdd5856a012718a0a351488a94a2e97536906bd02fd87a6569e4b5d1",
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      "storage_unit": null,
      "tier_order": [
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       "hyperscaler",
       "silver"
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      "units": "% uplift",
      "url": "https://www.clustermax.ai/tco"
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     ],
     "disposition": "open",
     "id": "tco-large-llm-pretrain-gb300-nvl72-orchestration",
     "kind": "claim",
     "revision": 1,
     "summary": "SemiAnalysis TCO calculator assumes GPU line \"Orchestration\" at included/0/included (% uplift) gold/hyperscaler/silver (scenario \"Large LLM Pretrain (GB300 NVL72)\", quantity 0, calc pct_of_gpu).",
     "tier": "public_observation",
     "title": "TCO assumption [Large LLM Pretrain (GB300 NVL72)]: GPU / Orchestration"
    },
    "prev": "1177adf89d742e4d64f1f7eb4523217e78a4d19a31790d8ca2541ad59c254b61",
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    "payload": {
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      "calc": "qty_x_price_GiB",
      "description": "Checkpoints + active training data; Nebius discounts aggressively, silver uses warm-tier pricing",
      "engineering_salary_raw": "2e5",
      "label": "Hot (Lustre/Weka)",
      "line_item_id": "hot_storage",
      "network_unit": null,
      "note": "A calculator default in a public web app, not a market price observation. Non-numeric price values (for example \"included\") are kept as strings.",
      "prices": {
       "gold": 0.035,
       "hyperscaler": 0.0725,
       "silver": 0.055
      },
      "quantity": 500,
      "retrieved_date": "2026-09-29",
      "scenario": "Large LLM Pretrain (GB300 NVL72)",
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      "source_chunk_url": "https://www.clustermax.ai/_next/static/immutable/chunks/1bj4l-d6k82v1.js",
      "storage_unit": "TiB",
      "tier_order": [
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       "hyperscaler",
       "silver"
      ],
      "units": "$/GiB/mo",
      "url": "https://www.clustermax.ai/tco"
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     "deps": [
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     ],
     "disposition": "open",
     "id": "tco-large-llm-pretrain-gb300-nvl72-hot-storage",
     "kind": "claim",
     "revision": 1,
     "summary": "SemiAnalysis TCO calculator assumes Storage line \"Hot (Lustre/Weka)\" at 0.035/0.0725/0.055 ($/GiB/mo) gold/hyperscaler/silver (scenario \"Large LLM Pretrain (GB300 NVL72)\", quantity 500, calc qty_x_price_GiB).",
     "tier": "public_observation",
     "title": "TCO assumption [Large LLM Pretrain (GB300 NVL72)]: Storage / Hot (Lustre/Weka)"
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    "payload": {
     "data": {
      "assertion_type": "calculator_assumption",
      "calc": "qty_x_price_GiB",
      "description": "Dataset archive and long-term checkpoints",
      "engineering_salary_raw": "2e5",
      "label": "Cold (S3/Object)",
      "line_item_id": "cold_storage",
      "network_unit": null,
      "note": "A calculator default in a public web app, not a market price observation. Non-numeric price values (for example \"included\") are kept as strings.",
      "prices": {
       "gold": 0.01,
       "hyperscaler": 0.02,
       "silver": 0.015
      },
      "quantity": 10,
      "retrieved_date": "2026-09-29",
      "scenario": "Large LLM Pretrain (GB300 NVL72)",
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      "sha256": "e54ecff3cdd5856a012718a0a351488a94a2e97536906bd02fd87a6569e4b5d1",
      "source_chunk_url": "https://www.clustermax.ai/_next/static/immutable/chunks/1bj4l-d6k82v1.js",
      "storage_unit": "PiB",
      "tier_order": [
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       "hyperscaler",
       "silver"
      ],
      "units": "$/GiB/mo",
      "url": "https://www.clustermax.ai/tco"
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     ],
     "disposition": "open",
     "id": "tco-large-llm-pretrain-gb300-nvl72-cold-storage",
     "kind": "claim",
     "revision": 1,
     "summary": "SemiAnalysis TCO calculator assumes Storage line \"Cold (S3/Object)\" at 0.01/0.02/0.015 ($/GiB/mo) gold/hyperscaler/silver (scenario \"Large LLM Pretrain (GB300 NVL72)\", quantity 10, calc qty_x_price_GiB).",
     "tier": "public_observation",
     "title": "TCO assumption [Large LLM Pretrain (GB300 NVL72)]: Storage / Cold (S3/Object)"
    },
    "prev": "0c926c2fce79fd26392c8f48cf604426622503dc657fdbffdbb46a8664aa905f",
