CoreWeave has booked Nvidia A100 GPUs through 2029, nine years after the chip's May 2020 launch, CEO Mike Intrator confirmed on the company's second-quarter earnings call. Nvidia CEO Jensen Huang ampl…
NVIDIA's CUDA programming guide introduces a swizzling technique to eliminate shared memory bank conflicts without wasting memory, using an XOR-based formula that rearranges shared memory indices. The…
VectorWare has enabled Rust's portable SIMD (core::simd) to run natively on GPUs, allowing the same SIMD code to execute on x86 CPUs, ARM, and NVIDIA GPUs without modification. This breakthrough maps …
Nvidia CEO Jensen Huang said on August 13th that the A100 fleet remains 'mission-capable' through 2029, citing CUDA's role in extending the lifespan of AI accelerators. The statement amplifies a CoreW…
Danny Arends released DLLM, a minimal coding agent written in the D language that runs directly on llama.cpp via importC, eliminating Python and bindings. The agent operates a three-model pipeline (ag…
China's leading AI developers continue to rely on Nvidia chips for training large language models due to high transition costs to domestic alternatives, according to industry sources. The steep engine…
A developer attempting to run Qwen3.8-2, a 2.4-trillion-parameter model, on a 24GB consumer GPU encountered a CUDA out-of-memory error, with the system trying to allocate 12.50 GiB while only 4.20 GiB…
A fork of antirez/ds4 optimizes DeepSeek V4 Flash for NVIDIA GeForce RTX 5080 with 16 GB VRAM, achieving a 7.6% decode speedup (3.687 tok/s vs 3.427 tok/s baseline) and a 49.2% prefill improvement (49…
A developer's local LLM crashed during a RAG experiment due to CUDA out-of-memory errors caused by an oversized 32,768-token context window, which inflated the KV cache. Fixing the issue by sliding LM…
A developer's worklog documents iterative optimization of a CUDA matrix multiplication kernel to reach the top percentile on LeetGPU, focusing on memory hierarchy and data reuse bottlenecks rather tha…
A new CUDA benchmark compares three knowledge-distillation loss implementations—Full Dense KL, Forward-Chunked Loss, and Full Chunked KL—showing that the Full Chunked KL method fuses the output projec…
Engineers are integrating large language models into real-time data pipelines, facing challenges such as state synchronization and KV cache transfer bottlenecks. Optimizations using TensorRT-LLM and a…
Developer Pavan Sai rebuilt his portfolio as a cinematic, interactive experience instead of a traditional resume site, featuring a black and champagne gold visual theme with scene-based navigation. Th…
A user reports that the MiniMax H3 video generation model fails with a CUDA out-of-memory error while Wan 2.2 runs normally, citing insufficient GPU memory during VAE encoding. The error occurs in the…
VectorWare has made Rust's portable SIMD (core::simd) work natively on GPUs, allowing the same SIMD code to run on x86, ARM, and NVIDIA GPUs without modification. This direct mapping of SIMD vectors t…
A developer's third installment in a GPU optimization series details implementing distributed training for large language models using CUDA and ROCm, covering All-Reduce, Ring-AllReduce, and ZeRO shar…
NVIDIA announced partnerships with Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs and KKR to establish independent financing platforms aimed at mobilizing over $500 billion of third-party ca…
China's most advanced AI models are still being trained on Nvidia chips despite Beijing's push for semiconductor self-sufficiency, with industry insiders citing the high cost and complexity of switchi…
China's top AI developers remain reliant on Nvidia chips for training large language models, despite Beijing's push for self-sufficiency, due to high transition costs and the dominance of Nvidia's CUD…
An engineer details the GPU memory requirements for self-hosting large language models, explaining that the KV cache, not model weights, is the primary bottleneck under real traffic. The post provides…