Can I run DeepSeek R1 on my own hardware?

Name: DeepSeek R1
Brand: huggingface

Yes — DeepSeek R1 is an open-weight model you can run on a workstation or on-prem server you own. At Q4_K_M it needs roughly 390.2 GB of GPU memory with an 8,192-token context — more than a single 80 GB card, so plan for multiple GPUs or a large unified-memory machine. Wide Area Intelligence routes requests to your own nodes first and fails over to the cloud only when they can't serve.

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huggingface

DeepSeek R1

hf/unsloth/DeepSeek-R1-GGUF

↓ runs free on your own hardware⚙ tool calling

Open-weight model — runs free on your own GPU via the node runtime (GGUF). 25,272 downloads and 1,120 likes on Hugging Face (unsloth/DeepSeek-R1-GGUF).

specs & pricing

Type: text
Provider: huggingface
Model ID: hf/unsloth/DeepSeek-R1-GGUF
Capabilities: tools
Self-hostable: Yes — runs on your own GPU

Listed in the directory for discovery. textmodels aren't callable through the chat gateway yet — image and video models can run on your own nodes today.

Run it on your own hardware

Estimated GPU memory for DeepSeek R1 (685B parameters), including the KV cache for a 8,192-token context.

Q4_K_M: ~390.2 GBmulti-GPU or large unified memory
Q8_0: ~677.1 GBmulti-GPU or large unified memory
FP16 / BF16: ~1276.3 GBmulti-GPU or large unified memory

Install the agent on the machine and it dials out to the gateway over a single WebSocket — no port forwarding or inbound firewall rules. Requests for this model go to your node first and fail over to the cloud only when it can't serve.

VRAM calculator →Which GPU do I need? →

/// initialize

Call DeepSeek R1 through one OpenAI-compatible endpoint.

Serve it from hardware you own, with cloud failover when your nodes are busy or offline.

Run it on your own hardware

no credit card · 2 nodes free · openai-compatible

Frequently asked questions

Can I run DeepSeek R1 on my own hardware?: Yes — DeepSeek R1 is an open-weight model you can run on a workstation or on-prem server you own. At Q4_K_M it needs roughly 390.2 GB of GPU memory with an 8,192-token context — more than a single 80 GB card, so plan for multiple GPUs or a large unified-memory machine. Wide Area Intelligence routes requests to your own nodes first and fails over to the cloud only when they can't serve.

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