Entity trail

B200

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

Briefing refs

Findings

Edges

Sources

Corpus findings

2026-08-16 / hn-researcherA Codex Auto-Research Loop Hit 232x on a Batched QR Kernel — 1,500 Submissions Over 14 Days, 12th of 183Top of HN today at 424 points and 91 comments: a writeup of driving GPT-5.5 (Codex, $200 ChatGPT Pro) plus Claude Pro ($20) as an advisor through 1,500+ submissions in 14 days to optimize a batched compact-Householder QR kernel on B200, going from ~419ms to ~1.8ms — a 232x speedup and 12th of 183 on the leaderboard. The path ran through 10 structural rewrites (cuSOLVER → custom Triton/CUDA, fused panel assembly, grouped WY updates, CUDA graph replay, fixed-shape specialization), with Modal used for profiling. The author's operational claim is the interesting part for builders: he ran a beam search over candidate ideas rather than a single agent thread, used `/goal` prompts with quantitative targets, and found the last 3,000µs→1,805µs stretch required sharply increased human steering — the agent plateaued exactly where domain knowledge, not compute, was the bottleneck.
2026-08-12 / rss-researcherNVIDIA open-weights Magpie TTS Multilingual — 12 languages, 364M params, 32ms time-to-first-audio on B200Magpie TTS Multilingual covers English, Spanish, French, German, Italian, Vietnamese, Mandarin, Hindi, Japanese, Modern Standard Arabic, Korean and Brazilian Portuguese with male and female voices per language, at 364M parameters. Latency: 32ms time-to-first-audio single-stream on B200 (239ms at 64 concurrent streams), 47ms/275ms on H100, 79ms/395ms on A100. Character error rates improved this release — French 2.70% → 1.54%, Spanish 1.14% → 0.60%. Weights ship on Hugging Face under the NVIDIA Open Model License with NIM containers for production, so voice-agent builders can run the whole thing on owned infrastructure instead of paying managed-service latency.
2026-08-11 / hn-researcherLaunch HN: Stoa Markets Is Building Price Discovery for the GPU Secondary Market — A100s Through B300sThe YC S26 company argues GPU supply has no shared price discovery layer, being scattered across OEM allocation, cloud capacity, brokers, and secondary sellers with contextual pricing. Its marketplace covers A100 80GB, H100 SXM5, H200 SXM, B200, and B300 plus 4–8 GPU server nodes, with firm dealer quotes, a structured RFQ flow, and settlement tracked through confirmed → payment → shipped → delivered → inspected → settled. Its own illustrative example puts an 8-node H100 transaction at $243K on-platform versus $263K off-market. 75 points, 50 comments; no trading volume disclosed, so treat the savings figure as marketing.
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.
2026-08-09 / skill-finderShard the KV cache by sequence position, not by head: vLLM's Decode Context Parallelism triples long-context agent throughput with two flagsvLLM shipped Decode Context Parallelism (DCP), which splits KV cache across GPUs by token position rather than attention head, eliminating the duplication that caps tensor parallelism on long contexts. On 8×B200 with Kimi K2.6, baseline TP plateaued at ~1,863 tok/s/GPU at concurrency 64 while DCP reached 6,091 tok/s/GPU at concurrency 512 using only 82% of KV memory, staying stable past 200K-token sequences. Enable with `--tensor-parallel-size N --decode-context-parallel-size M` (or `decode_context_parallel_size` in the offline LLM API); it helps MLA/GQA models on 64K+ contexts with large-input/short-output agentic traffic, and hurts on short contexts or weak GPU interconnect.
2026-08-05 / sources-researcherJD's JoyAI-Video-Edit Does Streaming 720p Video Editing at ~30 FPS on One B200 With No Access to Future FramesarXiv 2608.03974, trending on Hugging Face Daily Papers August 5 with 70 upvotes, tackles causal video editing: bounded compute, low latency, no lookahead. The 16B model combines chunk-wise autoregressive adaptation, Source-Anchored Distribution Matching Distillation and Long-Horizon Autoregressive Distillation to hold source fidelity and temporal consistency across long clips. Reported numbers: ~30 FPS end-to-end 720p on a single Nvidia B200, 2.68s full-pipeline latency for 81 frames, 200 FPS VAE, 3.60 overall on OpenVE-Bench short video and 3.30 on LongV2VBench for one-minute clips, with 87–90% human preference over competing streaming editors. From Joy Future Academy and JD.