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    "payload": {
     "data": {
      "assertion_type": "calculator_assumption",
      "calc": "qty_x_price",
      "description": "Minimal egress for model distribution",
      "engineering_salary_raw": "2e5",
      "label": "Egress",
      "line_item_id": "egress",
      "network_unit": "GiB",
      "note": "A calculator default in a public web app, not a market price observation. Non-numeric price values (for example \"included\") are kept as strings.",
      "prices": {
       "gold": 0,
       "hyperscaler": 0.09,
       "silver": 0
      },
      "quantity": 100,
      "retrieved_date": "2026-09-29",
      "scenario": "Large LLM Pretrain (GB300 NVL72)",
      "section": "Network",
      "sha256": "e54ecff3cdd5856a012718a0a351488a94a2e97536906bd02fd87a6569e4b5d1",
      "source_chunk_url": "https://www.clustermax.ai/_next/static/immutable/chunks/1bj4l-d6k82v1.js",
      "storage_unit": null,
      "tier_order": [
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       "hyperscaler",
       "silver"
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      "units": "$/GiB/mo",
      "url": "https://www.clustermax.ai/tco"
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     "deps": [
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     ],
     "disposition": "open",
     "id": "tco-large-llm-pretrain-gb300-nvl72-egress",
     "kind": "claim",
     "revision": 1,
     "summary": "SemiAnalysis TCO calculator assumes Network line \"Egress\" at 0/0.09/0 ($/GiB/mo) gold/hyperscaler/silver (scenario \"Large LLM Pretrain (GB300 NVL72)\", quantity 100, calc qty_x_price).",
     "tier": "public_observation",
     "title": "TCO assumption [Large LLM Pretrain (GB300 NVL72)]: Network / Egress"
    },
    "prev": "f810c7f31b27c7005cbd524db2a0bad4610a20a5dd12f708ad1ef8809bfb6b74",
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    "payload": {
     "data": {
      "assertion_type": "calculator_assumption",
      "calc": "qty_x_price",
      "description": "Per-GiB NAT data processing (AWS)",
      "engineering_salary_raw": "2e5",
      "label": "NAT processing",
      "line_item_id": "nat_processing",
      "network_unit": "GiB",
      "note": "A calculator default in a public web app, not a market price observation. Non-numeric price values (for example \"included\") are kept as strings.",
      "prices": {
       "gold": 0,
       "hyperscaler": 0.045,
       "silver": 0
      },
      "quantity": 100,
      "retrieved_date": "2026-09-29",
      "scenario": "Large LLM Pretrain (GB300 NVL72)",
      "section": "Network",
      "sha256": "e54ecff3cdd5856a012718a0a351488a94a2e97536906bd02fd87a6569e4b5d1",
      "source_chunk_url": "https://www.clustermax.ai/_next/static/immutable/chunks/1bj4l-d6k82v1.js",
      "storage_unit": null,
      "tier_order": [
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       "hyperscaler",
       "silver"
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      "units": "$/GiB",
      "url": "https://www.clustermax.ai/tco"
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     "deps": [
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     ],
     "disposition": "open",
     "id": "tco-large-llm-pretrain-gb300-nvl72-nat-processing",
     "kind": "claim",
     "revision": 1,
     "summary": "SemiAnalysis TCO calculator assumes Network line \"NAT processing\" at 0/0.045/0 ($/GiB) gold/hyperscaler/silver (scenario \"Large LLM Pretrain (GB300 NVL72)\", quantity 100, calc qty_x_price).",
     "tier": "public_observation",
     "title": "TCO assumption [Large LLM Pretrain (GB300 NVL72)]: Network / NAT processing"
    },
    "prev": "5793956e01064ceb6273a1489a61c65cd02e3e119fa70e972abc7486492262ab",
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    "payload": {
     "data": {
      "assertion_type": "calculator_assumption",
      "calc": "qty_x_price",
      "description": "Inter-region data transfer",
      "engineering_salary_raw": "2e5",
      "label": "Data transfer",
      "line_item_id": "data_transfer",
      "network_unit": "GiB",
      "note": "A calculator default in a public web app, not a market price observation. Non-numeric price values (for example \"included\") are kept as strings.",
      "prices": {
       "gold": 0,
       "hyperscaler": 0.01,
       "silver": 0
      },
      "quantity": 500,
      "retrieved_date": "2026-09-29",
      "scenario": "Large LLM Pretrain (GB300 NVL72)",
      "section": "Network",
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      "source_chunk_url": "https://www.clustermax.ai/_next/static/immutable/chunks/1bj4l-d6k82v1.js",
