github 看到一个项目， 3090 跑 27B， 129tps，最高 207tps

[stefwoo]

https://github.com/Luce-Org/lucebox-hub

DFlash DDtree Qwen3.5 & Qwen3.6 27B GGUF on RTX 3090 First GGUF port of DFlash speculative decoding. Qwen3.5-27B on a single RTX 3090, Q4_K_M target + BF16 draft, DDTree budget=22.

Up to 207 tok/s in the demo (207.6 tok/s DFlash vs 38.0 tok/s AR, 5.46×) 129.5 tok/s mean on the HumanEval 10-prompt bench 3.43× faster than autoregressive (+15% over chain speculative decoding) 2.8× faster than SGLang AWQ on the same hardware Up to 256K context in 24 GB via TurboQuant TQ3_0 KV cache (128K Q4_0 bench: 134.78 tok/s at ctx=131072)

PFlash speculative prefill on RTX 3090 In-process speculative prefill, C++/CUDA only. A drafter (Qwen3-0.6B BF16) loaded directly into the dflash daemon scores per-token importance over a long prompt; the heavy target (Qwen3.6-27B Q4_K_M) only prefills the spans that matter. Both models share the same ggml allocator on a single RTX 3090. No Python, no Triton, no PyTorch at runtime — just the dflash binary and four custom CUDA kernels (mean_K → score → select → sparse_fwd) plus BSA (mit-han-lab/Block-Sparse-Attention, FA-2 derived, sm_80+) for the long-context drafter forward.

~10.4× TTFT on 128K context: 24.8 s dflash daemon vs ~257 s llama.cpp (FA on, Q4_0 KV). 10.0× TTFT on 64K context: 13.5 s dflash vs 134.95 s llama.cpp. NIAH single-needle retrieved at every measured context (32K → 128K), keep_ratio=0.05, DFLASH_FP_ALPHA=0.85.

[stefwoo]

为了把大模型和草稿小模型一起塞进 24G 显存，选 4-bit 量化（~16G ），草稿保持 BF16 （~1.2G ），KV 缓存用 quant 量化。
预填充时，草稿小模型飞速扫遍长文本，只挑出最重要的 5% 片段；大模型只对这 5% 做稀疏预填充，跳过其余 95% 的无关内容。
随后进入生成阶段：草稿模型一次幻想出多个候选 token ，大模型用树形注意力一次性验证整棵树，实现高速逐词解码。

[strobber16]

这让我回去再看了一遍 bycloud 讲 speculative decoding 的那个视频。然后发现自己还是看不懂

[Hermitist]

全是跑 cuda 的, 可惜了我的 macbook air 测试不了

[beyondstars]

这么激进的 quantization 确定不影响模型实际表现吗？

[sddyzm]

27b 就是弱智