Qwen2.5-Coder-32B-Instruct
Q4 GGUF download: Qwen/Qwen2.5-Coder-32B-Instruct-GGUF - 7.99 GB on disk, read 2026-09-27. VRAM figures above are estimates; this is the actual file size.
Intelligence benchmarks
Artificial Analysis indexes - compared with the best open and proprietary models
Intelligence
6.7
AA Index
Intelligence Index - Qwen2.5-Coder-32B-Instruct vs. the field
Best open-weight models (you can run locally) and leading proprietary models for context.
Benchmark data from Artificial Analysis · updated 2026-10-19.
Arena rating (LMArena)
Human preference wins from head-to-head chat battles
| Metric | Value |
|---|---|
| Overall arena rating | 1230.0 |
| Rank (overall) | #301 |
| Battle votes | 5,432 |
Overall rating published by LMArena on 2026-10-15 - a pairwise human-preference Elo across live chat battles; higher is better. Fetched 2026-10-19. Open-weights models compete head-to-head with proprietary ones here — no benchmark prompts, just what people pick.
Will it run on your hardware?
Pick your GPU memory - see which quantizations fit, and the cheapest card for the rest
Need an exact figure for your context length? Use the VRAM calculator.
Run it locally
Copy-paste - running in under a minute
vllm serve Qwen/Qwen2.5-Coder-32B-InstructNew to this? Start with Ollama · serve to many users with vLLM.
Deep dive
Notes, sources, and the full write-up
Qwen2.5-Coder-32B-Instruct
Qwen2.5-Coder-32B-Instruct is a 32.8B-parameter apache-2.0 model from Qwen. At Q4_K_M it needs roughly 19 GB of VRAM, placing it in the 12-24gb hardware tier.
Specifications
| Spec | Value |
|---|---|
| Parameters | 32.8B |
| Context length | 33K tokens |
| License | apache-2.0 |
| Modalities | text |
| Released | 2024-11-06 |
| Weights | Qwen/Qwen2.5-Coder-32B-Instruct |
VRAM requirements
| Quant | VRAM | Runs on |
|---|---|---|
| Q4_K_M | ~19 GB | RTX 3090, RTX 4090 |
| Q5_K_M | ~23 GB | RTX 3090, RTX 4090 |
| Q8_0 | ~35 GB | RTX 6000 Ada, dual RTX 3090 |
| FP16 | ~66 GB | A100 80GB, H100 |
VRAM is estimated from parameter count; MoE models still need all weights resident.
How to run
vLLM:
vllm serve Qwen/Qwen2.5-Coder-32B-InstructPopularity
Qwen2.5-Coder-32B-Instruct has 1,360,411 downloads in the last month on HuggingFace and 2,032 likes.
Frequently asked
Quick answers to common questions
How much VRAM does Qwen2.5-Coder-32B-Instruct need?
Qwen2.5-Coder-32B-Instruct with 32.8B parameters needs approximately 19 GB at Q4_K_M quantization. Use our VRAM calculator for an exact estimate.
Is Qwen2.5-Coder-32B-Instruct better than other Qwen models?
Qwen2.5-Coder-32B-Instruct has 32.8B parameters with 32,768 context - a strong choice for general use.
What license is Qwen2.5-Coder-32B-Instruct under?
Qwen2.5-Coder-32B-Instruct is released under the apache-2.0 license, making it suitable for most commercial and personal projects.
What hardware runs Qwen2.5-Coder-32B-Instruct well?
With 32.8B parameters, Qwen2.5-Coder-32B-Instruct requires adequate VRAM. High-end GPUs like the RTX 4090 (24GB), RTX 5090 (32GB), or Mac Studio with unified memory are good options. Check our hardware directory for specific recommendations.
What is the best quantization for Qwen2.5-Coder-32B-Instruct?
Q4_K_M is the recommended sweet spot - ~98% of FP16 quality at ~27% of the size. Q5_K_M (~23 GB) is an option if you have spare VRAM. Use our VRAM calculator to compare.
How long can Qwen2.5-Coder-32B-Instruct's context window handle?
Qwen2.5-Coder-32B-Instruct supports a 32,768-token context window - enough for most medium-length documents and conversations. Real-world usable context may vary by implementation.
What models compete with Qwen2.5-Coder-32B-Instruct?
Qwen2.5-Coder-32B-Instruct competes with other 16B–49B. Browse our model directory for comparisons, benchmarks, and community reviews to find the best fit.
Nearby options
Similar models and compatible hardware by spec