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QwQ 32B

Alibaba · Text generation · 32B · 131k context · Released 6 March 2025

Commercial use permitted Open weights Runs on CPU Apple Silicon

QwQ is a reasoning-first model: it generates detailed reasoning before its final answer, which makes it strong on maths, logic, and hard multi-step problems, and noticeably slower and more verbose than a standard model. At 32B it fits a single 24GB card at Q4, making frontier-style reasoning accessible locally.

Strengths

  • Strong reasoning on maths, logic, and multi-step problems
  • Competitive with much larger reasoning models on some tasks
  • Apache 2.0, and fits a single 24GB card at Q4

Weaknesses

  • Slow and verbose, since it thinks at length before answering
  • Overkill for simple tasks, where a standard model is faster and cheaper
  • Long reasoning traces eat into the context budget

Hardware requirements

QuantisationApprox. VRAMNotes
Q4_K_M~20GBFits a 24GB card, but leaves little room for long reasoning traces
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
MMLU-Pro69.07 Qwen model card March 2025
GPQA Diamond65.6 Qwen3 technical report (Table 13, QwQ-32B baseline) May 2025
AIME 202479.5 Qwen3 technical report (Table 13, QwQ-32B baseline) May 2025
AIME 202569.5 Qwen3 technical report (Table 13, QwQ-32B baseline) May 2025

How it compares

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

AIME 2025

higher is better

AIME 2024

higher is better

GPQA Diamond

higher is better

MMLU-Pro

higher is better

QwQ 32B: common questions

What hardware do I need to run QwQ 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 QwQ 32B free for commercial use?
Yes. QwQ 32B is licensed under Apache 2.0, which permits commercial use with no meaningful conditions.
Can I run QwQ 32B on Apple Silicon?
Yes. QwQ 32B has builds optimised for Apple Silicon, through MLX or GGUF on a Mac.
Does QwQ 32B run on CPU?
Yes, QwQ 32B can run on the CPU, though generation is slower than on a GPU.
What is QwQ 32B's context window?
QwQ 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:

  • Bartowski GGUF Q2-Q8 (imatrix)

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

  • Unsloth GGUF (Dynamic 2.0, imatrix)

    Dynamic and imatrix GGUF quants that often hold quality better than a plain quant at the same bit-width, especially at 4-bit and below.

  • MLX community MLX 4-bit and 8-bit

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

Recommended for

  • Local reasoning work on a 24GB card
  • Maths, logic, and hard problem solving
  • Cases where answer quality matters more than speed

Related models

Run it with

Related guides

Glossary

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