DeepSeek V4.1 Flash — VRAM, Speed & Local Setup

Autor: Jakub Rusinowski · Ostatnia aktualizacja: 26 czerwca 2026

Model library → DeepSeek V4.1 → DeepSeek V4.1 Flash

PREVIEW — unverified. ~284B-total MoE with ~13B active, 1M context, MIT. Workstation-tier like V4-Flash (~140 GB at Q4); mainline local runtimes still WIP. Estimates carried from V4-Flash — confirm on the Hugging Face model card.

DeepSeek V4.1 Flash needs about 172 GB of VRAM at Q4 (experimental) — quantized weights plus framework overhead, before any KV cache. On Apple Silicon that figure comes out of unified memory.

Specifications

Parameters284 Billion (13B active)
Context window1,000,000
ArchitectureMixture-of-Experts
ProviderDeepSeek
LicenceMIT
Specified atQ4 (experimental)
System RAM256 GB
Record updated2026-06-26

Licence

MIT — commercial use permitted. Commercial use permitted. No usage restrictions beyond attribution.

VRAM and Speed by Quantization

Modelled on a reference NVIDIA RTX 4090 (24 GB), with no KV cache (this record has no published architecture). Speed figures are ESTIMATES from the memory-bandwidth roofline described on the methodology page, not benchmarks we ran — rows marked measured come from published or reader-submitted runs. VRAM here includes the KV cache, so it reads higher than the headline figure above, which does not.

QuantWeightsVRAM neededEst. speedFit on 24 GB
Q2_K93.4 GB94.2 GB—Won't fit
Q3_K_M121.1 GB121.9 GB—Won't fit
Q4_K_M171.5 GB172.3 GB—Won't fit
Q5_K_M201.3 GB202.1 GB—Won't fit
Q6_K232.9 GB233.7 GB—Won't fit
Q8_0301.8 GB302.6 GB—Won't fit
F16568.0 GB568.8 GB—Won't fit

Want the memory numbers alone, at every quantization level and your own context length? Use the DeepSeek V4.1 Flash VRAM calculator.

Buy This HardwareApple Mac Studio M2 Ultra — 192 GB VRAM · 60 W board powerDeploy in the Cloud NowRunPod

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Recommended GPU

The cheapest catalogued GPU that runs DeepSeek V4.1 Flash is the Apple M2 Ultra (192 GB).

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Apple Mac Studio M2 Ultra
192 GB VRAM · 60 W board power
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How to Run DeepSeek V4.1 Flash

Install Ollama, then run:

ollama run deepseek-v4-1

Weights on Hugging Face: deepseek-ai/DeepSeek-V4.1-Flash.

Best for: reasoning, coding, long context, enthusiast workstation.

Can I Run DeepSeek V4.1 Flash on My GPU?

Other DeepSeek V4.1 Sizes

DeepSeek V4.1 Flash — Frequently Asked Questions

How much VRAM does DeepSeek V4.1 Flash need?
About 172 GB at Q4 (experimental) — quantized weights plus framework overhead, before any KV cache. The cache grows with context length and is added on top; the table above folds it in. Apple Silicon counts unified memory toward the same figure.
Does DeepSeek V4.1 Flash run on an RTX 4090 (24 GB)?
No. DeepSeek V4.1 Flash needs about 172 GB at Q4 (experimental), more than a single RTX 4090's 24 GB. It needs a larger card, several GPUs, or Apple Silicon with enough unified memory — or it runs with part of the weights offloaded to system RAM, which is much slower.
How do I run DeepSeek V4.1 Flash locally?
Install Ollama and run `ollama run deepseek-v4-1`. That pulls the weights and starts a local OpenAI-compatible endpoint; after the download nothing leaves the machine.
What other sizes does DeepSeek V4.1 come in?
DeepSeek V4.1 Flash (172 GB), DeepSeek V4.1 (967 GB). Every size shares the family's training and licence; the larger ones score higher and need proportionally more memory.

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