      "storage_unit": null,
      "tier_order": [
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       "hyperscaler",
       "silver"
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      "units": "$/GiB/mo",
      "url": "https://www.clustermax.ai/tco"
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     "deps": [
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     ],
     "disposition": "open",
     "id": "tco-large-llm-pretrain-gb300-nvl72-data-transfer",
     "kind": "claim",
     "revision": 1,
     "summary": "SemiAnalysis TCO calculator assumes Network line \"Data transfer\" at 0/0.01/0 ($/GiB/mo) gold/hyperscaler/silver (scenario \"Large LLM Pretrain (GB300 NVL72)\", quantity 500, calc qty_x_price).",
     "tier": "public_observation",
     "title": "TCO assumption [Large LLM Pretrain (GB300 NVL72)]: Network / Data transfer"
    },
    "prev": "abdb2f9bb72a0c3fd4b517d2f96f1e260b715cb0e5d19eacf9008877395be09c",
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   {
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    "payload": {
     "data": {
      "assertion_type": "calculator_assumption",
      "calc": "qty_x_price_hr",
      "description": "Login nodes + slurmctld; 3 machines for control plane services",
      "engineering_salary_raw": "2e5",
      "label": "Ctrl plane",
      "line_item_id": "ctrl_plane",
      "network_unit": null,
      "note": "A calculator default in a public web app, not a market price observation. Non-numeric price values (for example \"included\") are kept as strings.",
      "prices": {
       "gold": "included",
       "hyperscaler": 1.536,
       "silver": "included"
      },
      "quantity": 3,
      "retrieved_date": "2026-09-29",
      "scenario": "Large LLM Pretrain (GB300 NVL72)",
      "section": "CPU",
      "sha256": "e54ecff3cdd5856a012718a0a351488a94a2e97536906bd02fd87a6569e4b5d1",
      "source_chunk_url": "https://www.clustermax.ai/_next/static/immutable/chunks/1bj4l-d6k82v1.js",
      "storage_unit": null,
      "tier_order": [
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       "hyperscaler",
       "silver"
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      "units": "$/vm/hr",
      "url": "https://www.clustermax.ai/tco"
     },
     "deps": [
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       "revision": 1
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     ],
     "disposition": "open",
     "id": "tco-large-llm-pretrain-gb300-nvl72-ctrl-plane",
     "kind": "claim",
     "revision": 1,
     "summary": "SemiAnalysis TCO calculator assumes CPU line \"Ctrl plane\" at included/1.536/included ($/vm/hr) gold/hyperscaler/silver (scenario \"Large LLM Pretrain (GB300 NVL72)\", quantity 3, calc qty_x_price_hr).",
     "tier": "public_observation",
     "title": "TCO assumption [Large LLM Pretrain (GB300 NVL72)]: CPU / Ctrl plane"
    },
    "prev": "5115ee880f81acf421d452dde1eda290276162f6416b3434c0944fb84e99ebcd",
    "seq": 1810,
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   {
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    "hash": "8110275187c954d03dda07fb20e13bf1606f8b9b833289a2ab558962ba1ebaff",
    "payload": {
     "data": {
      "assertion_type": "calculator_assumption",
      "calc": "pct_of_pre_support",
      "description": "AWS Unified Operations (max tier); Nebius/Silver include support in price",
      "engineering_salary_raw": "2e5",
      "label": "Support",
      "line_item_id": "support",
      "network_unit": null,
      "note": "A calculator default in a public web app, not a market price observation. Non-numeric price values (for example \"included\") are kept as strings.",
      "prices": {
       "gold": "included",
       "hyperscaler": 0.03,
       "silver": "included"
      },
      "quantity": 0,
      "retrieved_date": "2026-09-29",
      "scenario": "Large LLM Pretrain (GB300 NVL72)",
      "section": "Support",
      "sha256": "e54ecff3cdd5856a012718a0a351488a94a2e97536906bd02fd87a6569e4b5d1",
      "source_chunk_url": "https://www.clustermax.ai/_next/static/immutable/chunks/1bj4l-d6k82v1.js",
      "storage_unit": null,
      "tier_order": [
       "gold",
       "hyperscaler",
       "silver"
      ],
      "units": "% uplift",
      "url": "https://www.clustermax.ai/tco"
     },
     "deps": [
      {
       "id": "tco-src-clustermax-tco-chunk",
       "revision": 1
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     ],
     "disposition": "open",
     "id": "tco-large-llm-pretrain-gb300-nvl72-support",
     "kind": "claim",
     "revision": 1,
