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DeepSeek R1 Distill Llama 70B

DeepSeek R1 Distill Llama 70B needs roughly 50.8 GB VRAM at Q4_K_M quantization (159.8 GB at FP16). 40 GPUs we track can run it fully in VRAM at 8k context.

40 GPUs run this natively · 35 with CPU offload

DeepSeek70B params125k contextMITCommercial use ok

DeepSeek R1 Distill Llama 70B is a 70B parameter dense model developed by DeepSeek. 70B distillation of DeepSeek-R1's reasoning capabilities into Llama-3.3 architecture.

To run DeepSeek R1 Distill Llama 70B locally: Q4_K_M ~35-40GB — same requirements as Llama-3.3-70B. Best way to get R1-style reasoning locally.

MMLU-Pro 70.0%, GPQA 65.2%, Math 94.5% — inherits R1's reasoning strength at practical size.

VRAM at each quantization

DeepSeek R1 Distill Llama 70B natively supports a longer context window, but the table below is capped at 8k for comparability — KV cache grows linearly with context length.

QuantWeightsKV cacheTotal
FP32280.0 GB2.68 GB316.6 GB
BF16140.0 GB2.68 GB159.8 GB
FP16140.0 GB2.68 GB159.8 GB
Q8_074.4 GB2.68 GB86.3 GB
Q6_K57.5 GB2.68 GB67.4 GB
Q5_K_M49.8 GB2.68 GB58.8 GB
Q4_K_Mrec42.6 GB2.68 GB50.8 GB
Q3_K_M33.7 GB2.68 GB40.7 GB
Q2_K26.7 GB2.68 GB32.9 GB
NVFP4cuda35.0 GB2.68 GB42.2 GB

Shown at 8k context with FP16 KV cache. NVFP4 needs a CUDA GPU to run. Toggle TurboQuant in the calculator to view compressed KV cache numbers.

Benchmarks

MMLU-Pro
70
GPQA Diamond
65.2
MATH
94.5

GPUs that run DeepSeek R1 Distill Llama 70B natively (40)

Plus 35 GPUs that run it with CPU offload (slower)

Notes

Reasoning model — outputs long chains-of-thought before answering.

Hugging Face ↗Ollama ↗Released 2025-01-20

Compare DeepSeek R1 Distill Llama 70B with other models

How to run DeepSeek R1 Distill Llama 70B locally

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Q4_K_M needs 50.8 GB — needs a workstation or datacenter GPU (48–80 GB).

Ollama

ollama run deepseek-r1:70b

llama.cpp

./llama-cli -m deepseek-r1-distill-llama-70b.Q4_K_M.gguf -c 8192 -ngl 99

LM Studio: Search for 'DeepSeek R1 Distill Llama 70B' in LM Studio. Requires 40+ GB of VRAM at Q4 -- plan for dual GPUs or a workstation card.

Why this quantization? This 70B dense model distills DeepSeek R1's reasoning into the Llama architecture. Q4_K_M at roughly 40 GB for weights is the minimum viable quantization for fitting on prosumer hardware. The model's extraordinary MATH score (94.5) and strong GPQA (65.2) are well-preserved at Q4. Grouped-query attention with 8 KV heads keeps the KV cache manageable alongside the heavy weight footprint.

Who is DeepSeek R1 Distill Llama 70B for?

Users with 48 GB+ VRAM setups who want the absolute best open-weight reasoning model that runs locally without a server rack. This distill outperforms the 32B variant on most benchmarks, making it the choice when you have the hardware to support it and need maximum reasoning capability.

Best for

  • Olympiad-level mathematics and formal proofs (MATH: 94.5)
  • Advanced scientific reasoning and research (GPQA: 65.2)
  • Complex code generation with multi-step reasoning chains
  • Situations where the 32B distill's accuracy isn't quite sufficient

Not ideal for

  • Users with a single 24 GB GPU -- the 32B distill is your model
  • Latency-sensitive applications -- 70B parameters means slower inference than the 32B variant
  • General-purpose chat where reasoning overhead is unnecessary

Continue reading

vram-guides9 min
Best LLMs for 48 GB VRAM (2026)
vram-guides10 min
DeepSeek Family Guide: R1, V3 & Distilled Models
hardware11 min
Multi-GPU Setups for Local LLMs: The Complete Guide
hardware10 min
Best GPUs $1000+ for LLMs (2026)
hardware11 min
Apple Silicon for LLMs: M1-M5 Complete Guide

Frequently asked questions

What are the VRAM requirements for DeepSeek R1 Distill Llama 70B?
DeepSeek R1 Distill Llama 70B requires approximately 50.8 GB of VRAM at Q4_K_M quantization, 86.3 GB at Q8, and 159.8 GB at FP16. These numbers assume 8k context window; VRAM scales linearly with context length due to the KV cache.
How many parameters does DeepSeek R1 Distill Llama 70B have?
DeepSeek R1 Distill Llama 70B has 70 billion parameters.
Is DeepSeek R1 Distill Llama 70B good at reasoning and math?
Yes. With a MATH score of 94.5 and MMLU-Pro of 70, DeepSeek R1 Distill Llama 70B handles complex multi-step reasoning, analytical tasks, and problem-solving well.
Can DeepSeek R1 Distill Llama 70B run on a 16 GB GPU?
No. At Q4_K_M, DeepSeek R1 Distill Llama 70B needs 50.8 GB of VRAM — more than 16 GB. You will need a multi-GPU server.
Can DeepSeek R1 Distill Llama 70B run on a 24 GB GPU?
No. Even at Q4_K_M, DeepSeek R1 Distill Llama 70B needs 50.8 GB. Consider a multi-GPU server with 80 GB+ total VRAM.
What is the smallest quantization for DeepSeek R1 Distill Llama 70B that fits in 24 GB of VRAM?
DeepSeek R1 Distill Llama 70B cannot fit in 24 GB of VRAM at any standard quantization level. The minimum needed is 32.9 GB at Q2_K.
What GPU do I need to run DeepSeek R1 Distill Llama 70B locally?
You need a multi-GPU server. At Q4_K_M, DeepSeek R1 Distill Llama 70B needs 50.8 GB VRAM, more than any single consumer GPU. Consider 2–4× H100 or A100 GPUs.