Qwen3 32B
Q4 GGUF download: Qwen/Qwen3-32B-GGUF - 19.76 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
7.3
AA Index
Intelligence Index - Qwen3 32B 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.
Hosted API cost & speed
What it costs to rent this model instead of running it locally
| Metric | Value |
|---|---|
| Input (per 1M tokens) | $0.15 |
| Output (per 1M tokens) | $0.60 |
| Median speed (hosted API) | 64 tok/s |
Median across hosted providers, measured by Artificial Analysis - updated 2026-10-19. Local llama.cpp speed depends on your hardware — see the hardware pages for estimates.
Arena rating (LMArena)
Human preference wins from head-to-head chat battles
| Metric | Value |
|---|---|
| Overall arena rating | 1340.1 |
| Rank (overall) | #213 |
| Battle votes | 3,926 |
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.
Standard benchmarks
Performance across standard evaluations
| Benchmark | Score |
|---|---|
| MMLU | 83.4 |
| HumanEval | 82.9 |
| MT-Bench | 8.7 |
| GSM8K | 94 |
Other scores are carried over from the site's initial dataset - no source on record, not re-verified.
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
ollama run qwen3:32bvllm serve Qwen/Qwen3-32BNew to this? Start with Ollama · serve to many users with vLLM.
Deep dive
Notes, sources, and the full write-up
Qwen3 32B
Qwen3 32B is Alibaba's flagship dense model in the Qwen3 lineup. At 32.8 billion parameters, it achieves 83.4 MMLU while fitting on a single RTX 4090 at Q4_K_M - making it the best quality-per-card model available.
Why Qwen3 32B is special
- 83.4 MMLU - highest of any 32B model, competitive with 70B models
- Fits on RTX 4090 - 19 GB at Q4_K_M
- Thinking mode - with adjustable thinking budget (0-8192 tokens)
- 4.8M+ monthly downloads - extremely popular
- 132K max context with YaRN extension
- 92 token support for batch inference
Benchmarks
| Benchmark | Qwen3 32B | R1 32B | Qwen3 14B | Llama 3.3 70B |
|---|---|---|---|---|
| MMLU | 83.4 | 72.6 | 77.0 | 86.0 |
| HumanEval | 82.9 | 57.2 | 80.5 | 81.7 |
| GSM8K | 94.0 | 94.3 | 88.0 | 95.1 |
| MT-Bench | 8.7 | 8.4 | 8.5 | 8.8 |
VRAM math
| Quant | VRAM | Recommended Hardware |
|---|---|---|
| Q4_K_M | ~19 GB | RTX 4090 (24GB) |
| Q5_K_M | ~23 GB | RTX 4090 (tight) |
| Q8_0 | ~34 GB | RTX 5090 |
| FP16 | ~64 GB | Dual RTX 5090 |
How to run
ollama run qwen3:32bFrom the community
What users are saying
“With temperature, it feels like a shot in the dark for Qwen to "pick" all the right numbers for the math. Its confidence in those number tokens has to overcome a ton of randomness. At zero or extremely low temperature, getting/not getting stuck in a grammer loop also feels like luck.”
“It seems like this model is the only one lacking a base variant. That's a shame, I could really use it.”
“Congratulations on the recent release of your new LLM! I’m very interested in trying it out and was wondering if you could kindly consider providing a GPTQ version of the model. It would be incredibly helpful for those of us looking to run it efficiently on local hardware.”
Frequently asked
Quick answers to common questions
How much VRAM does Qwen3 32B need?
Qwen3 32B with 32B parameters needs approximately 19 GB at Q4_K_M quantization. Use our VRAM calculator for an exact estimate.
Is Qwen3 32B better than other Qwen models?
Qwen3 32B scores 83.4 on MMLU and 82.9 on HumanEval. It has 32B parameters with 32,768 context - a strong choice for general-purpose, coding, agents.
What license is Qwen3 32B under?
Qwen3 32B is released under the Apache 2.0 license, making it suitable for most commercial and personal projects.
What hardware runs Qwen3 32B well?
With 32B parameters, Qwen3 32B 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 Qwen3 32B?
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 Qwen3 32B's context window handle?
Qwen3 32B 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 Qwen3 32B?
Qwen3 32B competes with other 16B–48B. Browse our model directory for comparisons, benchmarks, and community reviews to find the best fit.
Compare & pair with
Similar models and recommended hardware
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Nearby options
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