Entity trail

B300

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

Briefing refs

Findings

Edges

Sources

Corpus findings

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.
2026-08-23 / reddit-researcherA Homelab Operator Is Expanding a DGX Spark Cluster From 16 to 36 Nodes for 4.6TB of Unified MemoryThe builder behind what they claim was the first 16x DGX Spark cluster is adding 20 more Sparks, wired with one 24x200Gb QSFP56 plus 8x400Gb switch, 24 DAC cables and six 400Gb-to-2x200Gb breakouts. The rack is partitioned into 'inference modules' rather than one big serving target, with 16 nodes reserved for SOTA models like Kimi K3 and the rest doing rerank, embeddings, video, image and audio, coordinated as one persistent agent via Hermes and a custom memory sidecar. The stated reason for Sparks over B200/B300 is homelab cooling and power, plus easier resale liquidity.
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.
2026-08-20 / reddit-researcherSomeone Pre-Trained a 1.02B Kimi-K3 Replica on 5 Billion Tokens for $250 and Beat GPT-2 124M on HellaSwagA builder rented GPUs on Modal and pre-trained a 1.02B-parameter model (145M active per token) on exactly 5,000,003,584 decontaminated tokens for $250, keeping K3's actual architecture including Kimi Delta Attention, Gated MLA, attention residuals, LatentMoE with the aux-loss-free balancer, and K3's unmodified 163,840-token tokenizer. It scores 33.4% on HellaSwag against GPT-2 124M's 28%, with no instruction tuning at all. The model is roughly one two-thousandth of K3 by size, and the full tutorial plus a companion guide to hosting the real 2.8T K3 on eight B300s is published on books.vizuara.ai.
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.

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

Graph sources

entity graphfindings textkg edgeskg entitiesnewsletter issues

Fetching from the wire…