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Qwen3 32B

Alibaba · Text generation · 32B · 131k context · Released 29 April 2025

Commercial use permitted Open weights Runs on CPU Apple Silicon

Qwen3 32B is the most capable dense model in the family, and for many people the sweet spot between quality and hardware: a 24GB card runs it at Q4, and it competes with much larger models on reasoning when thinking mode is enabled. The trade-off is that a 24GB card leaves little room for long context.

Strengths

  • Strong reasoning, competitive with larger models when thinking is enabled
  • Fits a single 24GB card at Q4, or 32GB comfortably at higher quality
  • Apache 2.0, so no commercial-use conditions

Weaknesses

  • At Q4 on a 24GB card there is little headroom for long context
  • Thinking mode adds noticeable latency and token use
  • Slower generation than the 30B-A3B mixture-of-experts model at similar memory

Hardware requirements

QuantisationApprox. VRAMNotes
Q4_K_M~20GBFits a 24GB card, but leaves little room for context
Q5_K_M~23GBBetter quality, needs headroom beyond 24GB
Q8_0~35GBNear-lossless, needs 40GB or more
FP16~65GBFull precision, server or multi-GPU territory

Also runs on CPU (slower). Optimised builds available for Apple Silicon.

What you'd need to run this

Roughly what a machine to run this would need, at up to three levels of quality. Memory is the deciding factor.

Minimum to run it

Q4_K_M · ~20GB needed

One 24GB GPU

NVIDIA Tesla P40

or a Mac or mini-PC with unified memory, if you prefer no discrete GPU, Mac mini M5 Pro .

32–64GB of system RAM alongside the card.

around £700–£1,100

What else 24GB runs →

For good quality

Q5_K_M · ~23GB needed

One 32GB GPU

NVIDIA GeForce RTX 5090

or a Mac or mini-PC with unified memory, if you prefer no discrete GPU, Mac mini M4 Pro .

64GB of system RAM alongside the card.

around £4,000–£8,000

See the Single-card flagship build What else 32GB runs →

Best quality

FP16 · ~65GB needed

96GB of unified memory

AMD Ryzen AI Max+ 395 (Strix Halo)

or an 80GB-class data-centre card, which is usually rented by the hour, NVIDIA A100 80GB .

Unified memory is shared with the model, so it is already counted above.

around £1,100–£2,000

See the Quiet large-model box build What else 96GB runs →

Licence

Apache 2.0 — read the licence

Benchmarks

BenchmarkScoreSourceAs of
GPQA Diamond68.4 Qwen3 technical report (thinking mode) May 2025
AIME 202481.4 Qwen3 technical report (thinking mode) May 2025
AIME 202572.9 Qwen3 technical report (thinking mode) May 2025
LiveCodeBench v565.7 Qwen3 technical report (thinking mode) May 2025
Aider Polyglot40.0% Aider polyglot leaderboard (independent) August 2026
Artificial Analysis Intelligence Index v4.1.111 Artificial Analysis (independent) August 2026

How it compares

How this model’s reported scores sit against other models we cover, on the same benchmarks. This model is highlighted.

Aider Polyglot

higher is better

AIME 2025

higher is better

Artificial Analysis Intelligence Index v4.1.1

higher is better

LiveCodeBench v5

higher is better

AIME 2024

higher is better

GPQA Diamond

higher is better

Qwen3 32B: common questions

What hardware do I need to run Qwen3 32B?
At its most compressed (Q4_K_M) it needs roughly 20GB of VRAM, and about 24GB for good quality. VRAM figures are approximate and depend on context length and settings.
Is Qwen3 32B free for commercial use?
Yes. Qwen3 32B is licensed under Apache 2.0, which permits commercial use with no meaningful conditions.
Can I run Qwen3 32B on Apple Silicon?
Yes. Qwen3 32B has builds optimised for Apple Silicon, through MLX or GGUF on a Mac.
Does Qwen3 32B run on CPU?
Yes, Qwen3 32B can run on the CPU, though generation is slower than on a GPU.
What is Qwen3 32B's context window?
Qwen3 32B has a context window of 131,072 tokens, about 131k.

Availability

Where to get quantised weights

Some of the best quantised weights are made by the community, not the model’s authors. Look this model up on these providers:

  • Unsloth GGUF (Dynamic 2.0, imatrix)

    Dynamic quants keep the reasoning strong at Q4 on a 24GB card.

  • Bartowski GGUF Q2-Q8 (imatrix)

    A wide, reliable range of imatrix GGUF quants, typically Q2 through Q8.

  • MLX community MLX 4-bit and 8-bit

    MLX quants for Apple Silicon, usually 4-bit and 8-bit.

Recommended for

  • A single-card 24GB flagship for general use and reasoning
  • Mac users with 32GB or more unified memory
  • Work where you want frontier-adjacent reasoning without a server

Related models

Run it with

Related guides

Glossary

Our coverage

Catalogue entry last verified 30 July 2026. Specifications change; verify anything you are about to spend money on.