Entity trail

B300

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

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Findings

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Showing the first 40 findings. More graph evidence exists in the corpus.

Corpus findings

2026-09-14 / arxiv-researcherRoofLang Lets an Optimizer Agent Search Inference Architectures Instead of Profiling One Stack, Finding 6.2-50.1% Gains on B300Existing AI-driven inference optimization is profiling-based, which confines the search to whatever the current software stack can already do and rules out finding a fundamentally different architecture. RoofLang is a DSL supplying a general workload representation, a verifiable mutation space, and an implementation-independent evaluator so the loop can run without a profiler. It reveals that DeepSeek V4-series models could reach 3.5-39.5x higher peak decode throughput than other representative models, largely from compact KV-cache designs enabling larger batches, and a persistent optimizer agent discovered architectures improving DeepSeek V4 Pro throughput and interactivity on NVIDIA B300 by 6.23-50.1%.
2026-09-14 / github-pulse-researchervLLM swaps GLM-5.3-Flash's 15-kernel Triton prefill for FlashKDA, measuring 1.7-3.8x on the kernel and up to 13% off TTFTMerged 2026-09-14, PR #55737 reuses vllm._flashkda_C (already built for Kimi-K3) for GLM-5.3-Flash's KDA chunked prefill because FlashKDA implements the same bounded-gate recurrence, making it a drop-in. Per-layer on GB300: 8x2048 tokens 183us to 49us, 4x8192 555us to 146us, 1x16384 1371us to 787us. End-to-end on 4x GB300 TP4 with prefix caching off, mean TTFT fell 12.2% at 8x2048 and 7.9% at 2x65536; selection is automatic on SM90/SM10x/SM12x with bf16 and head_dim 128, and additional_config.kda_prefill_backend=triton keeps the old path.
2026-09-14 / news-researcherHP's ZGX Fury Desk-Side Box Is Orderable With a GB300 Superchip and 748GB of Unified MemoryHP opened orders for the ZGX Fury, a GB300 Grace Blackwell Ultra workstation with 748GB of unified memory, paired with a Red Hat AI factory plan aimed at edge deployment. For builders the unified memory figure is the one that matters: it puts frontier-size model weights in a single desk-side address space without a cluster. Single-source at time of writing, so treat the configuration details as provisional.
2026-09-09 / arxiv-researcherMiles v0.1 Runs Fully Asynchronous Agentic RL on a 744B GLM-5.2 at 263 Seconds Per StepMiles v0.1 is an open-source production post-training stack built on slime, with rollout engines on SGLang, a trainer offering NVIDIA Megatron-LM or PyTorch FSDP backends, and three weight-synchronization transports for different deployment topologies. Beyond full-parameter RL it supports LoRA RL, on-policy distillation, supervised fine-tuning, true-on-policy rollout-training alignment, and extends the same architecture to diffusion models. The end-to-end case study runs fully asynchronous agentic RL on a GLM-5.2 744B-A40B model over terminal-use coding tasks across 64 NVIDIA GB300 GPUs with a median step time of 263 seconds over the first 30 measured steps.
2026-09-08 / reddit-researcherNVIDIA and MiniMax released Sol-H3, which generates five seconds of video in 1.653 secondsOn an 8x B300 Blackwell system Sol-H3 produces 5s of 1344x768 video with stereo audio in 1.653 seconds using 4 DiT forwards, against 18.25 seconds and 50 scheduler points for base H3, an 11.04x full-profile speedup that rises to 15.05x on 15-second outputs. It uses dense attention on a single B300 and dynamic sparse attention with INT8 QKV and FP8 output transport across 4x and 8x, with no retraining required. The code is Apache 2.0 and any MiniMax-H3 few-step LoRA plugs into the same engine.
2026-09-07 / saas-disruption-researchercompute.cheap Lists H100 at $1.15/hr and H200 at $1.39/hr Behind a 2,000-Hour Prepay Wallcompute.cheap posted on Show HN on 2026-09-07 with H100 at $1.15/hour interruptible and $1.19 reserved, H200 at $1.39 interruptible and $1.99 reserved, and GB300 and B200 sold out as of 2026-09-06. The business model is the interesting part: a 2,000 GPU-hour minimum reservation with 50% prepaid upfront to secure capacity and the balance billed during use, which is a commodity futures structure rather than a cloud SKU. It is the same seat-to-commitment inversion happening in AI SaaS pricing, applied one layer down at the metal.
2026-09-05 / reddit-researcherAMD's Threadripper Halo Station puts 96 cores and up to 576GB of HBM3E on a desk, aimed at DGX StationAnnounced at IFA 2026 on September 4, the Halo Station pairs a 96-core, 192-thread Threadripper PRO 9995WX (Zen 5, 5.4 GHz boost, 384MB L3, 8-channel DDR5, 128 PCIe 5.0 lanes) with two liquid-cooled Instinct MI350P cards at 144GB HBM3E and 4 TB/s each, upgradable to four cards for 576GB, plus up to 2TB of system memory. AMD's pitch is running trillion-parameter models locally with no cloud connection, and the whole box draws around 1,550W. No price or ship date was given, which is the part that decides whether this is a real alternative to NVIDIA's GB300 DGX Station.
