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tandem.cpp boosts MoE model speed on consumer hardware · 1 source tracked

By PulseAugur Editorial · [1 sources] ·

A new patch set for llama.cpp, called tandem.cpp, has been developed to improve the performance of Mixture-of-Experts (MoE) models that exceed GPU memory capacity. By intelligently distributing model experts between the GPU and CPU, tandem.cpp achieves significant speedups, with Qwen3.8-Flash-Next seeing a 3.3x increase in tokens per second on a single RTX 4090 with 64GB of RAM. This optimization also benefits other MoE models, offering speed improvements of 1.4x to 2.1x and faster prompt processing for longer contexts. AI

IMPACT Enhances local inference performance for MoE models, making them more accessible on consumer hardware.

RANK_REASON A new software patch set for an existing inference engine that improves performance for a specific model architecture.

Read on r/LocalLLaMA →

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

tandem.cpp boosts MoE model speed on consumer hardware · 1 source tracked

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A new software patch set for an existing inference engine that improves performance for a specific model architecture.
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COVERAGE [1]

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

    tandem.cpp: Qwen3.8-Flash-Next at 97 tok/s on a single 4090 + 64 GB RAM, 3.3× llama.cpp, works for any MoE

    <!-- SC_OFF --><div class="md"><p>tandem.cpp is a patch set for llama.cpp that makes MoE models that don't fit in VRAM run faster. It keeps the most-used experts on the GPU and lets the CPU help with the rest, for any MoE model llama.cpp supports. Single-user decode is the target…

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