Llama 3.3 70B InstructvsDeepSeek R1 Distill 32B
Complete comparison between Llama 3.3 70B Instruct (70B parameters, Meta) and DeepSeek R1 Distill 32B (32B, DeepSeek). VRAM required by quantization, tokens per second measured on 4 reference GPUs, verdict by use case, license, installation commands. All figures are calculated from catalog data—no copy-pasting between pages.
In brief
| Feature | Llama 3.3 70B Instruct | DeepSeek R1 Distill 32B |
|---|---|---|
| Settings | 70B | 32B |
| Family | Llama | DeepSeek |
| Author | Meta | DeepSeek |
| Origin | US | CN |
| License | Llama 3.3 Community | MIT |
| Context | 128,000 tokens | 32 768 tokens |
| Output | December 2024 | January 2025 |
Memory footprint
Approximate VRAM required for inference with an average context window. The winner (in green) is the model that uses less — smaller models have the edge.
| Quantization | Llama 3.3 70B Instruct | DeepSeek R1 Distill 32B |
|---|---|---|
| Q4_K_M (lightweight) | 40 GB | 19 GB |
| Q5_K_M (balanced) | 48 GB | 23 GB |
| Q8 (near-lossless) | 75 GB | 35 GB |
| FP16 (max quality) | 140 GB | 64 GB |
| CPU-only RAM | 64 GB | 32 GB |
Estimated speed (tokens/second)
Estimates based on the best quantization that can fit on each GPU. Actual figures depend on the prompt, context, and engine (llama.cpp, vLLM, MLX). Methodology.
| Reference GPU | Llama 3.3 70B Instruct | DeepSeek R1 Distill 32B |
|---|---|---|
| RTX 4090 (24 GB) | ✗ too heavy | 30 tok/s · Q5_K_M |
| RTX 4080 (16 GB) | ✗ too heavy | ✗ too heavy |
| RTX 3060 12GB (12 GB) | ✗ too heavy | ✗ too heavy |
| Apple M4 Pro (48 GB) (36 GB) | ✗ too heavy | 12 tok/s · Q8 |
Verdict by use case
For each common use case, we indicate which of the two is better suited based on its tags, size, and specialization.
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Strengths and weaknesses
Llama 3.3 70B Instruct
Llama 3.1 405B quality at 1/6 the size. Weights under a community license, gated HF access.
- 405B quality at 1/6 the size
- 128k ctx
- Very strong at reasoning and coding
- HF gated (accept Meta's terms)
- Community license with a >700M MAU clause
- No vision
Installation
DeepSeek R1 Distill 32B
The best accessible open-weight reasoner.
- Explicit step-by-step reasoning
- Excellent at math and science
- 32k context
- MIT License
- Very verbose (thinking tokens)
- Slower to respond
Installation
Frequently asked questions
Which model runs best on RTX 4090 (24 GB): Llama 3.3 70B Instruct or DeepSeek R1 Distill 32B?
On a RTX 4090, Llama 3.3 70B Instruct does not fit in 24 GB—you need to offload part of it to the CPU, DeepSeek R1 Distill 32B in Q5_K_M (~30 tok/s). See the configurator to test your exact GPU.
Which uses the least VRAM?
In Q4_K_M, DeepSeek R1 Distill 32B fits in 19 GB versus 40 GB for its rival—a 21 GB gap, significant if you're targeting an RTX 3060 or a 4060 Ti 8 GB.
Can these models be used in commercial production?
Llama 3.3 70B Instruct is under Llama 3.3 Community — check the restrictions, especially the monthly user limits. DeepSeek R1 Distill 32B is also free under the MIT license. For SaaS, favor Apache 2.0 or MIT.
Which one should you choose in 2026?
Depends on your primary constraint. Smaller / faster: DeepSeek R1 Distill 32B (32B). More capable: Llama 3.3 70B Instruct (70B). If you're unsure, run the configurator with your GPU and use case — it will decide based on both.
Alternatives to consider
If neither works for you, here are some neighboring models you could explore.