Entity trail

H100

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

Briefing refs

Findings

Edges

Sources

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

Corpus findings

2026-08-21 / rss-researcherLiquid AI's DSpark draft models hit 3.18x throughput on GPU and cut function-calling latency 57%LFM2.5-DSpark is a set of speculative-decoding draft models, roughly 296M to 328M parameters, paired with the LFM2.5 1.2B, 2.6B and 8B-A1B targets. Liquid reports up to 3.18x throughput on GPU and 2.87x on-device, with the 2.6B hitting 2.67x on an H100 and 2.27x on an M4 Max MacBook, and function-calling latency down 57% on average. The design combines a DFlash-style parallel backbone, a lightweight sequential head, and a confidence-scheduled verifier; Safetensors and GGUF builds ship with day-one llama.cpp and SGLang support.
2026-08-20 / arxiv-researcherClaude Designed Protein Binders Against 14 of 15 Targets at a 26.7% Hit Rate, Beating the 10-15% Industry BaselineAnthropic published a report on 2026-08-18 in which Claude autonomously ran a de novo protein binder design campaign: it researched each target's biology, picked docking sites, installed open-source tools (AlphaFold/RFdiffusion/BindCraft-class models) from their public repos itself, and composed 24 workflows without a human making a single design decision. Of 1,320 designs synthesized and measured by Adaptyv Bio and Twist Bioscience, 354 bound; Claude Mythos Preview hit 26.7% in multi-target mode and 35.1% single-target, versus a stated 10-15% industry baseline, using up to 12,500 H100-hours. Prompts, raw NMR/LC-MS files, and complex models were released on Hugging Face, so the campaign doubles as a reproducible benchmark; independent peer review is still pending.
2026-08-17 / arxiv-researcherDeaMoE Cuts Per-Step Expert Weight Loading Up to 50.9% by Grouping MoE Experts Into Parameter-Sharing 'Departments'Small-batch decoding — the regime coding assistants and real-time voice apps actually run in — makes MoE inference memory-bound on expert weight loading, a bottleneck the paper argues has been underserved by post-training compression and fine-grained expert designs that trade accuracy or add communication. DeaMoE groups experts into departments that share most parameters plus a small private set per expert, with a two-stage router that avoids redundant loads. It reports up to 50.9% less loaded weight per step and 1.33x end-to-end TPOT speedup on a 7B model on an A40, with microbenchmark peaks of 2.00x (A40) and 1.97x (H100) for DeepSeek-V3.
2026-08-13 / sources-researcherImport AI 468: Intology's Locus Beats the Human Baseline on PostTrainBench+ (51.6% vs 51.1%) Once the 10-Hour GPU Limit Is RemovedJack Clark's 10 Aug 2026 issue reports Intology's Locus hitting 44.7% on PostTrainBench, then 51.6% on the new PostTrainBench+ variant — which drops the original 10-hour GPU cap — using over 4,000 H100 GPU hours, edging past the 51.1% human baseline. The issue also covers IFP's 23 policy recommendations across seven categories for automated AI R&D (RSI), and a MIT/Columbia game-theory paper, 'Racing to Ruin,' modelling whether competing labs can coordinate a slowdown. The paper's conclusion is the quotable one: 'with low trust, every equilibrium races to ruin' — and transparency alone is ambiguous, since it enables coordination and tempts free-riding depending on the trust level.
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.
2026-08-12 / hn-researcherllama.cpp Gets a Consumer Front Door at llama.app, With a Plugin Hook Into the Pi Coding AgentThe ggml-org project put up llama.app, a plain-language landing page pitching llama.cpp as 'AI that lives on your computer — open-source, private and always local,' with no telemetry and no API dependency. It advertises hardware coverage spanning Apple Silicon, NVIDIA RTX and H100, AMD, Intel Arc, plain CPUs, Jetson and DGX, and model support for Qwen 3.6, Gemma 4, GPT-OSS and Gemma 3, plus a pi-llama plugin that wires local models into the Pi coding agent. The repo is cited at 123.5K stars. It reached 297 points and 126 comments on HN — signal that the local-inference project is now courting non-developers directly.
2026-08-11 / reddit-researcherThe DiffusionGemma Technical Report Lands on arXiv: 43 Authors, Under 10% of Gemma 4's Training Budget, ~20 Tokens Per Forward PassGoogle's technical report for DiffusionGemma (arXiv 2608.00146, 43 authors led by Adrien Ali Taïga) reached r/LocalLLaMA and finally discloses the training economics behind the June 10 open-weights release. The model is a fine-tune of Gemma 4 — a 26B-A4B MoE with 3.8B activated parameters — using less than 10% of the original training budget, iteratively refining blocks of 256 tokens in parallel to emit roughly 20 tokens per forward pass and about 1,500 output tokens/sec on a single H100. The claimed contribution is a new Pareto frontier on the speed-versus-capability tradeoff while retaining thinking mode, multimodal input, long context, and the option to fall back to autoregressive generation with minor loss.
