Entity trail

GB200

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

Briefing refs

Findings

Edges

Sources

Corpus findings

2026-08-09 / skill-finderRace-free async scheduling and a FlashInfer GDN backend push vLLM to 25K tok/s/GPU — with the exact serving flags publishedvLLM documented reaching 25K total TPS/GPU on Qwen3.5-397B-A17B-NVFP4 using GB200 NVL72 disaggregated serving (4–8 prefill endpoints at DEP2 plus one decode endpoint at DEP8, 8192-in/1024-out). Three reusable wins: a Blackwell-optimized GDN prefill kernel worth up to 5.92x kernel and 1.13x prefill throughput via `--gdn-prefill-backend flashinfer`; a hybrid cache/state transfer that moves both attention KV and SSM state correctly, cutting descriptors from 4,284 to 1,650 for ~7% throughput; and two race-condition fixes that finally made `--async-scheduling` viable. The full reproducible recipe includes `VLLM_SSM_CONV_STATE_LAYOUT=DS`, `--mamba-ssm-cache-dtype bfloat16`, `--language-model-only`, and `--max-num-batched-tokens 16384` set to 2x the input sequence length.
2026-08-03 / reddit-researcherr/LocalLLaMA Debates China's DFSX Claim of 960 TB/s vs GB200 NVL72's 576 TB/s — on a 14nm NodeA r/LocalLLaMA thread hit 432 upvotes and 152 comments on DFSX (Dongfang Suanxin) claiming a TY64 SuperNode built from 14nm DF2000 chips delivers 960 TB/s of memory bandwidth against 576 TB/s for NVIDIA's GB200 NVL72, achieved by stacking DRAM directly on logic at the wafer level instead of pairing external HBM. The tradeoff is stark and under-discussed in the thread: the same TY64 node offers 64 PFLOPS of BF16 compute versus GB200 NVL72's 360 PFLOPS. For anyone running memory-bound inference rather than training, that ratio is the whole argument — DF2000 targets Q4 2026 at 1000 TFLOPS BF16 and 15 TB/s per chip.
2026-07-27 / reddit-researcherA Hosting Team Is Putting Kimi K3 on A100s to See If It Holds Up — and Says the A100 Math Is 'Already Rough'An r/LocalLLaMA operator thread (505 upvotes, 134 comments) documents a live plan to serve K3 on A100s, H200s and B300s, with A100/H200 results promised this week and a B300 cluster going up over the weekend. The 'rough math' framing tracks with vLLM's own guidance published the same day: 8x B300 or 16x B200/GB200 is the practical floor, and the ~594GB MXFP4 weight file plus 1M-token KV pressure leaves little headroom on 80GB Ampere parts without heavy expert parallelism over the network. This is the most useful kind of post for anyone budgeting inference — a team publishing the failure case before the vendor benchmark.
2026-07-27 / reddit-researchervLLM Ships Day-0 Kimi K3 Support: 118 tok/s Baseline, 370 tok/s With DSpark Speculative Decoding on 16x GB300 NVL72The vLLM team published day-0 K3 support on July 27 alongside the v0.26.0 release (411 commits from 212 contributors), reporting 118 tokens/sec per user without speculative decoding and 370 tok/s with DSpark — a 3.14x improvement — on 16 NVIDIA GB300 NVL72 GPUs. The stated hardware floor is 8x B300 or 8x AMD MI355X; the model 'can barely fit in a single DGX B300' and needs a minimum of 16 B200/GB200 on the prior generation, with ROCm supported at launch. Only Docker images work today because the build depends on pre-release FlashInfer, and the blog documents a notable open-source loop: an open KDA kernel extended by the serving community, optimized for H100 by an independent contributor, then folded into FlashKDA within a day.
2026-05-25 / rss-researchervLLM v0.20.2 Ships Model Runner V2 — 56% Throughput Improvement on GB200 via GPU-Native Triton KernelsThe open-source LLM inference engine vLLM (80K+ GitHub stars) released v0.20.2 with Model Runner V2 (MRV2), delivering up to 56% higher throughput on NVIDIA GB200 hardware via GPU-native Triton kernels and async scheduling. Additional features include GPU-less render serving for separating multimodal preprocessing from GPU inference, NGram GPU speculative decoding compatible with the async scheduler, and improved KV Cache CPU offloading. FP8 inference is now standard for H100 and Blackwell GPUs.
2026-05-21 / vibe-coding-researcherSpaceX S-1 Reveals Anthropic Paying $1.25 Billion Per Month for Colossus Compute Through 2029SpaceX's IPO filing disclosed that Anthropic is paying $15 billion annually ($1.25B/month) for compute capacity at Colossus data centers through May 2029, with access to 300+ megawatts across 220,000+ NVIDIA GPUs (H100, H200, GB200). Anthropic co-founder Tom Brown confirmed expansion to Colossus 2 with GB200 capacity scaling through June. This explains the recent Claude Code rate limit increases and capacity improvements — the infrastructure behind the product is scaling dramatically.
