Llama 3.3 70B InstructvsQwen 3 32B
Complete comparison between Llama 3.3 70B Instruct (70B parameters, Meta) and Qwen 3 32B (32B, Alibaba). 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 | Qwen 3 32B |
|---|---|---|
| Settings | 70B | 32B |
| Family | Llama | Qwen |
| Author | Meta | Alibaba |
| Origin | US | CN |
| License | Llama 3.3 Community | Apache 2.0 |
| Context | 128,000 tokens | 131,072 tokens |
| Output | December 2024 | April 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 | Qwen 3 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 | Qwen 3 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
Qwen 3 32B
Dense 32B with thinking mode. MMLU-Pro 65.5, SuperGPQA 39.8.
- Solid reasoning in thinking mode
- 131k ctx
- Apache 2.0
- Worse than QwQ-32B dedicated to pure reasoning
Installation
Frequently asked questions
Which model runs best on RTX 4090 (24 GB): Llama 3.3 70B Instruct or Qwen 3 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, Qwen 3 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, Qwen 3 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. Qwen 3 32B is under Apache 2.0 — also free to use. For SaaS, prefer Apache 2.0 or MIT.
Which one should you choose in 2026?
Depends on your primary constraint. Smaller / faster: Qwen 3 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.