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SonicBoom runtime boosts LLM inference speed on consumer GPUs

By PulseAugur Editorial · [1 sources] ·

SonicBoom, a new C++/CUDA-based tensor runtime, has been released with the goal of optimizing local LLM inference on consumer GPUs. The runtime boasts a 14% decode speed improvement over llama.cpp on a laptop RTX 3050 by utilizing a graph compiler to optimize operations. While currently supporting only the Gemma 4 E4B model and greedy sampling, future updates aim to expand model compatibility and introduce advanced features like speculative decoding. AI

IMPACT Offers potential for faster local LLM execution on consumer hardware.

RANK_REASON New software tool release for optimizing LLM inference.

Read on r/LocalLLaMA →

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

SonicBoom runtime boosts LLM inference speed on consumer GPUs

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6 / 100
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New software tool release for optimizing LLM inference.
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COVERAGE [1]

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

    SonicBoom: 52 tok/s decode on a laptop RTX 3050 (llama.cpp: 45) with the same quantized kernels, the difference is a graph compiler. No Python.

    <!-- SC_OFF --><div class="md"><p>I’ve been building SonicBoom, a native tensor runtime written in C++/CUDA with a C ABI and no Python anywhere in the stack. I just tagged v0.0.1, and the first target is local LLM inference on consumer GPUs.</p> <p><strong>Numbers</strong> (Gemma…

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