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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 25/10/2026, 06:47:26 UTC.

Briefings

A user successfully ran Qwen-Flash-Next Coder 70 T/s on an 8 GB GPU using llama.cpp, leveraging 262K context and 24 GB RAM. The model uses 3.5 BPW quantization with 6B active parameters, requiring an SSD and disabling hardware acceleration. Performance peaked at 104 T/s PP, though initial runs were slower due to memory mapping. The author recommends the sharp chat template and notes CUDA struggles with 8 GB VRAM, suggesting Vulkan as an alternative.

Key points

• Qwen-Flash-Next Coder 70 T/s was achieved on an 8 GB GPU using llama.cpp with 262K context and 24 GB RAM.
• The model uses 3.5 BPW quantization with 6B active parameters, requiring an SSD and disabling hardware acceleration.

Context

This finding demonstrates that Qwen-Flash-Next Coder 70 T/s can run on consumer-grade 8 GB GPUs via llama.cpp, provided memory is managed via SSD and specific launch parameters are used.

Limitations

Initial runs were slower due to memory mapping; final T/s results required disabling MMAP. CUDA struggles with 8 GB VRAM, suggesting Vulkan backend is necessary. Performance varies by PCIE speed and background processes.

Generated by Bittrees-hosted qwen35-2b sha256:aaf42c8b7c3c on 25/10/2026, 07:01:17 UTC. Based on publisher-provided feed evidence; may not cover the full original article. Check the source for details.

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