05:17
Run GPU-Powered Black Hole Interferometry with NVIDIA cuPhoton
Imaging black holes is challenging because they are distant and appear tiny in the sky—for example, M87*’s observed ring spans just 42 microarcseconds. The...
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Products & applications / 01Architecture patterns for fast session startup on Amazon Bedrock AgentCore runtimeAWS Machine Learning Blog
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llama.cpp releasesCompute & infrastructure / 03b12362llama.cpp releases
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1901 published storiesCompute & infrastructure · 262 storiesClear filters ×
2026-11-14
Follow the timeline02:52
b12364
A warning is now issued when the --spec-draft-p-min parameter is used with probabilistic draft sampling, as the draft's top probability does not reliably indicate how much of the draft the target model would accept under rejection sampling. The warning is skipped for drafters that do not use p_min, such as n-gram and DSpark.
01:58
b12362
The latest release of llama.cpp (b12362) includes optimizations for grammar matching through left factoring, improved handling of character class boundaries, prevention of infinite expansion, and enhanced code clarity with ASCII comments and deduplicated logic. It reverts prior changes related to AST refactoring and alternative limits, while introducing shared immutable rules and removing performance benchmarks. The release supports multiple platforms including macOS, Linux, Windows, Android, and iOS, with various backend options such as CUDA, Vulkan, ROCm, OpenVINO, and SYCL.
00:00
Patrick at Micro Center Grand Opening in Austin Texas Today
Patrick and Jeff will be at the grand opening of the Micro Center in Austin, Texas today so if you are around, come say hi The post Patrick at Micro Center Grand Opening in Austin Texas Today appeared first on ServeTheHome.
2026-11-13
Follow the timeline21:00
How AI Is Helping Care Teams Get Patients the Right Care, Sooner
From pediatric research to smart hospitals, AI is helping care teams spend less time on routine tasks and more time with patients. Health systems and startups are putting it to work across healthcare, from matching patients to clinical trials to supporting radiologists, hospital teams and patients at home. Clinicians stay at the center, reviewing AI […]
18:20
v1.0.0
The llama.cpp project has released version 1.0.0, marking a significant milestone with numerous improvements across multiple platforms including CUDA, Vulkan, Metal, SYCL, OpenCL, and WebGPU. Key updates include enhanced support for quantized models (Q5_0, Q5_1, FP8), optimizations for flash attention and MoE caching, improved server security features like CORS protection and SSRF mitigation, UI enhancements such as sidebar reorganization and live-preview markdown editing, and better cross-platform compatibility. The release also includes bug fixes, CI/CD improvements, and updated dependencies.
14:33
b12345
The release includes support for Q5_0 and Q5_1 quantization levels on Hexagon NPUs, with dedicated kernels for Q5_0 in the hex-mm module.
09:13
b12340
A security update has been released to address Server-Side Request Forgery (SSRF) vulnerabilities related to CORS proxy and media URLs in the llama.cpp server component.
08:46
Adapt Human Motion for Robots with NVIDIA SOMA Retargeter
Human motion offers roboticists a great starting point for teaching humanoid robots how to move. A human demonstration captures the coordination and timing...
08:34
b12336
The update requires the position embedding tensor for HunyuanVL to be explicitly provided as F32 during model loading to prevent buffer overflow issues when using F64 tensors, ensuring compatibility and stability on first image requests.
06:40
When One GPU Isn’t Enough: Serving GLM-5.3 Across Two NVIDIA DGX Stations with vLLM
Running a frontier-scale model locally means keeping all its weights in GPU memory; the largest models no longer fit on one machine. Two NVIDIA DGX Stations...
05:56
Chinese frontier AI models carry the fingerprint of export-controlled GPUs
Article URL: https://unemploymentlabs.com/checkpoint-forensics Comments URL: https://news.ycombinator.com/item?id=50474883 Points: 42 # Comments: 7
00:45
Fast by Default: Compile-Time Heuristics for Helion Kernels
TL;DR Helion now uses compile-time heuristics that analyze a kernel’s IR and dataflow to pick fast settings without slow autotuning. It classifies the kernel as matmul, reduction, or pointwise and... The post Fast by Default: Compile-Time Heuristics for Helion Kernels appeared first on PyTorch.
2026-11-12
Follow the timeline22:45
Integrating SGLang into Spyre in Under a Week with AI
TL;DR With help from AI coding agents, our two-person team integrated SGLang with IBM Spyre in less than a week, building a plugin of roughly 1,500 lines. Existing Spyre support... The post Integrating SGLang into Spyre in Under a Week with AI appeared first on PyTorch.
20:02
The Sequence Opinion - Issue #970: The Neocloud Financing Squeeze
Why booming AI demand can coexist with fragile balance sheets
08:00
DeepSeek-V4.1-Flash on Vera Rubin NVL72: 58x Throughput vs. GB300 NVL72 at 387 tokens/s
On one NVIDIA Vera Rubin NVL72 rack, vLLM serves DeepSeek-V4.1-Flash on SemiAnalysis AgentX at 1.7M tokens/s per GPU at a P90 of 387 tokens/s per user, 58x GB300 NVL72 at the same interactivity.
08:00
H3-Image Day-0 Support: 4K in 0.87s with Vera Rubin
H3-Image on Vera Rubin: 0.87-second 4K generation or 6,115 images/hour on four GPUs, with projected API revenue and support across consumer and data-center hardware.
08:00
Inkling 2 Small on vLLM: Day-0 Support
vLLM supports Thinking Machines' Inkling 2 Small from day 0: attention residuals, a new decode kernel for relative-bias attention, all-reduce fused with the ops around it, and DSpark speculative decoding.
02:15
b12287
MMVF is recommended for BF16 matrices with few rows and small batch sizes due to its superior performance when K is large, while MMF remains efficient for larger row counts. The decision depends on the condition rows × batch × 256 ≤ nsm × min(K, max_k) and K ≥ 1024, with GPU-specific limits on columns and max_k. Evaluation cases include 128×2560, 512×2560, and 320×10240 at batch sizes 2–8.
2026-11-11
Follow the timeline00:57
b12249
The update introduces graceful failure handling for unreachable RPC endpoints and failed handshakes in ggml-rpc, replacing fatal aborts with reportable errors. Functions now return appropriate values (e.g., false, nullptr) on connection failures, allowing host applications to handle issues without crashing. A regression test was added for unreachable endpoints, and low-value comments were removed. Mid-stream failures remain unchanged to prevent silent corruption.
Every update links back to its source.