     "summary": "SemiAnalysis TCO calculator assumes Support line \"Support\" at included/0.03/included (% uplift) gold/hyperscaler/silver (scenario \"Large LLM Pretrain (GB300 NVL72)\", quantity 0, calc pct_of_pre_support).",
     "tier": "public_observation",
     "title": "TCO assumption [Large LLM Pretrain (GB300 NVL72)]: Support / Support"
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      "line_item_id": "goodput",
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      "note": "A calculator default in a public web app, not a market price observation. Non-numeric price values (for example \"included\") are kept as strings.",
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      "network_unit": null,
      "note": "A calculator default in a public web app, not a market price observation. Non-numeric price values (for example \"included\") are kept as strings.",
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     "tier": "public_observation",
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      "network_unit": null,
      "note": "A calculator default in a public web app, not a market price observation. Non-numeric price values (for example \"included\") are kept as strings.",
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     "disposition": "open",
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     "tier": "public_observation",
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      "note": "A calculator default in a public web app, not a market price observation. Non-numeric price values (for example \"included\") are kept as strings.",
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     "tier": "public_observation",
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      "network_unit": null,
      "note": "A calculator default in a public web app, not a market price observation. Non-numeric price values (for example \"included\") are kept as strings.",
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     "tier": "public_observation",
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      "description": "Real pricing: Nebius/Silver at 25th pctile of GPU Pricing Data, AWS at 50th pctile (~75% off list)",
      "engineering_salary_raw": "2e5",
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      "line_item_id": "b200",
      "network_unit": null,
      "note": "A calculator default in a public web app, not a market price observation. Non-numeric price values (for example \"included\") are kept as strings.",
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     "tier": "public_observation",
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      "line_item_id": "orchestration",
      "network_unit": null,
      "note": "A calculator default in a public web app, not a market price observation. Non-numeric price values (for example \"included\") are kept as strings.",
      "prices": {
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       "silver": "included"
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     ],
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     "summary": "SemiAnalysis TCO calculator assumes GPU line \"Orchestration\" at included/0.1/included (% uplift) gold/hyperscaler/silver (scenario \"Multimodal RL Research (B200)\", quantity 0, calc pct_of_gpu).",
     "tier": "public_observation",
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      "network_unit": null,
      "note": "A calculator default in a public web app, not a market price observation. Non-numeric price values (for example \"included\") are kept as strings.",
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      "storage_unit": "PiB",
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      "url": "https://www.clustermax.ai/tco"
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     ],
     "disposition": "open",
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     "summary": "SemiAnalysis TCO calculator assumes Storage line \"Hot (Weka/VAST)\" at 0.035/0.0725/0.06 ($/GiB/mo) gold/hyperscaler/silver (scenario \"Multimodal RL Research (B200)\", quantity 25, calc qty_x_price_GiB).",
     "tier": "public_observation",
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    "payload": {
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      "line_item_id": "egress",
      "network_unit": "GiB",
      "note": "A calculator default in a public web app, not a market price observation. Non-numeric price values (for example \"included\") are kept as strings.",
      "prices": {
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     ],
     "disposition": "open",
