标签：#NVIDIA

Linux 7.1 Boosts Intel Arc, Flatpak Integrates ROCm, Vintage AMD Driver Refined Today's Highlights Recent developments enhance GPU performance and accessibility, with the Linux 7.1 kernel providing significant gains for Intel Arc Battlemage graphics. AMD's ROCm compute platform gains broader deployment potential through Flatpak 1.18 integration, while an older AMD GPU driver sees notable code cleanups. Linux 7.1 Helping Intel Arc Battlemage Graphics Achieve Better Performance (Phoronix) Source: https://www.phoronix.com/review/intel-b580-linux-71 Phoronix reports that the upcoming Linux 7.1 kernel release is delivering superior graphics performance for Intel's Arc B580 Battlemage desktop graphics card compared to the current stable Linux 7.0. This indicates ongoing, critical optimization work within the open-source Linux graphics stack, directly impacting the gaming and compute capabilities of Intel's latest GPU architecture. Such kernel-level improvements are vital for unlocking the full potential of new hardware on Linux platforms, ensuring users receive the best possible experience from their Intel Arc GPUs. The performance uplift suggests that deeper integration and fine-tuning of the kernel's display and compute drivers are progressing, addressing potential bottlenecks and enhancing throughput. For users and developers leveraging Intel Arc GPUs on Linux, this kernel update is a significant milestone, promising more stable and efficient operation for various workloads, from gaming to professional applications. It highlights the dynamic nature of Linux driver development, where continuous collaboration leads to tangible performance benefits even before major hardware refreshes. Comment: This shows how crucial kernel updates are for modern GPUs on Linux. Early adopters of Arc Battlemage should definitely keep an eye on Linux 7.1 for a noticeable performance bump. Flatpak 1.18 Released With Integration For AMD ROCm (Phoronix) Source: https://www.phoronix.com/news/Fl

Just in case you were wondering, Nvidia's RTX Spark isn't supposed to be a one-off. The company is not just flirting with becoming the fifth high-profile vendor of consumer laptop chips to see if people bite. At Computex 2026 in Taipei, Nvidia CEO Jensen Huang confirmed at least two additional generations of RTX Spark are […]

One hardware announcement and several software highlights from Microsoft Build.

Nvidia's new chips will power laptop workstations and mini desktop PCs at first.

Computex 2026 is kicking off in Taipei, Taiwan this week, where Nvidia, AMD, Qualcomm, Intel, and other tech brands are announcing new laptops, handhelds, chips, and more. Nvidia unveiled RTX Spark, its first family of consumer PC chips, arriving in laptops and mini PCs starting this fall. Intel is launching two new custom chips made […]

NVIDIA's CEO Jensen Huang is set to take the stage for his GTC Taipei keynote at 8PM PT / 11PM ET. You can watch all the announcements here and embedded below. Rumors have been flying about what to expect from today's presentation, but the big one is the possibility of a partnership with Microsoft and […]

Notes on Serving LLMs with TensorRT-LLM and Triton 2026-05-31 · LLM serving / NVIDIA stack These are working notes on taking an open-weights LLM from a Hugging Face checkpoint to a production-style serving endpoint on the NVIDIA stack — TensorRT-LLM for the engine, Triton Inference Server for the deployment surface — and benchmarking it honestly against vLLM on multi-GPU hardware. They follow the harness in trtllm-triton-serving (4× H100, NVLink). The goal is to move from "I use vLLM" to "I can stand up the NVIDIA inference stack on real multi-GPU hardware and reason about the trade-offs." 1. The serving pipeline The path from checkpoint to endpoint has four stages. Each one is a place where a decision affects latency, throughput, or accuracy: Checkpoint — a Hugging Face model. Engine build — compile to a TensorRT-LLM engine for a fixed tensor-parallel degree, precision, and batching policy. Model repository — wrap the engine in a Triton tensorrt_llm -backend model repo. Serving + load test — trtllm-serve (or Triton) exposes an OpenAI-compatible endpoint; a load generator drives it under controlled concurrency. The key mental shift from vLLM: TensorRT-LLM does ahead-of-time compilation . vLLM is a runtime that takes the model and serves it; TensorRT-LLM builds an engine specialized to your GPU, TP degree, and precision first. That build is where the performance comes from, and also where the rigidity comes from. 2. Tensor parallelism (TP) For a model that doesn't fit on one GPU — or to cut latency — TensorRT-LLM shards each layer across GPUs. On a 4× H100 NVLink box, TP=4 means every forward pass does an all-reduce across the four GPUs over NVLink. The all-reduce is not free. On this fabric it tops out around 77 % of the NVLink budget (see the separate NVLink-wall notes ). For prefill (large tensors) you're bandwidth-bound and TP helps. For decode (one token at a time) you're pinned against the small-message latency floor, and past a point more TP makes decode slowe

Nvidia says the Control Panel's features have been migrated to the Nvidia app.