The article presents benchmark results for DeepSeek V4 Flash 0731 on Strix Halo hardware, showing performance with different draft models and n_max settings, concluding that n_max=3 offers the best speed balance.
I know the front page is all Qwen3.8 27B right now. Fine model. I just finished a week of numbers on DeepSeek-V4-Flash-0731 on a Strix Halo box and figured I'd dump them here before I forget what I actually measured. Hardware is a Strix Halo mini PC, Ubuntu, 128 GB unified memory. Target is Unsloth's UD-IQ3_XXS with Q6 attention (~96 GB). At 264k context, no context quantization, the process sits at about 114 GB. At 64k I see 200–220 tok/s prefill and 20–22 tok/s decode with no draft. That's the baseline I used for everything below: 20.48 tok/s. I compared two DSpark drafters, Q8_0 (10.15 GB) and a Q2_K_S I quantized myself (6.45 GB), over n_max 2–7, 7 prompt types, 5 runs each. Same sampling the whole way: temp 0.9, top_p 0.95, min_p 0.01. Thinking on (reasoning_effort=low for the sweep so it wouldn't take a week). ngram-mod was off during the sweep so draft_n is only the drafter. Short version: n_max=3 is the peak. Average over both drafts is 28.5 tok/s, 1.39× the no-draft baseline. Q2 and Q8 are a wash. Every n_max, every category, they land inside 1–3% of each other. So the 6.45 GB file is the one I'd actually keep. One caveat on that 28.5 average: the suite is stacked with tasks that speculative decoding loves (repeat, math, code), and the prompts only used up to ~32k of context. Don't treat it as what you'll see on a mixed chat/coding day with a fuller window. Realistically I'd budget 22–28 tok/s. Best n_max per category (avg vs 20.48 tok/s): code — n_max 3 — 28.99 t/s — 1.42× json — n_max 3 — 28.59 — 1.40× math — n_max 3 — 31.22 — 1.52× dialog — n_max 3 — 25.48 — 1.24× translate — n_max 2 — 25.09 — 1.22× prose — n_max 2 — 23.38 — 1.14× repeat — n_max 5 — 40.17 — 1.96× n_max 5–7 only pays off on the high-acceptance stuff (repeat, some math). On prose / translate / dialog the extra draft tokens just get rejected and you go backwards. Overall table if you want it: n_max 2: 27.12 t/s, 1.32×, accept 0.69 n_max 3: 28.50, 1.39×, 0.60 n_max 4: 27.73, 1.35×, 0.52 n_max 5–7: ~26.4–26.5, 1.29×, accept ~0.45 I run this day to day at 128k with ngram-mod stacked on the drafter and thinking set to max. That's not what the sweep used (sweep was a 64k window, ~32k of it actually filled, draft-only, thinking=low), so don't treat the 28.5 as a promise for this exact line. The n_max=3 result is what I kept; 22–28 tok/s is what I'd quote for mixed use. llama-server \ -m DeepSeek-V4-Flash-0731-UD-IQ3_XXS-q6kattn.gguf \ -a DeepSeek-V4-Flash-0731-UD-IQ3_XXS \ --no-ui -ngl 999 -c 131072 --jinja -fa 1 --port 9989 \ --no-mmap --no-warmup -np 1 \ --temp 0.9 --top-p 0.95 --min-p 0.01 --host 0.0.0.0 \ -ngld 999 -fit off \ -md DeepSeek-V4-Flash-0731-DSpark-Drafter-Q2_K_S-dflash.gguf \ --spec-type ngram-mod,draft-dspark --spec-draft-n-max 3 \ -ub 1024 --cache-ram 2048 \ --chat-template-kwargs '{"reasoning_effort":"max"}' Fork is strix-halo-llamacpp (FA + MoE-prefill fixes, bundled Mesa, Vulkan/HIP). Upstream is still llama.cpp. Base weights: deepseek-ai/DeepSeek-V4-Flash-0731. GGUF I actually load: unsloth/DeepSeek-V4-Flash-0731-GGUF. Drafter: Lynxpda/DeepSeek-V4-Flash-0731-DSpark-Drafter-Q2_K_S-GGUF. Charts from the sweep (baseline drawn at 20.48): dashboard speedup_grouped_bars If you're on Strix Halo and already sitting on Flash 0731, --spec-draft-n-max 3 and the Q2 drafter is what I'd start with. Happy to answer questions about the sweep if something looks off.
Technical post detailing how to run DeepSeek V4 Flash on two Nvidia 4090d GPUs using custom Triton kernels and vLLM, achieving ~105 tokens/second with 262k context.
User asks for performance numbers on Deepseek V4 Flash running via Colibri, focusing on high VRAM setups, long context prefill, and token generation speed for agentic workloads.
The article explores how DeepSeek-V4-Flash, a powerful local model, makes LLM steering practical again, discussing the concept and its implementation in the DwarfStar 4 project by antirez.