Entity trail

Nvidia GB300

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

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Sources

Corpus findings

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.
2026-09-05 / 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.
2026-08-31 / 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.
2026-08-22 / sources-researcherNVIDIA Takes Poolside's Model Factory and 109 Employees for $6B in Licensing Plus $1B at a $12B Pre-Money, Founders Stay BehindAnnounced August 20 and covered in AINews on August 21, the structure is a reverse-acquihire: a non-exclusive license worth $6B, a $1B investment at $12B pre-money, and most of the technical staff moving to NVIDIA while the founders remain at Poolside. The founders say they lost a booked 40,000 GB300 cluster after failing to close a $2B round in time, and note fewer than 115 engineers and researchers built the model in the first place. Poolside Infrastructure Company, spun out in January 2026, keeps building its 1.2GW Texas datacenter with a stated path to 7GW.
2026-08-13 / reddit-researcherAlibaba Releases Qwen3.8-2.4T-A95B Open Weights — 2.4 Trillion Parameters, 512 Experts, With Day-0 vLLM and SGLang SupportAlibaba published open weights for Qwen3.8-2.4T-A95B, a fine-grained MoE with 2.4T total parameters, 95B active per token, 512 experts and a 92-layer hybrid full/linear attention backbone — one of the largest open-weight models ever released. vLLM shipped day-0 support verified on both NVIDIA and AMD with ready-made 4-bit checkpoints (NVFP4 at 1.32 TiB for an 8xB300 node, MXFP4 at 1.45 TiB for 8xMI355X), and NVIDIA measured over 4K tokens/sec per GPU on GB300 NVL72. The top r/LocalLLaMA post of the day at 1,507 upvotes, though the HuggingFace discussion tab is dominated by complaints that the open release is text-only and stripped of the 1M context and vision that Qwen3.8-Max has behind the API.
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.
2026-07-10 / sources-researcherGrok 4.5 Is the First Frontier Model Co-Trained With CursorxAI launched Grok 4.5 on July 8, describing it as Opus-class but faster and more token-efficient, at $2 per 1M input and $6 per 1M output. It was trained across tens of thousands of NVIDIA GB300 GPUs with RL over hundreds of thousands of tasks centered on multi-step software engineering, and — notably — trained alongside Cursor, shipping day-one in Cursor on all plans. A model lab co-developing with a coding-agent product rather than shipping to it is a new distribution pattern worth watching; Anthropic and OpenAI both ship into third-party IDEs but neither has claimed joint training.
2026-06-23 / rss-researcherSpaceX Signs Compute Deal Worth Up to $6.3B With Open-Source Lab Reflection AI — $150M/Month on Colossus 2 GB300sSpaceX agreed to grant Reflection AI access to its Colossus 2 data center near Memphis at $150M/month starting July 1, totaling ~$6.3B if it runs through 2029, with immediate access to Nvidia GB300s and a 90-day exit after the first three months. Reflection — last valued at $25B and building American open-source frontier models — joins Anthropic, Google and Cursor as Colossus compute tenants, turning Musk's training cluster into a commercial platform. The strategically notable part is an open-source lab renting frontier-scale compute at the exact moment enterprises are reassessing dependence on closed models; SPCX fell ~10% on the news.
2026-06-01 / rss-researcherNVIDIA DGX Station for Windows — Trillion-Parameter AI Supercomputer in a Deskside Form FactorNVIDIA partnered with Microsoft to bring DGX Station to Windows for the first time, featuring GB300 with 72 Neoverse V2 CPU cores, Blackwell Ultra GPU, and 252GB HBM3e memory. Designed for building and running always-on AI agents at developer desks. Systems from ASUS, Dell, HP, Gigabyte, MSI, and Supermicro expected Q4 2026. This is data-center-grade inference in a desktop box — a builder can run trillion-parameter models locally.
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.

Graph relationships

USES
Mistral
Mistral's data center will deploy 13,800 NVIDIA GB300 GPUs.
Source finding
USES
Mistral -> NVIDIA GB300
Mistral's data center will deploy 13,800 NVIDIA GB300 GPUs.
Source finding

Source trail

CNBC arXiv 2609.08368 TechPowerUp / AMD (via r/LocalLLaMA)r/LocalLLaMA (corroborated by VideoCardz, TechRadar and ServeTheHome)Tom's Hardware / SemiAnalysis, via r/singularity and r/OpenAI NVIDIA AI Blog Latent Space (AINews)Hugging Face (Qwen) — corroborated by vLLM, LMSYS/SGLang, NVIDIA Technical Blog, r/LocalLLaMA (1,507up/384c)vLLM (corroborated by vllm-project/vllm v0.26.0 release notes)xAI SiliconANGLE OpenAI Blog

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