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8 GB GPU, 262K context 3.5 BPW Qwen-Flash-Next Coder 70 T/s using llama.cpp

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 24/10/2026, 21:47:26 UTC.

Briefings

A user successfully runs Qwen-Flash-Next Coder 70 T/s on an 8 GB GPU using llama.cpp, leveraging 24 GB RAM and 32 GB total system memory. The model uses 3.5 bits per weight with 400 MB of non-coding vocabulary pruned, achieving 70 T/s peak performance. The author recommends using the Vulkan backend if CUDA fails due to VRAM constraints and suggests disabling hardware acceleration to save VRAM.

Key points

• Qwen-Flash-Next Coder 70 T/s is achieved on an 8 GB GPU using llama.cpp with 24 GB RAM and 32 GB total system memory.
• The model employs 3.5 bits per weight with 400 MB of non-coding vocabulary pruned, utilizing selective MOE offloading for peak performance.

Context

This finding demonstrates that Qwen-Flash-Next Coder 70 T/s can run on consumer-grade 8 GB GPUs via llama.cpp, provided sufficient RAM and system memory are available. The method involves keeping most MOEs in RAM and n-gram data on disk, with CUDA struggles noted as a potential pitfall requiring the Vulkan backend.

Limitations

Performance is limited by PCIE bandwidth and MMAP overhead, with the first run slower than subsequent runs. Specific launch parameters and hardware constraints (e.g., 3.0 YMMV PCIE speeds) are variable and not standardized.

Generated by Bittrees-hosted qwen35-2b sha256:aaf42c8b7c3c on 24/10/2026, 22:05:36 UTC. Based on publisher-provided feed evidence; may not cover the full original article. Check the source for details.

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