# DeepSeek-R1-Distill-Llama-8B

> An 8B Llama model taught to reason by DeepSeek-R1 — small enough to run locally, strong on math.

- Maker: DeepSeek
- Line: DeepSeek R1 Distill
- Canonical page: https://ai-tldr.dev/models/deepseek-r1-distill-llama-8b/
- Updated: 2026-06-18

## In short

DeepSeek-R1-Distill-Llama-8B is one of six distilled models DeepSeek released alongside its flagship DeepSeek-R1 reasoning model on January 20, 2025. Rather than being a new architecture, it takes Meta's Llama-3.1-8B base model and fine-tunes it on roughly 800,000 reasoning examples generated by the much larger DeepSeek-R1. You can use it through Hugging Face, Ollama and Fireworks AI.

## Facts

| Fact | Value | Source |
| --- | --- | --- |
| Context window | 128K tokens (inherited from the Llama 3.1 base; commonly served at 131,072 tokens) (max output 32K tokens (recommended; reasoning models emit a long <think> trace before the answer)) | [1] |
| Input price | ~$0.20 per 1M tokens | [2] |
| Output price | ~$0.20 per 1M tokens | [2] |
| Licence | Llama 3.1 Community License (the weights are derived from Llama-3.1-8B-Base). The wider DeepSeek-R1 repository is MIT-licensed, but this specific model inherits Meta's Llama 3.1 license terms. · open weights | [1] |
| Released | 20 January 2025 | [3] |
| Parameters | 8 billion (8.03B) | [1] |
| Knowledge cutoff | December 2023 (from the Llama 3.1 base; distillation added reasoning traces, not new pretraining data) | [1] |

## Sources

1. [Hugging Face model card](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-8B)
2. [Fireworks AI model page & pricing](https://fireworks.ai/models/fireworks/deepseek-r1-distill-llama-8b)
3. [DeepSeek-R1 repository (license & distill details)](https://huggingface.co/deepseek-ai/DeepSeek-R1)

## Overview

DeepSeek-R1-Distill-Llama-8B is one of six distilled models DeepSeek released alongside its flagship DeepSeek-R1 reasoning model on January 20, 2025. Rather than being a new architecture, it takes Meta's Llama-3.1-8B base model and fine-tunes it on roughly 800,000 reasoning examples generated by the much larger DeepSeek-R1. The goal is to give a small, locally-runnable 8B model some of the step-by-step reasoning behavior of a frontier reasoning system.

Because it is built on Llama 3.1, the model is a dense decoder-only transformer with 8 billion parameters and a context window of up to 128K tokens. It is text-only and, like DeepSeek-R1 itself, it 'thinks out loud' — it produces a long reasoning trace wrapped in <think> tags before giving its final answer. DeepSeek recommends a sampling temperature around 0.6, putting all instructions in the user prompt rather than a system prompt, and forcing the response to begin with reasoning so the model does not skip its thinking step.

The headline result is that a model small enough to run on a single consumer GPU (or even a laptop with enough memory) can post strong math scores — 89.1% on MATH-500 and 50.4% on AIME 2024. It is open weights under the Llama 3.1 Community License and is downloadable from Hugging Face for self-hosting, with quantized GGUF builds widely available through Ollama and LM Studio. As an older January 2025 release it has since been overtaken by newer small reasoning models, but it remains a popular, easy-to-run baseline.

## Benchmarks

- MATH-500 (pass@1): 89.1% ([source](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-8B))
- AIME 2024 (pass@1): 50.4% ([source](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-8B))
- GPQA Diamond (pass@1): 49% ([source](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-8B))
- LiveCodeBench (pass@1): 39.6% ([source](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-8B))

## Pricing

- Input: ~$0.20 per 1M tokens per 1M tokens
- Output: ~$0.20 per 1M tokens per 1M tokens

DeepSeek did not publish a first-party hosted price for this open-weights model — it is intended for self-hosting. Third-party hosts vary widely (roughly $0.025–$0.20 per 1M tokens); the figure shown is Fireworks AI's serverless rate for this 8B model. Self-hosting the open weights has no per-token cost.

