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2026-10-30

llama.cpp releases19:00

b12002

ggml : add batched tensor copy API (#30881) * ggml : add generic batched tensor copy API Assisted-by: pi:llama.cpp/ggml-org/DeepSeek-V4-Flash-Vision-Exp-GGUF (Mac Studio, M2 Ultra, 192GB) * llama : use batched tensor copy API in context io Assisted-by: pi:llama.cpp/ggml-org/DeepSeek-V4-Flash-Vision-Exp-GGUF (Mac Studio, M2 Ultra, 192GB) * ggml : add batched tensor copy API Assisted-by: pi:llama.cpp/ggml-org/DeepSeek-V4-Flash-Vision-Exp-GGUF (Mac Studio, M2 Ultra, 192GB) * llama : batch device io tensor copies Assisted-by: pi:llama.cpp/ggml-org/DeepSeek-V4-Flash-Vision-Exp-GGUF (Mac Studio, M2 Ultra, 192GB) * ggml : add CUDA support for batched tensor APIs Assisted-by: pi:llama.cpp/ggml-org/DeepSeek-V4-Flash-Vision-Exp-GGUF (Mac Studio, M2 Ultra, 192GB) * ggml : remove async tensor_n APIs a

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2026-10-27

arXiv cs.DC12:00

When and Why to Use TMA: A Workload-Grounded Characterization of Tensor Memory Acceleration on NVIDIA Hopper GPUs

arXiv:2610.34907v1 Announce Type: new Abstract: The Tensor Memory Accelerator (TMA), introduced on NVIDIA Hopper, is used pervasively in production GPU kernels yet its usage is conspicuously selective: expert kernel developers in CUTLASS, FlashAttention-3, and FlashInfer apply TMA to some tensor operands within a kernel but fall back to cp.async, direct global loads, or distributed shared memory (DSM) for others. Vendor documentation describes the API, and recent microbenchmarking work characterizes TMA throughput in isolation, but neither answers the question practitioners actually face: given a memory operand in a kernel, should it use TMA, and if so, with or without cluster multicast? We address this gap with a workload-grounded characterization study on H100. We catalog the TMA, multic

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