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DeepSeek-R1 Distill Llama 70B: Self-Hosting & Deployment Guide

DeepSeek-R1 Distill Llama 70B is a reasoning model distilled from DeepSeek R1 onto a 70B Llama backbone.

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What is DeepSeek-R1 Distill Llama 70B?

DeepSeek-R1 Distill Llama 70B is a reasoning model distilled from DeepSeek R1 onto a 70B Llama backbone, published by DeepSeek with open weights at deepseek-ai/DeepSeek-R1-Distill-Llama-70B. LLM API does not route this model today, so there is no endpoint or code sample for it here. What this page does give you is the self-hosting picture: published weight sizes, the VRAM each quantisation needs and the commands to serve it yourself. Every figure is read from the public repository and refreshed weekly.

How to run DeepSeek-R1 Distill Llama 70B yourself

Open weights (mit), 70.6B parameters. Below: how much memory the weights need at each quantization, which hardware fits them, and the commands to serve it.

DeepSeek-R1 Distill Llama 70B VRAM requirements

Precision / quantizationWeightsFigureSource
BF16 (full precision)141.1 GBEstimate70.6B params × 2 bytes
FP872.7 GBFile sizeRedHatAI/DeepSeek-R1-Distill-Llama-70B-FP8-dynamic
AWQ 4-bit39.8 GBFile sizecasperhansen/deepseek-r1-distill-llama-70b-awq
MLX 4-bit39.7 GBFile sizemlx-community/DeepSeek-R1-Distill-Llama-70B-4bit
GGUF Q8_075 GBFile sizeunsloth/DeepSeek-R1-Distill-Llama-70B-GGUF
GGUF Q4_K_M42.5 GBFile sizeunsloth/DeepSeek-R1-Distill-Llama-70B-GGUF
GGUF Q2_K26.4 GBFile sizeunsloth/DeepSeek-R1-Distill-Llama-70B-GGUF

File size = actual download size on Hugging Face. Estimate = parameter count × bytes per weight. Weights only — the KV cache for long contexts needs extra memory; the fit tables below add 15% headroom as a rule of thumb.

GPU requirements: deploy DeepSeek-R1 Distill Llama 70B on your hardware

HardwareBF16 (full precision)FP8AWQ 4-bitMLX 4-bitGGUF Q8_0GGUF Q4_K_MGGUF Q2_K
NVIDIA H200141 GB2×1×1×1×1×1×1×
NVIDIA H10080 GB3×2×1×1×2×1×1×
NVIDIA A100 80GB80 GB3×2×1×1×2×1×1×
NVIDIA L40S48 GB4×2×1×1×2×2×1×

GPUs needed per variant.

HardwareGGUF Q8_0GGUF Q4_K_MGGUF Q2_K
RTX 509032 GB3×2×1×
RTX 409024 GB4×3×2×
RTX 309024 GB4×3×2×

GGUF via llama.cpp; more cards or CPU offload for larger files.

HardwareGGUF Q8_0GGUF Q4_K_MGGUF Q2_KMLX 4-bit
Mac, 512 GBM3 UltraFitsFitsFitsFits
Mac, 192 GBM2 UltraFitsFitsFitsFits
Mac, 128 GBM4 Max / M3 MaxFitsFitsFitsFits
Mac, 64 GBM4 Pro / Max——FitsFits

Assumes ~75% of unified memory is usable by the GPU.

HardwareBF16 (full precision)FP8AWQ 4-bitMLX 4-bitGGUF Q8_0GGUF Q4_K_MGGUF Q2_K
AWS p5.48xlarge8× H100 · 640 GBFitsFitsFitsFitsFitsFitsFits
AWS p4de.24xlarge8× A100 80GB · 640 GBFitsFitsFitsFitsFitsFitsFits
AWS g6e.12xlarge4× L40S · 192 GBFitsFitsFitsFitsFitsFitsFits
Google Cloud a3-highgpu-8g8× H100 · 640 GBFitsFitsFitsFitsFitsFitsFits
Azure ND H100 v58× H100 · 640 GBFitsFitsFitsFitsFitsFitsFits

Whole-instance GPU memory; public instance specs.

Serve it yourself

pip install -U vllm
vllm serve deepseek-ai/DeepSeek-R1-Distill-Llama-70B
pip install 'sglang[all]'
python -m sglang.launch_server --model-path deepseek-ai/DeepSeek-R1-Distill-Llama-70B
llama-server -hf unsloth/DeepSeek-R1-Distill-Llama-70B-GGUF:Q4_K_M \
  --ctx-size 32768 --n-gpu-layers 99
pip install -U mlx-lm
mlx_lm.generate --model mlx-community/DeepSeek-R1-Distill-Llama-70B-4bit --prompt "Hello"

Commands use the official repositories above; vLLM and SGLang expose an OpenAI-compatible endpoint.

Not on LLM.API yet

We do not route this model through our API at the moment. Browse the models you can call today — most workloads have a close match already live.

Browse available models

Not available on LLM API yet

We do not route this model through our API at the moment, so there is no endpoint or code snippet for it yet. Browse the models you can call today — most workloads have a close match already live.

Browse available models

Why run DeepSeek-R1 Distill Llama 70B on LLM.API?

  • Unified AI Routing

    Reach DeepSeek-R1 Distill Llama 70B and sibling models through one OpenAI-compatible endpoint.

  • Cost Control

    Production: Compare provider price points and keep spend visible as you scale DeepSeek-R1 Distill Llama 70B.

  • Reliability Layer

    Retry and route across configured providers when a single upstream blips.

  • Observability

    Trace prompts, tokens, and errors for DeepSeek-R1 Distill Llama 70B alongside the rest of your stack.

  • Drop-in SDKs

    Keep using familiar OpenAI client patterns with base URL https://api.llmapi.ai/v1.

  • Model Breadth

    Swap DeepSeek-R1 Distill Llama 70B for chat, media, or embedding alternatives without rewriting auth.

When to Use — When NOT to Use

Use it if...

  • You want to run DeepSeek-R1 Distill Llama 70B on your own hardware and keep every prompt inside your network
  • The weights are published openly, so you can pin one checkpoint and keep it reproducible
  • You want to fine-tune or quantise the model rather than accept a hosted configuration
  • Your workload rewards deliberate step-by-step reasoning over fast replies

Avoid if...

  • LLM API does not route this model today, so there is no endpoint here to call
  • You have no GPU capacity: the VRAM figures below are the floor, not a suggestion
  • You want zero operations — serving, scaling and upgrades are yours to run
  • You need image, audio or video input; this checkpoint is text in, text out

COMMUNITY

What developers say about DeepSeek-R1 Distill Llama 70B

LMArena—Speed—Sources: LMArena, Artificial Analysis

Summarised from public developer discussions and write-ups. Opinions are the sources’, not LLM.API’s.

  • Developers at RunLocalAI report this model is the most important practical release of 2025 for workstation owners, offering frontier-adjacent reasoning that can run on consumer hardware like the RTX 4090.
  • Technical reviews indicate the model incurs a 'verbose chain-of-thought tax,' resulting in 2-3x higher token costs and slower speeds compared to the base Llama 3.3 70B model.
  • Developers note that while it wins decisively on reasoning tasks, its generalist quality for simple chat or everyday tasks is slightly lower than the base Llama 3.3 70B.

SOURCES

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