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Lean-QwenFlash: Qwen3.8-Flash-Next (125B) running at ~20 tok/s on 16gb vram and 8gb ram via direct nvme SSD offloading

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 25/10/2026, 20:47:26 UTC.

Briefings

A user adapted an existing GLM-5.3 offload technique to Qwen3.8-Flash-Next, achieving ~20 tokens per second on a 16GB VRAM setup. Benchmarks show 20.0 tok/s decode at 262k context and 1,627 tok/s prefill, with peak VRAM usage at 15.5 GB. The method utilizes direct NVMe SSD offloading to reduce system RAM requirements.

Key points

• Qwen3.8-Flash-Next achieves 20.0 tok/s decode and 1,627 tok/s prefill at 262k context length on a 16GB VRAM system.
• Peak VRAM usage reaches 15.5 GB while maintaining high throughput on a 16GB system RAM allocation.

Context

This finding demonstrates an efficient offloading technique for large language models, specifically applying a GLM-5.3 implementation to Qwen3.8-Flash-Next to optimize performance on constrained hardware.

Limitations

Benchmarks are limited to specific hardware configurations (RTX 4090, RTX 3080 Laptop) and context lengths (8k to 262k). Results are from a GitHub repository and do not represent production-grade performance or generalizability across different hardware or model variants.

Generated by Bittrees-hosted qwen35-2b sha256:aaf42c8b7c3c on 25/10/2026, 20:54:13 UTC. Based on publisher-provided feed evidence; may not cover the full original article. Check the source for details.

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