4× RTX PRO 6000
235tok/sEveryday
1 request, prose prompt, context 19, with DSpark (per recipe) Source
Intelligence34with thinking
- Custom kernel required
Local AI · Recipe · 4× RTX 3090
DeepSeek-V4-Flash 180.4B Q3_K with llama.cpp on 4× RTX 3090
DeepSeek-V4-Flash 180.4B Q3_K with llama.cpp on 4× RTX 3090: 38.9 tok/s according to github.com (dataset as of Nov 3, 2026).
by alesha-pro
Intelligence (original model) · Artificial Analysisno independent value
≤ 3 bit: quantization may cost quality
Engine
- Engine
- llama.cpp ds4-longctx @b001c8cd73
- Quantization
- GGUF Experten Q3_K/Q4_K (down Q4_K), Attention/Shared/Indexer/Output Q8_0, APE F32; 89.9 GB, 3.99 bpw
- Model family
- DeepSeek-V4-Flash
- Context
- 262,144
- Parameters
- 180.4B MoE (REAP, 160/256 Experten)
- Creator
- alesha-pro
- GitHub stars
- 56
- Repo updated
- Aug 8, 2026
Measurements
Every sourced value of this recipe, each with its condition and source. Bars relative to the largest value in the group.
Decode · 1 request
Prefill by context
What you need
- Hardware
- 4 × NVIDIA RTX 3090
- Weights
- Main weightsalesha-pro/DeepSeek-V4-Flash-0731-REAP-K160-GGUF MIT
- source0xSero/DeepSeek-V4-Flash-0731-REAP MIT
- Engine
- llama.cpp ds4-longctx @b001c8cd73
- Context
- 262,144 tokens
Notes
What matters before you rebuild it.
- ≤ 3 bit: quantization may cost qualityAt 3 bit and below the model may answer noticeably worse than the original. The intelligence number refers to the original.
- Experimental
- Custom kernel required
Sources
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