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     "summary": "SemiAnalysis TCO calculator assumes Network line \"Egress\" at 0/0.09/0 ($/GiB/mo) gold/hyperscaler/silver (scenario \"Multimodal RL Research (B200)\", quantity 100, calc qty_x_price).",
     "tier": "public_observation",
     "title": "TCO assumption [Multimodal RL Research (B200)]: Network / Egress"
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      "engineering_salary_raw": "2e5",
      "label": "Ctrl plane",
      "line_item_id": "ctrl_plane",
      "network_unit": null,
      "note": "A calculator default in a public web app, not a market price observation. Non-numeric price values (for example \"included\") are kept as strings.",
      "prices": {
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       "silver": "included"
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      "url": "https://www.clustermax.ai/tco"
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     ],
     "disposition": "open",
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     "summary": "SemiAnalysis TCO calculator assumes CPU line \"Ctrl plane\" at included/1.536/included ($/vm/hr) gold/hyperscaler/silver (scenario \"Multimodal RL Research (B200)\", quantity 3, calc qty_x_price_hr).",
     "tier": "public_observation",
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    "payload": {
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     ],
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     "tier": "public_observation",
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     ],
     "disposition": "open",
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     ],
     "disposition": "open",
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     "revision": 1,
     "summary": "SemiAnalysis TCO calculator goodput preset \"Maximum (Nebius blog)\" assumes an MTBF of 169800 GPU-hours, citing Nebius blog (https://nebius.com/blog/posts/how-we-build-reliable-clusters).",
     "tier": "public_observation",
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     ],
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     "revision": 1,
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     ],
     "disposition": "open",
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     "revision": 1,
     "summary": "SemiAnalysis TCO calculator goodput preset \"Meta paper claim\" assumes an MTBF of 50677 GPU-hours, citing Meta paper (https://arxiv.org/pdf/2410.21680v1).",
     "tier": "public_observation",
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     ],
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     "summary": "SemiAnalysis TCO calculator goodput preset \"Round number (mid)\" assumes an MTBF of 32000 GPU-hours.",
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     ],
     "disposition": "open",
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     "summary": "SemiAnalysis TCO calculator goodput preset \"Nebius blog sample\" assumes an MTBF of 26446 GPU-hours, citing Nebius blog (https://nebius.com/blog/posts/how-we-build-reliable-clusters).",
     "tier": "public_observation",
     "title": "TCO goodput preset: Nebius blog sample = 26446 GPU-hrs MTBF"
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    "payload": {
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     ],
     "disposition": "open",
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     "revision": 1,
     "summary": "SemiAnalysis TCO calculator goodput preset \"Round number (low)\" assumes an MTBF of 16000 GPU-hours.",
     "tier": "public_observation",
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    "payload": {
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      "note": "Preset value in a public web app; the cited source has not been fetched or checked for this record.",
      "preset_index": 7,
      "preset_label": "ClusterMAX 2.0 real",
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      "source_chunk_url": "https://www.clustermax.ai/_next/static/immutable/chunks/1bj4l-d6k82v1.js",
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     },
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     ],
     "disposition": "open",
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     "kind": "claim",
     "revision": 1,
     "summary": "SemiAnalysis TCO calculator goodput preset \"ClusterMAX 2.0 real\" assumes an MTBF of 10000 GPU-hours, citing ClusterMAX (https://www.clustermax.ai/).",
     "tier": "public_observation",
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    },
