DeepSeek-R1-Distill-Llama-70B

The strongest R1 distill: DeepSeek-R1's reasoning distilled onto Llama 3.3 70B, reaching o1-mini-class results you can self-host — with Llama's community license inherited.

This is the largest and most capable of DeepSeek's R1 distillations — the full R1 model's reasoning compressed into Meta's Llama 3.3 70B. At 70 billion parameters it's a serious model that needs multiple GPUs to run, but the payoff is reasoning quality that lands in the same neighborhood as OpenAI's o1-mini on math and code benchmarks (around 70% on AIME 2024, 94.5% on MATH-500, 57.5 on LiveCodeBench), while being fully open-weightA model where the trained weights are freely downloadable — you can run it yourself without contacting the creator. Llama, Mistral, Qwen, and Gemma are open-weight. Open-weight does not mean open-source: the training data and code often stay private. The license still governs what you can do with the weights, including whether you can use them commercially. and self-hostable.

For a business that wants top-tier reasoning entirely on its own infrastructure — no API, no data leaving the building — this is one of the strongest options available, and it was for a while the headline demonstration that open distills could rival proprietary reasoning models. It's a reasoning specialist: best on problems with a clear logical structure, less suited to general-purpose chat.

The catch, as with its 8B sibling, is the two-layer license: DeepSeek's MIT distillationA technique for training a smaller model (the "student") to imitate a larger model (the "teacher"). The result is a compact model that retains much of the larger model's capability at a fraction of the compute cost. Distilled models are common in production because they're cheaper to run than the full-size originals while performing nearly as well on most tasks. sits on top of a Llama base, so Meta's Llama 3.3 Community License governs the weightsThe numerical values inside a trained model that encode everything it has learned. A model is, functionally, a giant list of weights — tens of billions of numbers for a mid-sized model, hundreds of billions for a frontier model. "Open-weight" means those numbers are published. "Downloading the weights" means getting the actual file you'd need to run the model yourself..

• Self-hostedRunning a model on hardware you control — your own servers, your own cloud instance, or your own laptop — rather than paying to access it through someone else's API. Self-hosting gives you full control over data and predictable costs, but requires the hardware and operational effort to run the model. Only possible with open-weight models., top-tier reasoning where data must stay entirely in-house.
• Hard math, logic, and code problems that benefit from o1-mini-class chain-of-thought.
• Organizations with the GPUThe specialized chip that runs most AI models. Originally designed for 3D graphics, GPUs turned out to be excellent at the math AI requires. Nvidia dominates the AI GPU market; common datacenter models include the H100, H200, and B200. Running an AI model without a GPU is possible but painfully slow for anything but the smallest models. capacity to run a 70B model and a need for a proprietary-grade open reasoner.
• Research and evaluation of open reasoning models at the high end.

• Anyone without multi-GPUThe specialized chip that runs most AI models. Originally designed for 3D graphics, GPUs turned out to be excellent at the math AI requires. Nvidia dominates the AI GPU market; common datacenter models include the H100, H200, and B200. Running an AI model without a GPU is possible but painfully slow for anything but the smallest models. capacity — for laptop-class reasoning use DeepSeek-R1-Distill-Qwen-32B or DeepSeek-R1-Distill-Llama-8B.
• General chat or open-ended writing — it's a reasoning specialist.
• Products near the 700M-monthly-user mark, which trip Llama's license carve-out.
• Teams that want a clean, unrestricted license — the Qwen-based distills and DeepSeek's own MIT flagships avoid Llama's terms.

Like the 8B Llama distill, this is two-layered. DeepSeek released its distillationA technique for training a smaller model (the "student") to imitate a larger model (the "teacher"). The result is a compact model that retains much of the larger model's capability at a fraction of the compute cost. Distilled models are common in production because they're cheaper to run than the full-size originals while performing nearly as well on most tasks. weightsThe numerical values inside a trained model that encode everything it has learned. A model is, functionally, a giant list of weights — tens of billions of numbers for a mid-sized model, hundreds of billions for a frontier model. "Open-weight" means those numbers are published. "Downloading the weights" means getting the actual file you'd need to run the model yourself. under MIT, but the base is Meta's Llama 3.3 70B Instruct, so Meta's Llama 3.3 Community License governs the underlying model and travels with the weights. You can use, modify, and redistribute commercially, but you inherit Llama's terms: display "Built with Llama," include the license, observe the acceptable-use restrictions, and — the one clause worth flagging — secure a separate Meta license only if your product exceeds 700 million monthly active users. That threshold is irrelevant for virtually all businesses, but it's why commercial use is "conditional." If you need the strongest open reasoning without Llama's strings, compare against the Apache-licensed DeepSeek-R1-Distill-Qwen-32B and DeepSeek's MIT-licensed DeepSeek-R1 itself.

Against DeepSeek-R1-Distill-Qwen-32B, the 70B is somewhat stronger but much heavier (multi-GPUThe specialized chip that runs most AI models. Originally designed for 3D graphics, GPUs turned out to be excellent at the math AI requires. Nvidia dominates the AI GPU market; common datacenter models include the H100, H200, and B200. Running an AI model without a GPU is possible but painfully slow for anything but the smallest models. versus single-GPU), and the Qwen distill's Apache-over-MIT license is cleaner — so the 32B is often the more practical choice unless you specifically need the 70B's edge. Against DeepSeek-R1-Distill-Llama-8B, this is the high end of the same family: far more capable, far less portable. Against its parent DeepSeek-R1, the 70B distill is the self-hostable stand-in — not as strong as the full 671B R1, but runnable on a single high-end server rather than a cluster.