DeepSeek V4.1 — Local AI Model by DeepSeek

Name: LLM Configurator — GPU VRAM Checker
Author: LLM Configurator

作者： Jakub Rusinowski · 最后更新： 2026年7月10日

PREVIEW (June 2026, specs unverified). Incremental update to DeepSeek V4 — V4.1 (flagship, ~1.6T/49B) and V4.1 Flash (~284B/13B), MIT-licensed with a 1M-token context. Local support tracks V4 (experimental llama.cpp forks only). Numbers carried from the V4 lineage; verify against the Hugging Face org page.

Hardware Requirements

DeepSeek V4.1 Flash	Min 172 GB VRAM · Q4 (experimental) · 1,000,000 ctx ·
DeepSeek V4.1	Min 967 GB VRAM · Q4 (experimental, datacenter only) · 1,000,000 ctx ·

Recommended GPU

The cheapest GPU that runs DeepSeek V4.1 locally (min 172 GB VRAM) is the Apple M2 Ultra (192 GB).

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Apple Mac Studio M2 Ultra

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How to Run Locally

Install Ollama then run: ollama run

Minimum VRAM: 172 GB. For best results use Q4_K_M quantization.

DeepSeek V4.1 — Frequently Asked Questions

How much VRAM does DeepSeek V4.1 need?

DeepSeek V4.1 needs about 172 GB VRAM at Q4 (experimental) quantization for its smallest variant. Variants: DeepSeek V4.1 Flash (172 GB, Q4 (experimental)); DeepSeek V4.1 (967 GB, Q4 (experimental, datacenter only)). On Apple Silicon, unified memory counts toward this requirement.

Can I run DeepSeek V4.1 on an RTX 4090 (24 GB)?

DeepSeek V4.1's smallest variant needs about 172 GB, which exceeds a single RTX 4090 (24 GB). Use multiple GPUs, a higher-VRAM card, or Apple Silicon with large unified memory.

What quantization should I use for DeepSeek V4.1?

Q4_K_M is the best balance of quality and VRAM for DeepSeek V4.1 in most cases. Choose Q8_0 for near-lossless quality if you have spare VRAM, or smaller quants (Q3/Q2) only when memory is tight.

How do I run DeepSeek V4.1 with Ollama?

Install Ollama, then run: ollama run . This downloads DeepSeek V4.1 and starts a local, OpenAI-compatible endpoint — no internet connection is needed after the initial download.