Entity trail

NVFP4

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

Briefing refs

Findings

Edges

Sources

Corpus findings

2026-07-10 / thought-leaders-researcherRadixArk's Miles Lands on the PyTorch Foundation Blog — a PyTorch-Native RL Post-Training Stack With a Two-Decorator APIMiles composes SGLang for rollout, Megatron-LM for training, and Ray for orchestration behind a small pluggable trainer, exposing a decorator-based API where you annotate @rollout and @update on an ordinary PyTorch loop and the framework handles distribution and weight sync. It ships Rollout Routing Replay to fix MoE routing drift across the rollout/training boundary, a unified BF16/FP8/MXFP8/NVFP4/INT4-QAT pipeline, and recipes for DeepSeek V4, GLM 5.2, Qwen3.6, Kimi K2.6 and Nemotron 3 Ultra. The repo went open source July 1 — the PyTorch Foundation feature is the new event here, so recency is softer than the rest of this list.
2026-06-30 / sources-researcherNVIDIA's June 2026 DGX Spark Update Makes Fully-Local Agents PracticalNVIDIA's June 2026 DGX Spark software release ships automated four-node clustering via a new Cluster Assistant (enabling ~700B models locally), a 2.6x throughput gain on Qwen3.6-35B through NVFP4 plus Multi-Token Prediction, and a streamlined NemoClaw install that drops setup from hours to under an hour. NemoClaw bundles open models, an agent harness (Hermes Agent / OpenClaw) and the sandboxed OpenShell runtime that adds access controls and guardrails to the agent loop — a concrete stack for builders running agents off-cloud.
2026-05-26 / reddit-researcherQwen3.5 35B A3B Heretic Uncensored Release Preserves All 785 MTPs — Immediate Community ResponseWithin hours of the Financial Times investigation into Heretic, the r/LocalLLaMA community (120 upvotes) shared a new Qwen3.5-35B-A3B uncensored model built with Heretic that preserves all 785 native Multi-Token Predictions. Available in Safetensors, GGUF, NVFP4, and GPTQ-Int4 formats on Hugging Face, this release demonstrates the speed at which decensored variants appear for new models — directly illustrating the FT's policy concerns about guardrail enforcement in open-weight AI.
2026-05-23 / github-pulse-researcherNVlabs/LongLive 2.0 at 1.7K Stars: NVIDIA Ships First NVFP4 Training and Inference System for Long Video Generation at 45.7 FPSNVIDIA's LongLive-2.0 introduces NVFP4-based parallel infrastructure for long video generation, achieving 2.15x training speedup and 1.84x inference speedup on Blackwell GPUs. The 5B parameter model hits 45.7 FPS inference while supporting long, interactive, multi-shot, and real-time generation in a single streamlined pipeline. Bypasses the complex multi-stage processes of previous methods with a direct AR training approach on bidirectional models.
2026-05-17 / hn-researcherNVIDIA Open-Sources SANA-WM: 2.6B-Parameter World Model Generates 1-Minute 720p Video on a Single GPU — 371 Points on HNNVIDIA Labs released SANA-WM under Apache 2.0 — a 2.6B-parameter Diffusion Transformer that takes a single image and camera trajectory and synthesizes 60-second 720p video. A distilled variant runs on a single RTX 5090 with NVFP4 quantization, producing a 60-second clip in 34 seconds (2.1x real-time). Trained in ~18.5 days on 64 H100s with only 212,975 public video clips, achieving 36x higher throughput than LingBot-World (14B+14B) at comparable VBench scores.
2026-05-07 / hn-researcherUnsloth and NVIDIA Collaborate on Three LLM Training Optimizations — Packed-Sequence Caching, Gradient Overlap, MoE RoutingUnsloth (27K+ GitHub stars) and NVIDIA published joint work on three training speedups: caching packed-sequence metadata to avoid per-layer reconstruction, dual-buffer gradient checkpointing for overlapping activation reloads with backward compute, and cheaper GPT-OSS MoE routing via argsort/bincount token grouping. Unsloth is now optimized for NVIDIA Blackwell GPUs with NVFP4 precision, spanning GeForce RTX 50 Series to enterprise HGX B200 systems.