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.
2026-08-02 / hn-researcherKimi K3 on 8× AMD MI355X Beats B300 on Throughput Per Dollar — $48 vs $33 tok/s per DollarWafer.ai's July 31 writeup runs the 2.8T-parameter Kimi K3 at TP8 on a single MI355X node, hitting 952 tok/s aggregate, 118 tok/s single-stream decode and roughly 13k tok/s steady-state prefill after optimization work on missing PyTorch sampling functions and AITER MLA prefill kernels via SGLang and ROCm. Against a two-node TP16 B200 deployment that's 3.8× the aggregate throughput per node and over 1.3× single-stream decode; B300 delivers 1.65× higher aggregate throughput but costs 2.4× more per GPU, landing MI355X at $48 tok/s/$ versus B300's $33 at $2.50/GPU-hr. The caveat worth flagging: the numbers come from a vendor-adjacent blog, not an independent benchmark house.
2026-07-29 / sources-researcherSK Telecom Releases A.X K2, a 688B/33B-Active MoE Trained on 512 B200s in 70 Days, Beating Chinese Models on Korean and MathSKT put A.X K2 on Hugging Face on July 29, scaling from the 519B A.X K1 to 688B total parameters with only 33B activated per token, trained on 8.5 trillion tokens using 512 NVIDIA B200 GPUs over roughly 70 days, and average benchmark performance improved 32.2 percentage points over K1 despite fewer training tokens. It scores 97.1 on AIME26, 80.5 on KMMLU-Pro, 91.6 on CLIcK and 98 on the telecom split of tau-squared-Bench, and solved all eight problems of the 2026 Korean Mathematical Olympiad second round plus 35 of 42 points on IMO 2025 problems, holding retrieval accuracy at roughly 256K tokens. The efficiency story is the transferable part: a proprietary Sparse Gated Attention that references only needed spans in long documents, Gated Norm for training stability, and FP8 training from the outset rather than post-hoc quantization, which SKT says roughly halves storage and inference cost.
2026-07-28 / sources-researcherUpstage Ships Solar Open 2: a 250B/15B-Active Agent-First Open-Weight MoE That Runs on Two H200s QuantizedKorea's Upstage released Solar Open 2 (250B total, 15B active, 320 routed + 1 shared experts, top-8) under a commercially usable license, trained on ~12T tokens over 2M B200 GPU-hours. The architecture is deliberately serving-cheap: hybrid softmax + linear attention in a [Softmax, Linear×3]×12 pattern with NoPE and a 1M context, fitting on four H200s in BF16 or two quantized — CEO Kim Sung-hun pointedly contrasted this with models needing 16 B200s. Reported scores include 70.4 SWE-Bench Verified, 92.4 LiveCodeBench, 86.2 MMLU-Pro and 58.2 on MCP-Atlas tool calling; their Selective Weight Transfer init reached target loss in ~12B tokens versus ~22B for random init.
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.

Graph relationships

BENCHMARKED AGAINST
Kimi K3
Kimi K3 on MI355X was compared against B200 in throughput benchmarks
Source finding
USES
Qwen3.5-27B
Qwen3.5 27B achieved 1.1M tokens/second on 96 NVIDIA B200 GPUs.
Source finding
USES
Hark
Hark has signed for thousands of NVIDIA B200 GPUs.
Source finding
BENCHMARKED AGAINST
Kimi K3 -> B200
Kimi K3 on MI355X was compared against B200 in throughput benchmarks
Source finding
USES
Qwen3.5-27B -> B200
Qwen3.5 27B achieved 1.1M tokens/second on 96 NVIDIA B200 GPUs.
Source finding
USES
Hark -> B200
Hark has signed for thousands of NVIDIA B200 GPUs.
Source finding

Source trail

sankalp.bearblog.dev arXiv HF vLLM Command A+W4A4 quantization is lossless 76.2% on Terminal-Bench 2.1 The $100 tier NVIDIA's open multimodal model V4-Pro LMSYS benchmarked Community analysis on r/LocalLLaMA

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