DeepSeek R1 Distill Llama 70B
A 70B reasoning model distilled from DeepSeek R1 onto Llama 3.3 70B, with weights under the MIT license.
- Params
- 70.6B
- Context
- 131K tokens
- Max output
- 33K
- Released
- License
- MIT
Estimated cost to self-host
DeepSeek R1 Distill Llama 70B needs about 46 GB of GPU memory at 4-bit with 32K context. The cheapest rental that fits is 4× RTX 5060 Ti at about $403 a month.
| Precision | Memory | Cheapest fit (32K context) | Cost /mo |
|---|---|---|---|
| 4-bit INT4 / FP4 | 46 GB |
|
$403 |
| 8-bit FP8 / INT8 | 83 GB |
|
$720 |
| 16-bit FP16 / BF16 | 154 GB |
|
$1,440 |
Estimates based on median on-demand rates for Nvidia GPUs. Memory is weights plus KV cache for one request, using FP8 KV cache where supported and FP16/BF16 otherwise. No guarantee of runtime support, usable performance, or that a matching quantized build exists. Pricing methodology.
Capabilities
What DeepSeek R1 Distill Llama 70B accepts and can do, as published by DeepSeek.
- Text Accepts and generates natural-language text.
- Reasoning Thinks before it answers, always on or as a switchable mode.
Common questions
What is DeepSeek R1 Distill Llama 70B good for?
Reasoning work on a dense 70B model. MIT-licensed weights, far lighter to self-host than the 671B R1 it was distilled from, on a single high-memory GPU.
When is DeepSeek R1 Distill Llama 70B not a good fit?
Tool use and image input: it has no function calling or structured output, and takes text only.
What is the cheapest way to run DeepSeek R1 Distill Llama 70B?
No provider we track hosts it; self-hosting on the cheapest rental that fits (4x RTX 5060 Ti, $0.56 per hour) costs about $403 a month.
Can I self-host DeepSeek R1 Distill Llama 70B?
Yes. The weights are MIT licensed. At 4-bit it needs about 46 GB of GPU memory, which starts at roughly $403 a month on the cheapest rental that fits.
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