What GPU do I need to run deepseek-ai/DeepSeek-R1-Distill-Llama-70B?
A 70.6B model tuned to reason step by step before answering. 70.6B parameters, published in BF16. View on Hugging Face
DeepSeek-R1-Distill-Llama-70B is published by deepseek-ai on Hugging Face, with 94,032 downloads and 798 likes to date. It's a LlamaForCausalLM model built for text-generation, published natively in BF16.
What DeepSeek-R1-Distill-Llama-70B is
DeepSeek-R1-Distill-Llama-70B is a 70.6B-parameter language model published by DeepSeek on Hugging Face. It works through a problem step by step before answering, rather than responding directly. It is released under MIT.
License note: permissive: allows commercial use, modification and redistribution. Facts in this section are sourced from DeepSeek-R1-Distill-Llama-70B's Hugging Face model card, not benchmarked by Aquanode.
What it's used for
- Multi-step reasoning and math
- Code generation
- Agentic tool use
VRAM required & cheapest live GPU fit
Required VRAM = weight size at each precision, plus a fixed overhead for KV-cache, activations, and fragmentation. Full formula and assumptions: methodology.
| Precision | Weight size | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| BF16 | 131.4 GB | 157.7 GB | RTX A5000 | 7 | $1.23/hr |
| FP8 (quantized) | 65.7 GB | 78.8 GB | RTX PRO 6000 | 1 | $1.38/hr |
| cheaper alt. | RTX 5060 Ti | 5 | $0.550/hr | ||
| INT4 (quantized) | 32.9 GB | 39.4 GB | RTX A6000 | 1 | $0.363/hr |
| cheaper alt. | RTX 5060 Ti | 3 | $0.330/hr |
A GPU is only matched to a row if its hardware supports that precision, and the primary recommendation is always a single-GPU fit when one exists.
INT4 requires a quantized checkpoint actually published for this model, check its Hugging Face page before relying on this row.
Cheapest way to run DeepSeek-R1-Distill-Llama-70B at its published (BF16) precision: 7× RTX A5000, at $0.176/hr per GPU ($1.23/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
DeepSeek-R1-Distill-Llama-70B: common questions
Can DeepSeek-R1-Distill-Llama-70B run on a single GPU?
No. At BF16 it needs 157.7 GB of VRAM, more than a single 32 GB desktop card holds. The cheapest capable card in the live feed is a 24.0 GB RTX A5000, and it takes 7 of them.
How many GPUs do I need to run DeepSeek-R1-Distill-Llama-70B?
7 at BF16. It needs 157.7 GB of VRAM and the cheapest capable live offer is a 24.0 GB RTX A5000, so 7 of them come to $1.23/hr in total.
Does quantizing DeepSeek-R1-Distill-Llama-70B lower the GPU bill?
Yes. At BF16 the cheapest live fit is 7 RTX A5000 cards at $1.23/hr. At INT4 (quantized) it drops to one RTX A6000 at $0.363/hr, provided a quantized checkpoint exists for it.
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 A5000 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 podWeight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More DeepSeek R1 models
- DeepSeek-R1-Distill-Llama-70B-FP8-dynamic (70.6B, F8_E4M3)
- DeepSeek-R1-Distill-Llama-8B (8.0B, BF16)
- DeepSeek-R1-Distill-Llama-8B-abliterated (8.0B, BF16)
- DeepSeek-R1-Distill-Qwen-32B-FP8-dynamic (32.8B, F8_E4M3)
- DeepSeek-R1-Distill-Qwen-32B (32.8B, BF16)
Related reading: RTX A5000 pricing and specs, The NVIDIA Inception program, explained, Google Colab alternatives for dedicated GPU access, Free GPU credits for students and researchers, and RunPod volume disk vs. network volume, compared.
