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Text model · DeepSeek-V4

DeepSeek-V4-Pro: RAM and VRAM requirements

DeepSeek-V4-Pro needs about 965 GB to run at Q4_K_M (the Q4_K_M GGUF file is ~954.58 GB to download; KV cache and overhead add the rest), or about 1682.2 GB at Q8_0. The lightest hardware that runs it is a high-memory machine.

DeepSeek-V4 family · 1600B params (Mixture-of-Experts: activates only 49B of 1600B params per token, so generation is faster than the total size suggests) · released Apr 2026.

Shopping for hardware? See what runs DeepSeek-V4-Pro →

License MIT · Commercial OK ↓ 1.6M/mo ♥ 5.4K on HuggingFace
Q4_K_M GGUF
954.58 GB
Q8_0 GGUF
1671.82 GB
Memory @ Q4 (4k)
~965 GB
Context
1000 k

Q4_K_M and Q8_0 are GGUF download (file) sizes. Memory @ Q4 includes the KV cache and overhead, so it runs larger than the file on disk.

Will it run on your device?

DeepSeek-V4-Pro runs on 0 of 40 tracked devices at Q4_K_M.

0 run well 0 tight fit 40 too small
Fit check Q4_K_M
No, not enough memory
needs 965 GB usable 10.5 GB
See the full breakdown

Memory breakdown

Weights (Q4_K_M)954.58 GB
+
KV cache (4k)9.6 GB
+
Overhead0.8 GB
=
Total965 GB

How context length changes it

4k context ~965 GB 32k context ~1032.2 GB 128k context ~1262.6 GB

Longer context grows the KV cache, which for DeepSeek-V4-Pro is sized by its 49B active params, not the full 1600B. It needs ~965 GB at 4k and ~1262.6 GB at 128k.

Quantization sizes

GGUF quantson disk
Quantization Size on disk
Q2_K 670 GB est
Q3_K_M 782 GB est
Q4_K_M (default) 954.58 GB
Q5_K_M 1140 GB est
Q6_K 1312 GB est
Q8_0 1671.82 GB
FP16 3200 GB est

Lower quant = smaller and faster, slightly lower quality. Q4_K_M is the common default.

Run it

llama.cpp
$ llama-cli -hf teamblobfish/DeepSeek-V4-Pro-GGUF:Q4_K_M
LM Studio
$ lms get teamblobfish/DeepSeek-V4-Pro-GGUF

Which devices can run DeepSeek-V4-Pro?

Apple Silicon Macs

RAM-only laptops

iPhone & iPad

Android

NVIDIA GPUs

AMD GPUs

Same job, different size

Pick by what fits your memory: step down to free up VRAM, or step up if you have headroom.

Next size down Kimi K2.7 Code 1000B

Similar models

Head-to-head

FAQ

How much VRAM or RAM does DeepSeek-V4-Pro need?

At Q4_K_M, DeepSeek-V4-Pro needs about 965 GB (weights ~954.58 GB + KV cache + overhead) at a 4k context. At Q8_0 budget ~1682.2 GB.

What is the Q4_K_M GGUF file size of DeepSeek-V4-Pro?

The Q4_K_M GGUF file is about 954.58 GB to download, and the Q8_0 GGUF is about 1671.82 GB. That is the weights file on disk; to run it you also need room for the KV cache and overhead, so budget ~965 GB of memory at Q4_K_M.

Can DeepSeek-V4-Pro run on a laptop?

DeepSeek-V4-Pro is large; you need a high-memory Mac or multi-GPU setup at Q4_K_M.

Is DeepSeek-V4-Pro cheaper to run because it is a MoE model?

It is faster, not lighter. DeepSeek-V4-Pro activates only 49B of 1600B params per token (so it runs quickly), but all experts must stay in memory, so it still needs memory for the full 1600B.

Can I use DeepSeek-V4-Pro commercially?

Yes. DeepSeek-V4-Pro is licensed MIT, which permits commercial use.

Understand the numbers

Short guides to the ideas behind DeepSeek-V4-Pro's memory and quant figures.

The largest open-weight model tracked here: 1.6T total, 49B active, 1M context, MIT. Q4 is ~955GB (split community GGUFs); even 2-bit is 574GB. Strictly multi-node; listed for the record and the licence, not for a shopping decision.

Sources

Last validated 2026-08-03. Memory figures are estimates. See methodology.