Can I run DeepSeek V4 Pro 0813 locally?
Only on server-class hardware. The smallest complete main-model GGUF is 849.7GB across 20 shards. A 512GB Mac Studio and a 768GB workstation cannot load the weights. About 1TB of combined RAM/VRAM or unified memory is the first plausible short-context tier; 1.5TB is the safer target.
Answer by hardware tier
Weight capacity is the first gate. Passing it still does not guarantee useful speed: bandwidth, interconnects, offload layout and context length determine whether generation is practical.
| Hardware memory | Q4 result | Why |
|---|---|---|
| 128–512GB | NO | The 849.7GB files alone exceed installed memory. |
| 768GB | NO | Still 81.7GB short before any runtime overhead. |
| 1TB | BORDERLINE | Weights can fit at short context, but the remaining margin is limited. |
| 1.5TB+ | SAFER | Room for runtime, context, fragmentation and operational headroom. |
| 4×GB300 node | OFFICIAL ROUTE | DeepSeek's current vLLM example uses this server configuration. |
Actual GGUF sizes and memory floor
The byte totals below come from all 20 shards in each Hugging Face directory. The loaded-weight column adds AICanRun's 5% mapping and alignment allowance, but deliberately leaves KV cache, runtime buffers and the operating system uncounted. These are planning estimates, not measured speeds.
| Quant | Files | Weights | Loaded-weight floor | Practical tier |
|---|---|---|---|---|
| UD-Q4_K_XL | 20 shards | 849.7GB | At least 892.2GB for loaded weights | 1TB is the first plausible tier; 1.5TB is safer |
| UD-Q8_K_XL | 20 shards | 873.4GB | At least 917.1GB for loaded weights | 1TB is tight; 1.5TB is the safer target |
Why 49B active still needs nearly 1TB
DeepSeek V4 Pro is sparse: about 49B parameters participate in each token, but the router can choose from the main model's full 1.573T-parameter expert set. Those experts must remain addressable, so active parameters reduce compute rather than turning the publisher-rounded 1.6T release into a 49B download.
The Q8 set is only 23.8GB larger than Q4 because these are Unsloth Dynamic formats. Much of the expert portion keeps its native low-precision representation; the Q4/Q8 label mainly changes other tensors. Do not apply the usual dense-model assumption that Q8 must be roughly twice the Q4 size. These GGUFs omit the separate 41.8B-parameter DSpark draft head, so this page makes no DSpark speed claim.
Two valid ways to use V4 Pro
Run your app, editor or Harness locally and call DeepSeek's hosted model. This is the practical route for ordinary computers; your machine does not store the 849.7GB weights.
Official API model and pricing page ↗Download the open weights or GGUF and serve them on your own infrastructure. This gives you the strongest control boundary, but storage, memory, networking and inference operations become your responsibility.
Official open-weight release ↗Use a current llama.cpp build with DeepSeek V4 and multi-shard GGUF support. Start with a short context only after the full 849.7GB set fits. This command follows the current Unsloth V4 guidance; AICanRun has not executed the Pro model on 1TB-class hardware.
hf download unsloth/DeepSeek-V4-Pro-0813-GGUF \
--local-dir DeepSeek-V4-Pro-0813-GGUF \
--include "UD-Q4_K_XL/*"
llama-server \
-m DeepSeek-V4-Pro-0813-GGUF/UD-Q4_K_XL/DeepSeek-V4-Pro-0813-UD-Q4_K_XL-00001-of-00020.gguf \
--ctx-size 32768 \
--temp 1.0 \
--top-p 0.95DeepSeek's raw checkpoint does not ship a Jinja chat template; it provides a dedicated encoder instead. The GGUF path relies on Unsloth's current V4 template support. For production servers, use the official vLLM or SGLang recipes linked from the publisher card rather than treating this workstation command as a benchmark.
DeepSeek's official vLLM example serves the unquantized release on a single 4×GB300 node and enables its DSpark speculative module. The current GGUF repository does not include that separate draft head, so the official DSpark setup and the community GGUF setup are not interchangeable.
