DeepSeek1.6T (49B active)~967 GB VRAM at Q2 (experimental, datacenter only)

DeepSeek V4-Pro — VRAM, speed & local setup

Written by Jakub Rusinowski · Last updated April 24, 2026

DeepSeek's flagship MoE — 1.6T total, 49B active, 1M context, MIT license. Not realistically self-hostable on any consumer or prosumer setup: even Q2 quantization is estimated around 400GB, requiring an 8x80GB+ H100/H800/B200-class server. Included for reference; the API or DeepSeek's hosted chat is the practical access path for almost everyone.

DeepSeek V4-Pro needs about 967 GB of VRAM at Q2 (experimental, datacenter only) — quantized weights plus framework overhead, before any KV cache. On Apple Silicon that figure comes out of unified memory.

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VRAM and speed by quantization

Quoted against NVIDIA RTX 4090 (24 GB). Weights plus overhead only: this model's architecture is unpublished, so no KV cache is added.

Quant	VRAM	Speed (est.)	Fit
Q2_K 2.63 bpw	526.8 GB	—	Too big
Q3_K_M 3.41 bpw	682.8 GB	—	Too big
Q4_K_M 4.83 bpw	966.8 GB	—	Too big
Q5_K_M 5.67 bpw	1134.8 GB	—	Too big
Q6_K 6.56 bpw	1312.8 GB	—	Too big
Q8_0 8.50 bpw	1700.8 GB	—	Too big
F16 16.00 bpw	3200.8 GB	—	Too big

Black marker = usable memory on the NVIDIA RTX 4090 (24 GB). Estimates from the memory-bandwidth roofline on the methodology page. DeepSeek V4-Pro VRAM calculator →

Get DeepSeek V4-Pro running

DeepSeek V4-Pro is not practical on a single consumer GPU: it needs about 966.8 GB at Q4_K_M, more than any single device in our catalogue holds with headroom. How multi-GPU inference works, or rent a multi-GPU server by the hour:

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How to run DeepSeek V4-Pro

Install Ollama, then run:

$ollama run deepseek-v4

Weights on Hugging Face: deepseek-ai/DeepSeek-V4-Pro ↗

Specifications

Verified — Checked against the primary source — the model card or the vendor spec page — and corroborated by a second independent source.

Parameters: 1.6 Trillion (49B active)
Context window: 1,000,000
Architecture: Mixture-of-Experts
Provider: DeepSeek
Licence: MIT
Practical deployment: Private cloud / datacenter cluster
Specified at: Q2 (experimental, datacenter only)
System RAM: 1024 GB
Record updated: 2026-04-24

LicenceMITCommercial use permitted

Commercial use permitted. No usage restrictions beyond attribution.

Quality and use cases

Scores as published by the model’s authors or an independent evaluator — quality, not throughput, and not measured by us.

Best forfrontier tasksenterpriseresearchcloud api

Can I run DeepSeek V4-Pro on my GPU?

Other DeepSeek V4 sizes

DeepSeek V4-Flash~172 GB at Q4_K_M

DeepSeek V4-Pro — frequently asked questions

How much VRAM does DeepSeek V4-Pro need?

About 967 GB at Q2 (experimental, datacenter only) — 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-Pro run on an RTX 4090 (24 GB)?

No. DeepSeek V4-Pro needs about 967 GB at Q2 (experimental, datacenter only), 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-Pro locally?

Install Ollama and run `ollama run deepseek-v4`. That pulls the weights and starts a local OpenAI-compatible endpoint; after the download nothing leaves the machine.

What other sizes does DeepSeek V4 come in?

DeepSeek V4-Flash (172 GB), DeepSeek V4-Pro (967 GB). Every size shares the family's training and licence; the larger ones score higher and need proportionally more memory.