Reasoning
How to deploy DeepSeek-R1-Distill-Llama-70B on a GPU cloud
A 70.6B model tuned to reason step by step before answering. Full specs, license and use cases.
DeepSeek-R1-Distill-Llama-70B size and hardware requirements
70.6B
Total parameters
Dense (no MoE)
Architecture
BF16
Published precision
157.7 GB
Min VRAM (native)
| Precision | Weight size on disk | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| BF16 | 131.4 GB | 157.7 GB | RTX 3090 | 7 | $1.03/hr |
| FP8 (quantized) | 65.7 GB | 78.8 GB | RTX PRO 6000 | 1 | $1.64/hr |
| INT4 (quantized) | 32.9 GB | 39.4 GB | RTX A6000 | 1 | $0.330/hr |
How to run DeepSeek-R1-Distill-Llama-70B
Run DeepSeek-R1-Distill-Llama-70B with vLLM
From deepseek-ai/DeepSeek-R1-Distill-Llama-70B's own deployment docs.
vllm serve deepseek-ai/DeepSeek-R1-Distill-Qwen-32B --tensor-parallel-size 2 --max-model-len 32768 --enforce-eagerSource: https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-70B/raw/main/README.md
Run DeepSeek-R1-Distill-Llama-70B with GGUF quantizations
Prebuilt GGUF weights published at unsloth/DeepSeek-R1-Distill-Llama-70B-GGUF. Run with llama.cpp's llama-server or load the repo directly in LM Studio.
llama-server -hf unsloth/DeepSeek-R1-Distill-Llama-70B-GGUFSource: https://huggingface.co/unsloth/DeepSeek-R1-Distill-Llama-70B-GGUF
Deploy DeepSeek-R1-Distill-Llama-70B on Aquanode
Aquanode has no one-click deploy template for DeepSeek-R1-Distill-Llama-70B; you install the inference engine yourself with the commands below. Aquanode sells GPU pods billed per second, not a hosted inference API.
- Launch a bare GPU pod sized to the requirement above (7× RTX 3090 or larger).
- Open a terminal on the pod, or save one of the commands above as a startup script so it runs automatically the first time the pod boots.
- Run the command and connect to the resulting endpoint.
Launch a GPU pod