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capy-flash v0.7.1 enables larger Qwen3.8 models on consumer GPUs

By PulseAugur Editorial · [1 sources] ·

The open-source inference engine capy-flash has released version 0.7.1, enabling larger Qwen3.8 models to run on consumer hardware. The Qwen3.8-Flash-Next (125B MoE) model can now operate with 64 GB of RAM, achieving 234 tokens/sec on dual 16 GB GPUs. Additionally, the Qwen3.8-27B model, quantized to 3-bit, fits onto a single 16 GB card and runs at 174 tokens/sec. This update also improves CPU performance and memory handling for Windows users. AI

IMPACT This release makes larger, more capable Qwen3.8 models accessible to users with consumer-grade GPUs, potentially increasing adoption and experimentation.

RANK_REASON This is a software release for an inference engine that enables existing models to run on consumer hardware.

Read on r/LocalLLaMA →

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

capy-flash v0.7.1 enables larger Qwen3.8 models on consumer GPUs

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This is a software release for an inference engine that enables existing models to run on consumer hardware.
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COVERAGE [1]

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

    capy-flash v0.7.1: Qwen3.8-Flash-Next (125B MoE) now runs in 64 GB of RAM (234 tok/s on two 16 GB cards), and Qwen3.8-27B 3-bit fits on a single 16 GB card (174 tok/s)

    <!-- SC_OFF --><div class="md"><p>I've been building <strong>capy-flash</strong>, an open-source inference engine for running Qwen3.8-Flash-Next (125B MoE) and Qwen3.8-27B on consumer RTX 50 cards. Until now it needed my setup (two 16 GB cards and 96 GB of RAM) or better. v0.7 is…

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