QwQ 32B
By Alibaba · China
Updated 2026-07-13
Overview
Alibaba's dedicated 32B reasoner, trained with reinforcement learning rather than distillation. Hits 79.5 on AIME24 and 90.6 on MATH-500 — a direct Apache-licensed alternative to DeepSeek R1.
When to pick this model
- You need a frontier-class reasoner you can run on a single 48GB GPU
- You're solving math, logic, or formal problems where chain-of-thought matters
- You want an Apache-licensed alternative to DeepSeek R1
- You need 131K context for long reasoning traces
VRAM requirements by quantization
| Quantization | VRAM required |
|---|---|
| Q4_K_M (recommended) | 19 GB |
| Q5_K_M | 23 GB |
| Q8_0 | 35 GB |
| FP16 (no quantization) | 64 GB |
VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.
In practice, QwQ 32B wants a 24 GB card at Q4_K_M (19 GB). Stepping up to Q8_0 nearly doubles the footprint to 35 GB, and unquantized FP16 weights take 64 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.
Without a GPU, QwQ 32B needs roughly 32 GB of system RAM to run on CPU via llama.cpp or Ollama — workable for background jobs, but far slower than GPU inference. Throughput estimates from our compatibility engine: around 3 tokens/sec on entry-level GPUs, on the order of 12 tokens/sec on a mid-range card, and up to 30 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.
What hardware do you need
The table below matches QwQ 32B to common GPU memory tiers, using the highest-fidelity quantization that fully fits each card class. Spilling layers to system RAM works but costs most of the speed, so size your card to the quantization you actually want to run.
| GPU memory | Example cards | Best fit for QwQ 32B |
|---|---|---|
| 8 GB | RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB | Does not fit — needs 19 GB at Q4_K_M |
| 12 GB | RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop | Does not fit — needs 19 GB at Q4_K_M |
| 16 GB | RTX 5080, RTX 4080 Super, Radeon RX 9070 XT | Does not fit — needs 19 GB at Q4_K_M |
| 24 GB | RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop | Q5_K_M (23 GB used) |
| 32 GB | RTX 5090 | Q5_K_M (23 GB used) |
Which GPU should you buy to run QwQ 32B?
To run QwQ 32B locally at Q4, you need ~19 GB of VRAM. The best value for this is a RTX 4090 (24 GB VRAM).
As an Amazon Associate, BestLLMfor earns from qualifying purchases, at no extra cost to you. It does not influence our independent rankings.
Published benchmark scores
| Benchmark | Score |
|---|---|
| AIME 2024 | 79.5 |
| MATH-500 | 90.6 |
Scores published by the model author or aggregated from public leaderboards. Re-measured monthly by our editorial team.
To put QwQ 32B in context: its AIME 2024 score of 79.5 ranks #3 of the 8 catalog models with a published AIME 2024 result (catalog median 71.7); its MATH-500 score of 90.6 ranks #6 of the 8 catalog models with a published MATH-500 result (catalog median 93.3). Scores are author-published and measured under different harnesses, so treat gaps of a few points as noise rather than a verdict.
Strengths
- Direct competitor to DeepSeek R1 at a fraction of the size
- 131K context for long thinking traces
- Trained with RL, not just distilled
- Apache 2.0
Limitations
- Very verbose — token costs add up fast
- Requires YaRN for context beyond 8K
- Overkill for non-reasoning chat workloads
Typical workloads
In our catalog grid, QwQ 32B is filed under Advanced Math, Science, Step-by-Step Reasoning — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: multi-step reasoning and math-flavoured tasks.
The 128k-token context window is large enough to hold entire codebases' worth of files or long reports in a single prompt, which is what makes local RAG and document analysis practical. The Apache 2.0 license is permissive, so shipping it inside a commercial product raises no special legal questions.
Architecture & training
Architecture: Dense · 64 layers · GQA (40Q/8KV) · RoPE · SwiGLU · trained with outcome-based RL
Training: RL on reasoning (not a simple distillation).
The best Apache-licensed reasoner you can run on a single GPU.
Quick start
ollama run qwq:32bOr use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.
Similar models worth comparing
Frequently asked questions
How much VRAM does QwQ 32B need?
At the recommended Q4_K_M quantization, QwQ 32B needs about 19 GB of VRAM. Q8_0 takes 35 GB, and unquantized FP16 weights take 64 GB.
Can QwQ 32B run without a GPU?
Yes — with roughly 32 GB of system RAM it runs CPU-only through llama.cpp or Ollama. Expect a fraction of GPU speed, which is fine for background or batch jobs but slow for interactive chat.
What context window does QwQ 32B support?
QwQ 32B supports a 128k-token context window (131,072 tokens).
Can I use QwQ 32B commercially?
Yes. QwQ 32B is released under Apache 2.0, a permissive open-source license that allows commercial use, modification and redistribution.
How fast is QwQ 32B on consumer hardware?
Our compatibility engine estimates on the order of 12 tokens/sec on a mid-range GPU and up to 30 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.
Which quantization of QwQ 32B should I download first?
Start with Q4_K_M (19 GB) — the standard size/quality sweet spot. Step up to Q5_K_M or Q8_0 only if you have VRAM headroom. On a 24 GB card you can run up to Q5_K_M.