How to Deploy DeepSeek V4.1 Flash Locally
DeepSeek V4.1 Flash is a MoE model with 552B total parameters and 8 / 16B active per token. At Q4 it needs 309GB of VRAM to sit entirely on the GPU; with experts offloaded to system RAM it needs only 7GB of VRAM plus 304GB of RAM. The cheapest card that runs it today is the Mac Studio M3 Ultra 512GB at about $19,315, at roughly 41.2 tokens/s. In offload mode the cheapest card that runs it is the Tesla V100 16GB, at roughly 9 tokens/s.
DeepSeek V4.1 Flash details
How much VRAM does DeepSeek V4.1 Flash need for local deployment?
MoE: all 552B parameters must stay resident in memory, while speed is set by the 16B active per token.
| Quantization | WeightsGB | Total at 8KGB | Total at 32KGB | Total at 128KGB |
|---|---|---|---|---|
| 1-bit | 129.6est. | 130.6 | 130.6 | 130.7 |
| 2-bit | 205.1est. | 206.1 | 206.1 | 206.2 |
| 3-bit | 264.5est. | 265.5 | 265.5 | 265.6 |
| 4-bit | 307.7est. | 308.7 | 308.7 | 308.8 |
| 5-bit | 372.5est. | 373.5 | 373.5 | 373.6 |
| 8-bit | 572.2est. | 573.2 | 573.2 | 573.3 |
| FP16 | 1,079.6est. | 1,080.6 | 1,080.6 | 1,080.7 |
Total = weights + KV cache (FP16) + 1GB runtime overhead. Weights marked GGUF are measured file sizes from the repository; the rest are estimated from bytes per parameter. Contexts beyond this model's 1M limit show a dash. Each tier takes whichever file of that bit width the repository has, preferring unsloth dynamic quants, so the exact file name varies by model.
Which GPUs can run DeepSeek V4.1 Flash?
Xianyu
| Device | VRAMGB | Price | Est. t/s | Suggested context |
|---|---|---|---|---|
| Mac Studio M3 Ultra 512GB | 512 | $19,3156 listings | 41.2 | 128K |
Fits when weights + KV cache + runtime overhead is at or below usable memory, single card. "Offload" on MoE models means it runs with expert weights in system RAM (only attention layers and KV cache stay in VRAM); that speed assumes 70 GB/s RAM bandwidth. The context column is the largest tier that fits.
Recommended GPUs for DeepSeek V4.1 Flash with CPU/GPU offloading
Xianyu
Expert weights live in system RAM (needs at least 304 GB); VRAM holds only attention layers, shared experts and the KV cache, at least 7 GB at Q4. Speed is bound by RAM bandwidth (estimated at 70 GB/s) and is far slower than a full-VRAM setup.
Deploy DeepSeek V4.1 Flash locally with Ollama, llama.cpp or vLLM
The Ollama library has no official tag for it yet.
Follow the deployment guide to prepare the required runtime and hardware.
docker run --gpus all --privileged --ipc=host -p 8000:8000 -v ~/.cache/huggingface:/root/.cache/huggingface -e VLLM_ENGINE_READY_TIMEOUT_S=3600 -e VLLM_USE_RUST_FRONTEND=1 vllm/vllm-openai:deepseekv41-flash-0909 deepseek-ai/DeepSeek-V4.1-Flash --tokenizer-mode deepseek_v41 --tensor-parallel-size 8 --tool-call-parser deepseek_v41 --enable-auto-tool-choice --reasoning-parser deepseek_v41 --mm-encoder-tp-mode data
File names follow that week's model-library snapshot; the definitions are in the methodology. Methodology
FAQ
How much VRAM does DeepSeek V4.1 Flash need?
At Q4_K_M with an 8K context DeepSeek V4.1 Flash needs about 309GB of VRAM; 372GB leaves comfortable headroom.
What is the cheapest GPU that runs DeepSeek V4.1 Flash?
The Mac Studio M3 Ultra 512GB: 512GB of VRAM, currently about $19,315, at roughly 41.2 tokens/s.
Can you run DeepSeek V4.1 Flash with Ollama?
Not yet: there is no official Ollama tag and no public GGUF build for it.