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llama.cpp PR boosts inference speed by moving sampling to GPU

By PulseAugur Editorial · [1 sources] ·

A new pull request for llama.cpp aims to improve inference speed by moving sampling operations from the CPU to the GPU. This change resulted in an approximate 8% increase in tokens per second for the Qwen3.6:35b model on an RTX 5090, and a 4% boost on a Tesla P40. The performance gains are attributed to reducing the overhead of CPU-GPU data transfer, which is more pronounced on higher-end GPUs with greater memory bandwidth. AI

IMPACT This optimization could lead to faster local inference for users of llama.cpp, especially on consumer hardware.

RANK_REASON This is a code optimization for an open-source project, not a new model release or significant industry event.

Read on r/LocalLLaMA →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

llama.cpp PR boosts inference speed by moving sampling to GPU

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0 / 100
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Newsworthiness bucket
Tool
This is a code optimization for an open-source project, not a new model release or significant industry event.
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Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
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infra, product
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High
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Story freshness
43 days old
Aged out of breaking-news scoring windows; ranking reflects the durable signal from the full source set.

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COVERAGE [1]

  1. r/LocalLLaMA TIER_1 English(EN) · /u/otacon6531 ·

    Llama.cpp PR 8% speed boost

    <!-- SC_OFF --><div class="md"><p>Llama.cpp currently uses cpu based sampling for user with mtp enabled. The PR moves sampling to the gpu, which on a 5090 boasts an 8% increase in tok/s for qwen3.6:35b. I tested it on my P40 and observed a 4% increase inference speed boost.</p> <…

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