Source: https://fireworks.ai/models/fireworks/deepseek-r1-distill-llama-8b

## Strengths

- Strong math and reasoning for its size — 89.1% on MATH-500 and 50.4% on AIME 2024, far above a typical vanilla 8B chat model
- Small enough to run locally on a single consumer GPU, and available as quantized GGUF builds for Ollama and LM Studio
- Open weights you can download, fine-tune, and self-host with no API dependency
- Long 128K-token context inherited from the Llama 3.1 base
- Transparent chain-of-thought: emits a visible <think> reasoning trace before the final answer

## Best for

- Running a private, offline reasoning assistant on local hardware for math and logic problems
- Cost-sensitive math, science, and coding-help tasks where a small open model is enough
- Generating step-by-step reasoning traces for research, teaching, or building reasoning datasets
- A lightweight base to fine-tune for domain-specific reasoning on a budget
- Edge or on-device deployments where data cannot leave the machine

## How to access

- [Hugging Face](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-8B) — model id `deepseek-ai/DeepSeek-R1-Distill-Llama-8B`
- [Ollama](https://ollama.com/library/deepseek-r1:8b) — model id `deepseek-r1:8b`
- [Fireworks AI](https://fireworks.ai/models/fireworks/deepseek-r1-distill-llama-8b) — model id `accounts/fireworks/models/deepseek-r1-distill-llama-8b`
- [OpenRouter](https://openrouter.ai/deepseek/deepseek-r1-distill-llama-8b) — model id `deepseek/deepseek-r1-distill-llama-8b`

## DeepSeek R1 Distill versions

- [DeepSeek-R1-0528-Qwen3-8B](https://ai-tldr.dev/models/deepseek-r1-0528-qwen3-8b/) (29 May 2025)
- [DeepSeek-R1-Distill-Llama-70B](https://ai-tldr.dev/models/deepseek-r1-distill-llama-70b/) (20 January 2025)
- [DeepSeek-R1-Distill-Qwen-32B](https://ai-tldr.dev/models/deepseek-r1-distill-qwen-32b/) (20 January 2025)
- [DeepSeek-R1-Distill-Qwen-14B](https://ai-tldr.dev/models/deepseek-r1-distill-qwen-14b/) (20 January 2025)
- DeepSeek-R1-Distill-Llama-8B (20 January 2025) — this page
- [DeepSeek-R1-Distill-Qwen-7B](https://ai-tldr.dev/models/deepseek-r1-distill-qwen-7b/) (20 January 2025)
- [DeepSeek-R1-Distill-Qwen-1.5B](https://ai-tldr.dev/models/deepseek-r1-distill-qwen-1-5b/) (20 January 2025)

## FAQ

### Is DeepSeek-R1-Distill-Llama-8B the same as DeepSeek-R1?

No. DeepSeek-R1 is a very large Mixture-of-Experts reasoning model. This is a small 8B model built on Meta's Llama 3.1 that was fine-tuned on about 800,000 reasoning examples generated by DeepSeek-R1. It borrows R1's reasoning style at a fraction of the size, but it is a distinct, much smaller model.

### What license is DeepSeek-R1-Distill-Llama-8B under?

The model weights are under the Llama 3.1 Community License because they are derived from Meta's Llama-3.1-8B base. The broader DeepSeek-R1 repository is MIT-licensed, but for this specific model you should follow Meta's Llama 3.1 license terms, which permit commercial use subject to its conditions.

### Can I run it locally?

Yes. It is open weights and small enough to run on a single consumer GPU. Quantized GGUF builds are available through Ollama (as deepseek-r1:8b) and LM Studio, and the full weights can be downloaded from Hugging Face for self-hosting or fine-tuning.

### Why does it print <think> tags before answering?

It is a reasoning model, so it generates a step-by-step chain-of-thought inside <think>...</think> before the final answer. DeepSeek recommends a temperature around 0.6, putting instructions in the user prompt instead of a system prompt, and forcing the reply to start with reasoning so the model does not skip its thinking step.

## Links

- [Hugging Face model card](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-8B)
- [DeepSeek-R1 technical report (arXiv)](https://arxiv.org/abs/2501.12948)
- [DeepSeek-R1 repository (license & distill details)](https://huggingface.co/deepseek-ai/DeepSeek-R1)
- [Ollama deepseek-r1:8b](https://ollama.com/library/deepseek-r1:8b)
- [Fireworks AI model page & pricing](https://fireworks.ai/models/fireworks/deepseek-r1-distill-llama-8b)
- [OpenRouter model page](https://openrouter.ai/deepseek/deepseek-r1-distill-llama-8b)
- [Artificial Analysis profile](https://artificialanalysis.ai/models/deepseek-r1-distill-llama-8b)

Source: AI/TLDR LLM registry, https://ai-tldr.dev/models/deepseek-r1-distill-llama-8b/