2026-04-16 / rss-researcherNVIDIA Redefines AI Infrastructure Economics — Cost Per Token Is the Only Metric That Matters, Blackwell Delivers 15x Lower Cost vs. HopperNVIDIA published a framework repositioning data centers as 'AI token factories' where cost-per-token replaces traditional TCO metrics. Blackwell (GB200 NVL72) delivers 10x throughput per megawatt and 15x lower cost per million tokens versus Hopper, despite costing nearly double per hour ($2.65 vs $1.41). DeepInfra cut cost from 20 cents to 5 cents per million tokens moving to Blackwell with NVFP4. NVIDIA claims a $5M GB200 NVL72 system generates $75M in token revenue — a 15x ROI.
2026-04-05 / vibe-coding-researcherDGX Spark NVFP4 Finally Unlocked: Avarok's Open-Source vLLM Image Achieves 20% Over AWQ After 6 Months of Vendor InactionAfter 6+ months of NVIDIA's DGX Spark shipping without working NVFP4 quantization (the feature that justified the product's price), Avarok published an open-source vLLM fork that enables NVFP4 and benchmarks 20% faster than AWQ across all workloads. The root cause: GB10's SM 12.1 chip lacks the cvt.rn.satfinite.e2m1x2.f32 PTX instruction, causing a catastrophic fallback to 1.1 tok/s (30x slower than expected). Community frustration boiled over on r/LocalLLaMA with 235+ upvotes and 141 comments.
2026-03-17 / vibe-coding-researcherMistral Small 4 (119B-2603) + Leanstral: First Code Agent Designed for Lean 4 Formal ProofsMistral AI released Mistral Small 4, a 119B-parameter MoE model with an NVFP4 quantized variant co-developed with NVIDIA. In the same drop, Mistral released Leanstral-2603 — the first open-source code agent designed specifically for Lean 4, the formal proof assistant used to express mathematical structures like perfectoid spaces. Leanstral extends the code agent paradigm into formal verification, a domain where LLM hallucinations have historically been disqualifying.
2026-03-16 / news-researcherNVIDIA GTC 2026 Opens: Vera Rubin NVL72 Platform Formally Launches — 10x Lower Inference Cost Than Blackwell, H2 2026 AvailabilityNVIDIA's GTC 2026 keynote by Jensen Huang on March 16 formally kicked off the Vera Rubin era: 72-GPU NVL72 rack with 50 PFLOPS/GPU (NVFP4), 260 TB/s NVLink 6 bandwidth, BlueField-4 AI-native storage for agentic KV-cache sharing, and 10x lower inference cost per token vs. Blackwell. New NVIDIA Inference Context Memory Storage Platform introduced to accelerate agentic AI reasoning. Available from AWS, Google Cloud, Microsoft, CoreWeave, and Nebius in H2 2026.
2026-03-16 / sources-researcherNVIDIA GTC 2026 Keynote: NemoClaw Enterprise Agent Platform Debuts Alongside Vera Rubin NVL72 — 5x Inference Over Blackwell, 288GB HBM4Jensen Huang's March 16 keynote introduced NemoClaw, an open-source enterprise agent deployment platform designed to mirror CUDA's lock-in strategy at the application layer. Vera Rubin NVL72 racks (now at AWS, GCP, Azure, Oracle) deliver 50 PFLOPS inference at NVFP4—5x over Blackwell GB200—with 288GB HBM4 targeting agentic workloads requiring extended context and multi-tool call loops. Notably, Huang dedicated keynote time to specialized CPUs for agentic orchestration, signaling NVIDIA's strategic pivot from pure GPU inference to full-stack agent infrastructure.
2026-03-14 / news-researcherNVIDIA GTC 2026 Keynote March 16: Rubin GPU with 288GB HBM4, 5x Blackwell Throughput ExpectedJensen Huang's two-hour keynote kicks off March 16 in San Jose. Rubin-generation GPUs are expected to be the centerpiece, featuring up to 288GB HBM4 memory, 22TB/s bandwidth, and 35-50 petaFLOPS of dense NVFP4 performance — approximately 5x the floating-point throughput of current Blackwell parts. NVIDIA is also expected to detail OpenClaw, its agentic AI platform for always-running local assistants, and new cooling targets likely exceeding one megawatt per rack.

Graph relationships

USES
LongLive 2.0
LongLive 2.0 uses NVFP4 for training and inference.
Source finding
SUPPORTS
llama.cpp
NVFP4 support coming to llama.cpp for local inference
Source finding
USES
LongLive 2.0 -> NVFP4
LongLive 2.0 uses NVFP4 for training and inference.
Source finding
SUPPORTS
llama.cpp -> NVFP4
NVFP4 support coming to llama.cpp for local inference
Source finding

Source trail

DGX Spark update The pipeline The PAR build SANA-WM 371 points on HN Pixelle-Video PyTorch Foundation Blog r/LocalLLaMA GitHub Unsloth / Hacker News NVIDIA AI Blog r/LocalLLaMA

Graph sources

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