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    "payload": {
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     "deps": [],
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     "summary": "GitHub release \"latest-data\" of thatkavish/OpenComputePrices (MIT). _master.csv is a member of data.tar.gz; it was streamed and hashed but not saved locally (10.6 GB).",
     "tier": "public_observation",
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     ],
     "disposition": "open",
     "id": "ocp-jul2026-h100",
     "kind": "claim",
     "revision": 1,
     "summary": "In the OpenComputePrices July 2026 data, the median across providers of per-provider median on-demand H100 prices is $2.99 per GPU-hr across 42 providers (row-median $8.99). Derived by the lane script from the release data.",
     "tier": "public_observation",
     "title": "OpenComputePrices July 2026 on-demand H100: provider-median $2.99/GPU-hr (42 providers)"
    },
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    "payload": {
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      "caveat": "derived figure; reproducing it requires the 10.6 GB _master.csv which is not stored with this packet",
      "coverage_md_sha256": "f8853594cacecd24b703bcb62fa45c4309432ded8fb4f7ae5fbd308410601099",
      "derivation": "lane processing of _master.csv and archives, tabulated in summary/coverage.md \"Median on-demand $/GPU-hr by month\" and REPORT.md finding 2",
      "derived": true,
      "derived_from_sha256": "2fba6d003e8fb5824f962162411241d7cdb6fab0311b1680149e7968ea175774",
      "month": "2026-07",
      "preferred_statistic": "provider_median (lane report: row medians are dominated by providers listing many instance shapes)",
      "price_type": "on_demand",
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      "report_md_sha256": "c88b2afbbb5c3bc1a6e77fba12012ac93f2c62ee173c5069f55db94436786ff8",
      "row_median": 8.39,
      "sku": "H200",
      "statistic": "provider_median = median of per-provider medians; row_median = median over all price rows; zero/null prices excluded",
      "unit": "USD per GPU-hour"
     },
     "deps": [
      {
       "id": "ocp-src-latest-data-2026-07-29",
       "revision": 1
      }
     ],
     "disposition": "open",
     "id": "ocp-jul2026-h200",
     "kind": "claim",
     "revision": 1,
     "summary": "In the OpenComputePrices July 2026 data, the median across providers of per-provider median on-demand H200 prices is $4.29 per GPU-hr across 33 providers (row-median $8.39). Derived by the lane script from the release data.",
     "tier": "public_observation",
     "title": "OpenComputePrices July 2026 on-demand H200: provider-median $4.29/GPU-hr (33 providers)"
    },
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   {
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    "hash": "fa2bd11926fbd4b9e9f75c42bf9a0e361c95c3c12646d7197c1584c1bb99a8c9",
    "payload": {
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      "assertion_type": "derived_median",
      "caveat": "derived figure; reproducing it requires the 10.6 GB _master.csv which is not stored with this packet",
      "coverage_md_sha256": "f8853594cacecd24b703bcb62fa45c4309432ded8fb4f7ae5fbd308410601099",
      "derivation": "lane processing of _master.csv and archives, tabulated in summary/coverage.md \"Median on-demand $/GPU-hr by month\" and REPORT.md finding 2",
      "derived": true,
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      "month": "2026-07",
      "preferred_statistic": "provider_median (lane report: row medians are dominated by providers listing many instance shapes)",
      "price_type": "on_demand",
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      "row_median": 8.05,
      "sku": "B200",
      "statistic": "provider_median = median of per-provider medians; row_median = median over all price rows; zero/null prices excluded",
      "unit": "USD per GPU-hour"
     },
     "deps": [
      {
       "id": "ocp-src-latest-data-2026-07-29",
       "revision": 1
      }
     ],
     "disposition": "open",
     "id": "ocp-jul2026-b200",
     "kind": "claim",
     "revision": 1,
     "summary": "In the OpenComputePrices July 2026 data, the median across providers of per-provider median on-demand B200 prices is $6.49 per GPU-hr across 23 providers (row-median $8.05). Derived by the lane script from the release data.",
     "tier": "public_observation",
     "title": "OpenComputePrices July 2026 on-demand B200: provider-median $6.49/GPU-hr (23 providers)"
    },
    "prev": "34587e1f44e22931ced04406a35302e0238a33176f203cdbba32e9c42deef007",
    "seq": 1845,
