How much memory does DeepSeek V4-Flash need?

Computed from its real GGUF file, DeepSeek V4-Flash needs about 146 GB of combined VRAM plus system RAM to load, which is well beyond any single consumer graphics card. It is designed for multi-GPU servers and workstations.

What is the difference between DeepSeek V4-Pro and V4-Flash?

Both are open-weight Mixture-of-Experts models under the MIT license. V4-Flash has about 284 billion parameters; V4-Pro is the flagship at roughly 1.6 trillion parameters. Both are far too large for a single consumer GPU.

Which DeepSeek model can I run locally?

The DeepSeek-R1 distilled models. For example, DeepSeek-R1-Distill-Llama-8B runs fully on an 8 GB GPU at Q6_K_L (about 7.7 GB). There are also 1.5B and 7B distills for small GPUs, and 14B, 32B and 70B distills for 16 to 48 GB cards.

Can You Run DeepSeek V4 Locally?

Q: Can you run DeepSeek V4 locally?

Not realistically on a consumer PC. DeepSeek V4 is a very large Mixture-of-Experts model. Even the smaller V4-Flash (about 284B parameters) needs roughly 146 GB of combined memory and runs at about 0.3 tokens per second on a single 24 GB GPU with offloading. DeepSeek's smaller R1 distills, however, run well locally.

By Lefi Abdelmonem

Author · AI Local Check · Published July 17, 2026

DeepSeek V4 is one of the most talked-about open-weight releases of 2026 — and unlike Qwen or Llama at consumer sizes, the honest answer to "can I run it locally?" is: almost certainly not on a normal PC. Here's exactly why, with the real numbers, and which DeepSeek models you can run at home.

Why DeepSeek V4 doesn't fit a consumer GPU

DeepSeek V4 ships in two sizes, both Mixture-of-Experts (MoE) models under the MIT license:

DeepSeek V4-Flash — about 284 billion total parameters, with a 1,048,576-token (1M) context.
DeepSeek V4-Pro — the flagship, roughly 1.6 trillion parameters.

Even the smaller "Flash" model is enormous. Computed from its real GGUF file, DeepSeek V4-Flash needs about 146 GB of combined memory to load, and on a single 24 GB consumer GPU with system RAM offloading it runs at roughly 0.3 tokens per second — technically "runnable", but far too slow to be usable.

The reality: DeepSeek V4 is built for multi-GPU servers and workstations with 100+ GB of memory, not a single gaming card. V4-Pro (~1.6T) is larger still. No mainstream desktop GPU runs either one at a usable speed.

Which DeepSeek models CAN you run locally?

The good news: DeepSeek also releases much smaller distilled models that carry a lot of its reasoning ability into sizes that fit a normal GPU. These are open-weight and available as GGUF:

DeepSeek-R1-Distill-Llama-8B — ~8B parameters; runs fully on an 8 GB GPU at Q6_K_L (about 7.7 GB).
DeepSeek-R1-Distill-Qwen-7B — another 8 GB-friendly option.
DeepSeek-R1-Distill-Qwen-1.5B — tiny, runs on almost anything (even CPU-only).
Larger 14B, 32B and 70B distills exist for 16–48 GB cards.

These give you DeepSeek-style reasoning locally, privately, and for free — without the 146 GB requirement of the full V4.

How to check what fits your GPU

Memory needs depend on the model size, quantization and your context length. Rather than guess, search any DeepSeek model to see its exact RAM/VRAM per quantization, or pick your graphics card to see which models fit. New to quantization? Read GGUF quantization explained, and the best LLM for your VRAM for size-by-size picks.

The bottom line

DeepSeek V4 itself is a data-center model — even the 284B "Flash" build needs ~146 GB and crawls on a single GPU. But DeepSeek's R1 distills (from 1.5B to 70B) run beautifully on consumer hardware. Check exactly what your PC can run and pick the largest distill that fits your VRAM.

→ Check what your exact GPU can run