Qwen 2.5 Coder 32BvsLlama 3.3 70B Instruct
Complete comparison between Qwen 2.5 Coder 32B (32B parameters, Alibaba) and Llama 3.3 70B Instruct (70B, Meta). VRAM required by quantization, tokens per second measured on 4 reference GPUs, use-case verdict, license, installation commands. All figures are calculated from catalog data—no copy-pasting between pages.
In brief
| Feature | Qwen 2.5 Coder 32B | Llama 3.3 70B Instruct |
|---|---|---|
| Settings | 32B | 70B |
| Family | Qwen | Llama |
| Author | Alibaba | Meta |
| Origin | CN | US |
| License | Apache 2.0 | Llama 3.3 Community |
| Context | 131,072 tokens | 128,000 tokens |
| Output | November 2024 | December 2024 |
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 | Qwen 2.5 Coder 32B | Llama 3.3 70B Instruct |
|---|---|---|
| Q4_K_M (lightweight) | 19 GB | 40 GB |
| Q5_K_M (balanced) | 23 GB | 48 GB |
| Q8 (near-lossless) | 35 GB | 75 GB |
| FP16 (max quality) | 64 GB | 140 GB |
| CPU-only RAM | 32 GB | 64 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 | Qwen 2.5 Coder 32B | Llama 3.3 70B Instruct |
|---|---|---|
| RTX 4090 (24 GB) | 30 tok/s · Q5_K_M | ✗ too heavy |
| RTX 4080 (16 GB) | ✗ too heavy | ✗ too heavy |
| RTX 3060 12GB (12 GB) | ✗ too heavy | ✗ too heavy |
| Apple M4 Pro (48 GB) (36 GB) | 12 tok/s · Q8 | ✗ too heavy |
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
Qwen 2.5 Coder 32B
The open-weight code reference from late 2024, now surpassed by the Qwen3-Coder / Devstral generation.
- Best open-weight coding LLM
- Very strong on HumanEval (2024 benchmark)
- 128k context
- Requires 20+ GB of VRAM
- Worse at general chat
Installation
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
Frequently asked questions
Which model runs best on RTX 4090 (24 GB): Qwen 2.5 Coder 32B or Llama 3.3 70B Instruct?
On an RTX 4090, Qwen 2.5 Coder 32B runs at Q5_K_M (~30 tok/s), Llama 3.3 70B Instruct does not fit on 24 GB either. See the configurator to test your exact GPU.
Which uses the least VRAM?
In Q4_K_M, Qwen 2.5 Coder 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.
Qwen 2.5 Coder 32B or Llama 3.3 70B Instruct for coding?
Qwen 2.5 Coder 32B is the best fit (code-specialized, top HumanEval scores). For an IDE copilot, pair it with Continue or Aider.
Can these models be used in commercial production?
Qwen 2.5 Coder 32B is under Apache 2.0 — free for commercial use. Llama 3.3 70B Instruct is under Llama 3.3 Community—also check the terms. For SaaS, prefer Apache 2.0 or MIT.
Which one should you choose in 2026?
Depends on your primary constraint. Smaller / faster: Qwen 2.5 Coder 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.