Model Releases
This draft model is OP on 16 GB cards for Qwen 3.8 27b
https://huggingface.co/HermiHg/Qwen3.8-27B-DFlash2-Q2_K_S-MIX-GGUF I used this draft model with https://huggingface.co/ISTA-DASLab/Qwen3.8-27B-GSQ-RCO-GGUF with the IQ3_XXS with 128k context and I saw
https://huggingface.co/HermiHg/Qwen3.8-27B-DFlash2-Q2_K_S-MIX-GGUF I used this draft model with https://huggingface.co/ISTA-DASLab/Qwen3.8-27B-GSQ-RCO-GGUF with the IQ3_XXS with 128k context and I saw it averaging about 60 tokens per second tg speed on the 16 GB RX 9070 XT. This is much better than using the built in MTP since the built in MTP seems to multiply the required VRAM. For single thread mode, I'd say this is one of the best choices on a 16 GB GPU. Though speculative draft models seem to make it more sensitive to GTT overflow, so if you use more VRAM than you have, then turning off speculative decoding is actually faster, at least on my DDR5 PCIE 5 system. I use kv cache quantization of k 8_0 and v 4_0 for this setup. This person has done more extensive testing on a different card https://huggingface.co/HermiHg/Qwen3.8-27B-DFlash2-Q2_K_S-MIX-GGUF/discussions/3 submitted by /u/pneuny [link] [comments]
Related
- yall are sleeping on qwen 3.8 27b q2 + q2 dflash + q5 kv
- 16 GB VRAM purgatory discussion thread
- A preliminary Qwen3.8-27B model card is live!
- DFlash 2 available for Qwen 3.8 27B and Muse Glimmer
- [[benchmark-optimal-dflash2-quants-for-speed-and-context-size-|[Benchmark] Optimal DFlash2 quants for speed and context size, 5090 RTX, llama.cpp, Qwen 3.8 27B Dynamic3 Unsloth. Comparison with MTP]]
Source: r/LocalLLaMA | 2026-09-12