Search posts, papers, and topics
NVIDIA released CUDA‑Rust, letting you write GPU kernels directly in Rust and compile to PTX. Two programming models are supported: the traditional SIMT model via the `cuda-oxide` backend (nightly Rust, custom codegen) and the newer Tile model via `cutile‑rs` (stable Rust, JIT‑compiled Tile IR). Both provide Rust‑typed safety guarantees (e.g., `DisjointSlice`, tensor partitioning) and simple Carg…
Jevlike is an open‑source starter model that scores a list of text options in a single forward pass. It provides a minimal architecture (option queries, shared dot‑product scorer), synthetic data generation, training/evaluation CLI, and examples on Doom and chess. The repo supports a byte‑level encoder or a frozen Hugging‑Face encoder (e.g., Qwen2.5‑0.5B), runs on CPU/MPS/CUDA, and reports benchm…
OpenJev is a browser‑only demo that lets you load small LLM checkpoints (e.g., MiniCPM‑5 2B, Qwen3 0.6B) onto your GPU and compare two inference paths: reading raw logits for a set of options versus prompting the model to emit a JSON with option probabilities token‑by‑token. The page reports model sizes, download times, balanced accuracy on a few benchmarks, and wall‑clock timings measured with `…
mini-AGI is a byte‑level continual‑learning language model that fits on a single 8 GB GPU by paging expert weight files from disk. It uses two dense prelude blocks followed by a recurrent block applied up to 24 times per character, with adaptive depth (PonderNet halting) and per‑application top‑8 expert routing. The system maintains a RAM cache and a VRAM resident set, swaps experts based on dema…
BITCOS is a distribution‑aware storage layout for ternary LLM weights that replaces the standard five‑trit packing. By storing a presence bitmap and a compact sign vector, it reduces the effective bits‑per‑weight to 2 − z (z = zero density), achieving as low as 1.485 b/w on sparse models. The authors provide AVX‑512, AVX2, and Xe2 GPU unpacking kernels and show up to 1.28× speedup in matrix‑vecto…
A Voronoi fragment shader stutters on an RTX 4070; the author discovers that replacing `fract()` with `x‑floor(x)` eliminates the glitch, suspects a driver miscompile, and documents a week‑long manual debugging process with code, device tests, and LLM‑assisted hypotheses.
ByteShape releases full ShapeLearn quantizations for Qwen 3.8 27B, showing that their GPU‑specific GGUFs (GPU‑1…GPU‑5) dominate the quality‑throughput frontier across six GPUs, with GPU‑5 hitting 99.63 % of BF16 accuracy at 90 TPS on a 13.1 GB model. Speculative decoding (MTP, DFlash2) further boosts throughput, and the Lite set remains competitive.
Samsung plans to more than double its HBM4 and HBM4E output next year, raising glass‑carrier cleaning to 50 k sheets/month and wafer input to ~250 k per month. The HBM4 family’s share of shipments is expected to jump from ~40 % to ~80 % as the higher‑layer HBM4E ramps up.
The authors reverse‑engineer AMD’s CDNA matrix cores, build per‑architecture MATLAB models that match hardware bit‑for‑bit on 10 M random tests, and use them to compare AMD vs NVIDIA tensor‑core accuracy.
Increasing the number of LLM candidates (N) improves reasoning accuracy, but the way those candidates are generated (batch size vs sequential calls) dramatically affects latency, GPU‑hours, and energy. On A100 GPUs, eight serial 1‑candidate calls consume ~5× more energy and take ~6× longer than a single 8‑candidate batched call, even though total candidate count is identical. The authors recommen…
VC-Attention introduces a training‑free low‑bit attention pipeline for diffusion transformers. It smooths value tensors via lightweight online clustering (V‑Smooth) and quantizes only the residual after subtracting block means, restoring the mean from the softmax row sum. It also replaces the FP32 softmax exponential with a fused FP8 cast (ExpCast‑FP8) that maps log‑scores directly to E4M3 probab…
NVIDIA showcased new power‑aware AI‑factory features at the AI Infra Summit. DSX MaxLPS and Flex improve token‑per‑watt efficiency by up to 40%, while the Vera Rubin NVL72 platform delivers up to 30× higher throughput per megawatt on agentic workloads.
NVIDIA’s Vera Rubin NVL72 AI inference system shows up to 3.7× higher throughput than the prior GB300 NVL72 on MLPerf v6.1 benchmarks (Qwen3‑VL, DeepSeek‑R1), achieves 99% scaling efficiency across 288 GPUs, and benefits from software optimizations (NVFP4 precision, kernel fusion, disaggregated serving). The post is a product announcement with concrete benchmark numbers but limited technical dept…
University of Manchester used NVIDIA Earth‑2 CorrDiff and StormCast generative models to downscale UK‑wide air‑pollution simulations. Training on the Isambard‑AI supercomputer (5,448 GH200 chips, 21 EFLOPS) took two days on an eight‑GPU node, producing a 2‑3 km resolution model. Inference runs on a desktop‑class DGX Spark, enabling rapid scenario forecasting and potential real‑time health alerts.…
NVIDIA’s DSX platform lets AI data‑centers shift workloads in response to grid signals, squeezing ~24% more token throughput (4 M→5 M tps) and ~23% better performance‑per‑watt on a fixed megawatt budget. The first production demo used Emerald AI’s Conductor to drop a 4 MW load to 3 MW in under a minute without interrupting high‑priority jobs. DSX MaxLPS reallocates headroom across HGX B200 server…
Nvidia’s Jensen Huang announced Salesforce’s Koa, a CRM‑reasoning LLM built by fine‑tuning Nvidia Nemotron 3 Super on a synthetic, three‑decade‑spanning dataset. Koa uses supervised fine‑tuning plus RL (NeMo RL, Gym, AutoModel), covers 14+ industries, and claims 3× fewer errors on Salesforce’s CRM‑Bench versus leading models. It’s already in internal Slack agents and slated for limited customer p…
Microsoft open‑sourced TauGrid, a Helm‑installable platform that bundles Kueue, KubeRay and other components to schedule, monitor and checkpoint AI workloads on GPU‑enabled Kubernetes clusters. Engineers define jobs in a simple `tau.yaml`, and TauGrid handles queuing, health checks, and observability, simplifying AI workload management.
Nvidia’s blog spotlights five companies that are using Nvidia AI platforms to accelerate clean‑energy projects—from grid interconnection and nuclear plant operations to off‑grid AI data‑center power, advanced reactors, and fusion tokamaks. The article is a marketing summary and provides few technical details.
NVIDIA added Firefox on Windows to the list of supported browsers for GeForce NOW, letting users stream over 2,000 PC games directly from the browser. The service offers up to 1440p/120 fps RTX performance for Ultimate members, requiring only the latest Firefox version.
Firefox added NVIDIA GeForce NOW streaming on Windows, announced upcoming JPEG XL image support, ran a contributor survey, and hosted a Reddit AMA.