DeepSeek V4 Pro 0813 vs V4 Flash 0731
Pro has about 1.6T total and 49B active parameters; its smallest current GGUF is 849.7GB. V4 Flash has 284B total and 13B active parameters, with catalogued weights starting at 117.3GB. Even Pro's smallest set is about 5.5× the size of Flash's 155GB MXFP4 file.
Choose Pro when maximum agentic quality justifies a large server. For a high-memory workstation, DeepSeek V4 Flash is the more realistic local model. DeepSeek's official code-agent evaluation used the minimal Harness profile; our tested Harness setup guide explains how to run the agent locally while keeping model inference on an API or separate server.
DeepSeek V4 Pro or Gemini 3.7 Flash?
Choose DeepSeek when downloadable MIT-licensed weights and self-hosting control justify server-class infrastructure. Choose Gemini when you want a hosted coding and agent model from an ordinary Mac or PC without operating an enormous model server. Our Gemini 3.7 Flash local-vs-cloud guide explains what stays on your computer and what remains on Google's servers.
Quick answers
Can I run DeepSeek V4 Pro 0813 locally?
Only on server-class hardware. The smallest complete main-model GGUF is 849.7GB before runtime and context memory. About 1TB of combined RAM and VRAM or unified memory is the first plausible short-context tier; 1.5TB provides a safer margin.
How much RAM or VRAM does DeepSeek V4 Pro 0813 need?
The 849.7GB Q4 set needs at least about 892.2GB just for loaded weights after a 5% mapping and alignment allowance. KV cache, the inference runtime and the operating system need additional memory, so 1TB is a floor rather than a comfortable recommendation.
Can a 512GB Mac Studio run DeepSeek V4 Pro 0813?
No. Even the smallest current GGUF is about 337.7GB larger than the machine's installed memory before macOS, runtime or context overhead. A 768GB workstation also cannot load the weights.
Why does 49B active not mean it needs only 49GB of memory?
DeepSeek V4 Pro is a mixture-of-experts model. It activates roughly 49B parameters for each token, which reduces compute, but the full expert set still has to remain addressable. Storage and memory therefore follow the roughly 1.6T total model, not only the active slice.
Why are the Q4 and Q8 GGUF files so close in size?
These are Unsloth Dynamic formats, not ordinary dense-model Q4 and Q8 conversions. Much of the expert portion is retained in its native low-precision representation, while the labels mainly change other tensors. The measured difference between the two complete sets is only 23.8GB.
Can I use DeepSeek V4 Pro's full 1M-token context locally?
The architecture supports up to 1,048,576 tokens, but that is not a practical default. Start with a short context only after the weights fit; KV cache and working buffers grow with context and further raise an already server-class memory requirement.
Should I run DeepSeek V4 Pro 0813 or V4 Flash 0731 locally?
V4 Flash is the realistic local choice for a high-memory workstation. Its catalogued weights start at 117.3GB, versus 849.7GB for V4 Pro. Pro targets maximum agentic capability on large servers; Flash is much easier to host yourself.
Can DeepSeek V4 Pro connect to DeepSeek Harness?
Yes, through a compatible model endpoint. DeepSeek's official code-agent evaluation used the minimal Harness profile, but most users should run Harness locally while calling a hosted API. Self-hosting the model itself still requires roughly 1TB or more of model-server memory.
Is 1TB of memory enough for DeepSeek V4 Pro 0813?
It is the first plausible short-context tier, not a comfortable guarantee. The Q4 loaded-weight floor is already about 892.2GB before KV cache, runtime buffers, the operating system and allocator fragmentation. A 1.5TB-class server gives substantially safer headroom.
Has AICanRun tested DeepSeek V4 Pro locally?
No. We verified the exact repository bytes, shard completeness, tensor-level parameter count and upstream run instructions, but we do not own a 1TB-class model server. This page labels memory values as planning estimates and makes no measured TPS claim.
Sources: official DeepSeek model card · current GGUF files · Unsloth V4 run guide. No speed figure on this page is presented as an AICanRun measurement.