DeepSeek-V3.1-Nex-N1 671B — VRAM, Speed & Local Setup

Written by Jakub Rusinowski · Last updated September 8, 2026

Model library → Nex-N1 → DeepSeek-V3.1-Nex-N1 671B

DeepSeek V3.1 post-trained by Nex-AGI for agent autonomy and tool use — the base model's 671B weights at the base model's size, so 406 GB at Q4_K_M. Scores 80.2 on tau2-Bench, 70.6 on SWE-bench Verified, 65.3 on BFCL v4 and 29.5 on GAIA2. MLA attention keeps the KV cache small enough for a 128K context at this scale.

DeepSeek-V3.1-Nex-N1 671B needs about 406 GB of VRAM at Q4_K_M — quantized weights plus framework overhead, before any KV cache. On Apple Silicon that figure comes out of unified memory.

Specifications

Parameters671 Billion (37B active)
Context window128,000
ArchitectureMixture-of-Experts (DeepSeek V3.1 post-train, MLA attention)
ProviderNex-AGI
LicenceMIT
Specified atQ4_K_M
System RAM512 GB
Record updated2026-09-08

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), at 8K context. 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_K220.6 GB222.0 GB—Won't fit
Q3_K_M286.0 GB287.4 GB—Won't fit
Q4_K_M405.1 GB406.5 GB—Won't fit
Q5_K_M475.6 GB476.9 GB—Won't fit
Q6_K550.2 GB551.6 GB—Won't fit
Q8_0712.9 GB714.3 GB—Won't fit
F161342.0 GB1343.4 GB—Won't fit

Want the memory numbers alone, at every quantization level and your own context length? Use the DeepSeek-V3.1-Nex-N1 671B VRAM calculator.

Buy This HardwareApple Mac Studio M3 Ultra — 512 GB VRAM · 60 W board powerDeploy in the Cloud NowRunPod

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Recommended GPU

The cheapest catalogued GPU that runs DeepSeek-V3.1-Nex-N1 671B is the Apple M3 Ultra (512 GB).

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Apple Mac Studio M3 Ultra
512 GB VRAM · 60 W board power
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How to Run DeepSeek-V3.1-Nex-N1 671B

Install Ollama, then run:

ollama run nex-n1

Weights on Hugging Face: nex-agi/DeepSeek-V3.1-Nex-N1.

Best for: agentic tasks, software engineering, research, enterprise.

Can I Run DeepSeek-V3.1-Nex-N1 671B on My GPU?

DeepSeek-V3.1-Nex-N1 671B — Frequently Asked Questions

How much VRAM does DeepSeek-V3.1-Nex-N1 671B need?
About 406 GB at Q4_K_M — 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-V3.1-Nex-N1 671B run on an RTX 4090 (24 GB)?
No. DeepSeek-V3.1-Nex-N1 671B needs about 406 GB at Q4_K_M, 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-V3.1-Nex-N1 671B locally?
Install Ollama and run `ollama run nex-n1`. That pulls the weights and starts a local OpenAI-compatible endpoint; after the download nothing leaves the machine.

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