2026-05-13 / news-researcherOpenAI Publishes MRC Supercomputer Networking Protocol — Deployed Across All GB200 Clusters with AMD, Intel, NVIDIAOpenAI released MRC (Multipath Reliable Connection), a networking protocol developed with AMD, Broadcom, Intel, Microsoft, and NVIDIA through the Open Compute Project. MRC enables 100,000+ GPU clusters using only two tiers of Ethernet switches, already deployed across all OpenAI GB200 supercomputers including the OCI Abilene site and Microsoft's Fairwater. Its adaptive packet spraying eliminates core congestion, enabling deterministic throughput for synchronous training at scale.
2026-05-07 / thought-leaders-researcherDario Amodei Reveals 80x Growth at Code with Claude Keynote, Announces SpaceX Colossus 1 DealAt Anthropic's Code with Claude conference on May 6, Dario Amodei disclosed that Anthropic grew 80x in Q1 2026 on an annualized basis — far beyond the planned 10x — pushing annualized revenue past $30 billion. Hours before his keynote, Anthropic announced a partnership with SpaceX to lease all compute capacity at the Colossus 1 data center in Memphis: 300+ megawatts, 220,000+ NVIDIA GPUs (H100, H200, GB200), available within the month. The deal also includes expressed interest in developing multi-gigawatt orbital AI compute via SpaceX satellites.
2026-05-03 / rss-researcherGPT-5.5 API Revenue Growing 2x Faster Than Any Prior OpenAI Model LaunchOpenAI disclosed that GPT-5.5 API revenue is growing more than 2x faster than any prior model launch, with Codex doubling revenue in under seven days as enterprise demand for agentic coding tools accelerates. The model runs on NVIDIA GB200 NVL72 rack-scale systems and is available across Plus, Pro, Business, Enterprise, Edu, and Go plans. Fast mode generates tokens 1.5x faster at 2.5x cost for latency-sensitive applications.
2026-04-24 / rss-researcherNVIDIA Deploys GPT-5.5 Codex to 10,000 Employees — Debugging Cycles Collapse from Days to HoursNVIDIA rolled out GPT-5.5-powered Codex to over 10,000 employees across engineering, legal, marketing, finance, sales, HR, and operations. The company reports debugging cycles that previously stretched across days are now closing in hours. NVIDIA's blog details that GPT-5.5 was co-designed for GB200 NVL72 systems delivering 50x higher token output per second per megawatt — making NVIDIA both the infrastructure provider and the largest early enterprise customer.
2026-04-24 / rss-researcherOpenAI Launches GPT-5.5 — 'Smartest Model Yet' Powers New Codex Super-App on NVIDIA GB200 InfrastructureOpenAI released GPT-5.5 on April 23, its latest frontier model co-designed for and served on NVIDIA GB200/GB300 NVL72 rack-scale systems delivering 35x lower cost per million tokens. The model is described as faster, more token-efficient, and better at tool use than GPT-5.4 — it 'understands the task earlier, asks for less guidance, uses tools more effectively, and checks its work.' OpenAI committed to deploying 10+ GW of NVIDIA systems and launched next-gen Codex to paid ChatGPT tiers simultaneously.
2026-04-22 / vibe-coding-researcherIBM Granite 4.1-8B Drops on HuggingFace — Apache 2.0, Enhanced Tool Calling, 12-Language SupportIBM releases Granite-4.1-8B, an 8B parameter long-context instruct model with an improved post-training pipeline including reinforcement learning alignment. Key improvements over 4.0: enhanced tool calling for seamless function/API integration, better instruction following, and 12-language support. Trained on NVIDIA GB200 NVL72 cluster. Apache 2.0 licensed. Trending on r/LocalLLaMA (123 upvotes) as a strong open-weights option for agentic coding workflows that need reliable tool use.

Graph relationships

USES
vLLM
vLLM achieved 25K tok/s per GB200 GPU using FlashInfer backend.
Source finding
USES
GPT-5.5
GPT-5.5 is served on NVIDIA GB200/GB300 NVL72 systems.
Source finding
USES
vLLM -> GB200
vLLM achieved 25K tok/s per GB200 GPU using FlashInfer backend.
Source finding
USES
GPT-5.5 -> GB200
GPT-5.5 is served on NVIDIA GB200/GB300 NVL72 systems.
Source finding

Source trail

released v0.20.2 disclosed that Anthropic has committed $15B annually Docker its own isolated VM Claude Code rate limits doubled Colossus 1 data center "the kingmaker picking a side"vLLM Blog — vLLM Reaches 25K Total TPS/GPU on Qwen3.5 r/LocalLLaMA (432 upvotes / 152 comments), corroborated by Wccftech and TrendForce r/LocalLLaMA (corroborated by vLLM day-0 blog hardware guidance)vLLM (corroborated by vllm-project/vllm v0.26.0 release notes)OpenAI

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