    "type": "record"
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   {
    "actor": "Public-tail packet builder",
    "at": "2026-09-29T22:00:00Z",
    "hash": "64d2c4e094014d2e18b299b2e956f75af0d050d1a3e004f96f6de241c808b056",
    "payload": {
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      "caveat": "derived figure; reproducing it requires the 10.6 GB _master.csv which is not stored with this packet",
      "coverage_md_sha256": "f8853594cacecd24b703bcb62fa45c4309432ded8fb4f7ae5fbd308410601099",
      "derivation": "lane processing of _master.csv and archives, tabulated in summary/coverage.md \"Median on-demand $/GPU-hr by month\" and REPORT.md finding 2",
      "derived": true,
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      "month": "2026-07",
      "preferred_statistic": "provider_median (lane report: row medians are dominated by providers listing many instance shapes)",
      "price_type": "on_demand",
      "provider_count": 7,
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      "report_md_sha256": "c88b2afbbb5c3bc1a6e77fba12012ac93f2c62ee173c5069f55db94436786ff8",
      "row_median": 2.19,
      "sku": "MI300X",
      "statistic": "provider_median = median of per-provider medians; row_median = median over all price rows; zero/null prices excluded",
      "unit": "USD per GPU-hour"
     },
     "deps": [
      {
       "id": "ocp-src-latest-data-2026-07-29",
       "revision": 1
      }
     ],
     "disposition": "open",
     "id": "ocp-jul2026-mi300x",
     "kind": "claim",
     "revision": 1,
     "summary": "In the OpenComputePrices July 2026 data, the median across providers of per-provider median on-demand MI300X prices is $3.04 per GPU-hr across 7 providers (row-median $2.19). Derived by the lane script from the release data.",
     "tier": "public_observation",
     "title": "OpenComputePrices July 2026 on-demand MI300X: provider-median $3.04/GPU-hr (7 providers)"
    },
    "prev": "fa2bd11926fbd4b9e9f75c42bf9a0e361c95c3c12646d7197c1584c1bb99a8c9",
    "seq": 1846,
    "type": "record"
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   {
    "actor": "Public-tail packet builder",
    "at": "2026-09-29T22:00:00Z",
    "hash": "c8635ff6213a2331e02cc2bfcccf371a37c139068017beb8b9c0172901609941",
    "payload": {
     "data": {
      "assertion_type": "derived_median",
      "caveat": "single provider; not a market median",
      "coverage_md_sha256": "f8853594cacecd24b703bcb62fa45c4309432ded8fb4f7ae5fbd308410601099",
      "derivation": "lane processing of _master.csv and archives, tabulated in summary/coverage.md \"Median on-demand $/GPU-hr by month\" and REPORT.md finding 2",
      "derived": true,
      "derived_from_sha256": "2fba6d003e8fb5824f962162411241d7cdb6fab0311b1680149e7968ea175774",
      "month": "2026-07",
      "preferred_statistic": "provider_median (lane report: row medians are dominated by providers listing many instance shapes)",
      "price_type": "on_demand",
      "provider_count": 1,
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      "report_md_sha256": "c88b2afbbb5c3bc1a6e77fba12012ac93f2c62ee173c5069f55db94436786ff8",
      "row_median": 8.6,
      "sku": "MI355X",
      "statistic": "provider_median = median of per-provider medians; row_median = median over all price rows; zero/null prices excluded",
      "unit": "USD per GPU-hour"
     },
     "deps": [
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       "id": "ocp-src-latest-data-2026-07-29",
       "revision": 1
      }
     ],
     "disposition": "open",
     "id": "ocp-jul2026-mi355x",
     "kind": "claim",
     "revision": 1,
     "summary": "In the OpenComputePrices July 2026 data, the median across providers of per-provider median on-demand MI355X prices is $8.60 per GPU-hr across 1 provider (row-median $8.60). Derived by the lane script from the release data.",
     "tier": "public_observation",
     "title": "OpenComputePrices July 2026 on-demand MI355X: provider-median $8.60/GPU-hr (1 provider)"
    },
    "prev": "64d2c4e094014d2e18b299b2e956f75af0d050d1a3e004f96f6de241c808b056",
    "seq": 1847,
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  "label": "Public tail 2026-09-29: ClusterMAX reviews, SemiAnalysis newsletter and podcasts, TCO calculator, OpenComputePrices",
  "schema": "second-run/research-desk@1",
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 "boundary": "Checksum and journal detect changes relative to this packet. They do not authenticate a publisher or prevent a hostile party from rebuilding an entirely new chain. Retain a trusted prior checksum separately."
}