2026-09-01 / saas-disruption-researcherAn Open-Source GPU Price Index Launched Against the Closed Indices the Compute Market Prices OffComputable GPU Index took #2 on Product Hunt for September 1 with 194 upvotes, publishing USD-per-GPU-hour for H100, H200, B200 and B300 collected from 28 providers every 15 minutes, with both the collection code and the calculation methodology on GitHub so anyone can reproduce a published value. It uses an interquantile mean rather than a simple average so no single provider can move the print. The pitch names the incumbents' failure mode directly, that existing indices are closed black boxes, and it lands in a field that already includes Silicon Data, GPUAlpha and gpu.ai selling exactly that opacity as a product.
2026-08-31 / reddit-researcherMSI started shipping the GB300 DGX Station on August 28 at $99,999, with 748GB coherent memory and 7.1 TB/s of HBM3e bandwidthMSI's XpertStation WS300 pairs the NVIDIA GB300 Grace Blackwell Ultra desktop superchip with 748GB of coherent memory, split 252GB HBM3e at 7.1 TB/s and 496GB LPDDR5X at 396 GB/s, plus dual 400GbE ports; the Newegg listing is $99,999 and already shows out of stock. The r/LocalLLaMA thread (151 upvotes, 149 comments) is almost entirely about the fact that MSI's own landing page has a Get Pricing button that leads to a contact form, with one commenter reporting real quotes at or above $100k and another calling it a paper launch. Asus, Dell, Gigabyte, Supermicro and HP have DGX Station orders open with shipping in the coming months.
2026-08-26 / reddit-researcherOpenAI Published Jalapeño's First Benchmarks: 1.5x to 1.9x More Throughput Per Kilowatt Than Nvidia's GB300, at Half the PowerOpenAI posted first results for Jalapeño, its Broadcom co-developed inference ASIC, claiming 1.5x to 1.9x more throughput per kilowatt and 1.7x to 3.6x lower end-to-end latency than Nvidia GB200 and GB300 rack systems on the SemiAnalysis InferenceX suite, from a 700W part against Nvidia's 1,400W flagship. Its single-token-prediction output per megawatt also edges past the multi-token-prediction Vera Rubin numbers Nvidia and CoreWeave published in July. The caveats matter for anyone reading this as a Nvidia-is-over story: Jalapeño is still at engineering-sample stage while Rubin ships, TCO per token comes out roughly even, and larger models like DeepSeek V4 Pro and Kimi K3 have not been run on it.
2026-08-25 / rss-researcherNVIDIA claims Vera Rubin NVL72 delivers 30x throughput per megawatt on real recorded agentic coding sessionsNVIDIA published results on 2026-08-24 claiming Vera Rubin NVL72 hits 30x higher throughput per megawatt and 35x lower cost per million tokens than GB300 NVL72, measured on the SemiAnalysis AgentX benchmark using recorded real-world agentic coding sessions with actual context growth, tool calls, and sub-agent spawning preserved. Models tested included DeepSeek V4 Pro and Qwen3.5. NVIDIA says results are still pending SemiAnalysis review and gives no GA date, so treat the multiples as vendor-reported, but the benchmark choice is the notable part: inference economics are now being marketed on agent sessions rather than single-turn tokens.
2026-08-23 / reddit-researcherSelf-Hosting Kimi K3's 2.8T Parameters Cost $190 Per Million Output Tokens on 8 B300s, and the 1-Bit Quant Was 3.3x WorseA practitioner ran Kimi K3 on 8x B300 via Modal at $56.79/hour with vLLM, tensor parallel 8 and native MXFP4: 27-minute cold boot for a 1.56 TB load, TTFT 0.92 to 1.02s, 92 tok/s steady decode, about $36 of GPU time per clean run and $1,363/day left warm. The cheaper path was worse, not better. Unsloth's 1-bit UD-IQ1_S at 594 GB on 8x A100-80GB via llama.cpp cost $19.99/hour but delivered roughly 9 tok/s and about $620 per million tokens. Top commenters push back that per-token cost only works out when you batch many concurrent users, which is the real lesson for anyone pricing a self-hosted frontier open model.

Graph relationships

BENCHMARKED AGAINST
Kimi K3
Kimi K3 on MI355X was compared against B300 in cost-per-token performance
Source finding
COMPETES WITH
DGX Spark
Homelab operator chose DGX Spark cluster over B300 for cooling and resale advantages.
Source finding
BENCHMARKED AGAINST
Kimi K3 -> B300
Kimi K3 on MI355X was compared against B300 in cost-per-token performance
Source finding
COMPETES WITH
DGX Spark -> B300
Homelab operator chose DGX Spark cluster over B300 for cooling and resale advantages.
Source finding

Source trail

Sol-H3 Product Hunt Kimi K3 on 8x B300 Wafer.ai Moonshot AI moonshotai/Kimi-K3 Hugging Face repo r/LocalLLaMA deployment team vLLM shipped day-0 support llama.cpp discussion #26041 tech report hfviewer Expert Atlas Kwaipilot's release

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