DeepSeek R1 Distill Llama 70B

Overview

DeepSeek's R1 reasoning behavior distilled into Llama 3.3 70B. Brings frontier-class reasoning down to a single high-end GPU, but inherits both Llama and DeepSeek licenses.

When to pick this model

• You want R1-style reasoning on a single 80GB GPU or dual 48GB setup
• You need 128K context for long chain-of-thought work
• You're already deploying Llama 3.3 70B and want a reasoning upgrade
• You can comply with both Llama Community and DeepSeek license terms

VRAM requirements by quantization

VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.

Published benchmark scores

Scores published by the model author or aggregated from public leaderboards. Re-measured monthly by our editorial team.

Strengths

• Frontier-class reasoning on a single workstation-class GPU
• 128K context window
• Outperforms SFT-only 70B models on hard reasoning
• Strong drop-in for existing Llama 70B deployments

Limitations

• Dual licensing (Llama 3.3 Community + DeepSeek)
• Hugging Face gated access via the Llama base
• Trails full R1 671B on the hardest problems

Architecture & training

Architecture: Dense Llama 3.3 · SFT distilled from R1 traces

Training: Distilled from R1 671B.

Quick start

ollama run deepseek-r1:70b

Or use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.