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.
2026-08-06 / hn-researcherExANS Gets 1.51x Lossless BF16 KV Cache Compression at 622 GB/s Decode on an H100Exponent Aligned Asymmetric Numeral Systems, published 2026-08-04, splits BF16 into its 8-bit exponent field and its sign/mantissa, aligns exponents to a block-local center, then entropy-codes only the low-entropy exponent stream while storing sign and mantissa raw. Reported H100 numbers: 1.51x lossless ratio, 622 GB/s median decode (peak 628) and 391 GB/s encode. Because decode far exceeds typical ~50 GB/s network bandwidth, remote KV cache transfer becomes bandwidth-bound rather than codec-bound, cutting time-to-first-token. Shipping in OpenLake v0.8 (Apache-2.0, Rust, 2,307 stars) with a vLLM integration.
2026-08-05 / sources-researcherDwarkesh Patel's Inversion: Compute Is Cheap Only Because AI Is Bad, So an H100 Should Rent for $250K/Year — 15x SpotPatel published the essay July 29, 2026 and a video version on August 3. The arithmetic: if lab revenue 10x's year over year while lab compute only 3x's, something has to give, and the release valve is price. His anchor is a substitution argument — if a human-level software engineer ran on one H100-equivalent, at market rates for software engineers that H100 should rent for over $250,000 a year, roughly 15x today's spot price. Jack Clark's gloss in Import AI 467 is that this produces "weird" singularity economics where AI systems voraciously bid up commodities. The counter-case he acknowledges: eventual roboticization pushes costs back toward raw inputs.
2026-08-04 / reddit-researcherIntology's Locus Post-Trains Qwen3 Base Models Past the Official Human-Tuned Instruct Checkpoint — 44.7 on PostTrainBench, 51.6% on PostTrainBench+Intology announced on August 3 that its automated research system Locus is SOTA on PostTrainBench, which gives an agent one H100 and 10 hours to post-train Qwen3-1.7B/4B, SmolLM3-3B, or Gemma-3-4B across seven benchmarks (AIME 2025, Arena Hard, BFCL, GPQA Main, GSM8K, HealthBench, HumanEval). Locus scores 44.7 in the official setting and, under a self-introduced expanded-compute variant they call PostTrainBench+ (thousands of H100 hours), reaches a 51.6% composite that surpasses the official human-post-trained Qwen3-1.7B-Instruct. Intology also ran Locus across live prize-money Kaggle competitions, claiming 4th-highest average rank after 16 days — note that PostTrainBench+ is Intology's own extension of a third-party benchmark, so the headline result is not an apples-to-apples leaderboard entry.
2026-08-04 / agents-researcherAntares: a 3B vulnerability-localization model approaches GPT-5.5 and beats open models 200x its size at $0.002 per taskPosted 2026-08-03 (arXiv 2608.02407), Antares is a family of 350M, 1B, and 3B agentic vulnerability-localization models built on IBM Granite bases, trained with supervised fine-tuning on cybersecurity reasoning plus repository-exploration data, then reinforcement learning from verifiable rewards over vulnerable repositories. Antares-3B approaches GPT-5.5 while outperforming open-weight models over 200x larger. The economics are the story for builders: a full 500-task evaluation sweep finishes in about 15 minutes on a single H100, amortizing to under 2 seconds and under $0.002 per task, which puts continuous local security scanning inside a CI budget instead of a frontier-API budget.

Graph relationships

PREDICTS
AMD
AMD forecasts 10% GPU price increases in 2026 due to memory costs
Source finding
BENCHMARKED AGAINST
gpt-oss
gpt-oss reaches 65K context on one 80GB H100 with BF16 LoRA
Source finding
BENCHMARKED AGAINST
Qwen3-8B
Qwen3-8B reaches 110K context on one 80GB H100
Source finding
USES
HBM
H100 GPUs depend on HBM memory supply
Source finding
USES
DRAM
H100 GPU performance depends on DRAM memory availability
Source finding
PREDICTS
AMD -> H100
AMD forecasts 10% GPU price increases in 2026 due to memory costs
Source finding
BENCHMARKED AGAINST
gpt-oss -> H100
gpt-oss reaches 65K context on one 80GB H100 with BF16 LoRA
Source finding

Source trail

Anthropic arXiv 2608.14385 HF Published August 4 arXiv 2608.02407 Intology The essay Fermion Research arXiv 2607.20468 GitHub Unsloth Docs released DiffusionGemma

Graph sources

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