Qwen3 32B — Hardware Requirements & GPU Compatibility
ChatFunctions
Qwen3 32B is the flagship dense model in Alibaba Cloud's Qwen 3 series, with 32 billion parameters. It is instruction-tuned for chat and delivers strong performance across reasoning, coding, mathematics, and multilingual tasks. Qwen3 32B supports a hybrid thinking mode that allows the model to engage in extended chain-of-thought reasoning or respond quickly depending on the task, giving users flexibility between depth and speed. The model requires a GPU with at least 24GB of VRAM for quantized inference, placing it within reach of high-end consumer cards like the RTX 4090. It represents a significant generational improvement over Qwen 2.5 in both instruction following and knowledge breadth. Released under the Apache 2.0 license.
Decent on a 24 GB GPU
Runs from 12 GB (IQ2_XXS · 16K)Best on 24 GB (Q4_K_M · ~32 tok/s · 16K)Macs from 16 GB (IQ2_XXS · ~15 tok/s)
Score 98.6#62 of 106 open#11 of 36 that fit 24 GBApache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
llmrun Composite Score →
98.6
- 90% range 95.6–101.6
- #62 of 106 open models
- #11 of 36 that fit 24 GB
Run Qwen3 32B
One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.
Recommended
ollama run qwen3:32bQ4_K_M fits fully on the RTX 4090 · 1.4 GB free · 16K context
Download: Qwen3-32B-Q4_K_M.gguf19.76 GBQwen
Ways to runOther ways
each opens step-by-stepRun in cloud
Your own private copy · Q4_K_M · 32 GB GPU
- The easiest way to try this model
- Running in a few minutes, easy setup
- Chat right here or connect any app
$5+ bonus for new users
Runs on RTX 5090 (32 GB) (9 GB headroom) · Q4_K_M
- Generation speed
- ~57 tok/s
- generation speed
- Cost per 1M output tokens
- $2.41
- per 1M output tokens
How Much VRAM Does Qwen3 32B Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 14.6 GB | 19.6 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 40K context · ~45 tok/s | |
| 15.0 GB | 20 GB | 3-bit small — 3-bit small quantization | Fits: 40K context · ~44 tok/s | |
| 16.6 GB | 21.6 GB | 3-bit medium — 3-bit medium quantization | Fits: 40K context · ~39 tok/s | |
| 17.0 GB | 22.1 GB | 4-bit · legacy — 4-bit legacy quantization | Fits: 40K context · ~39 tok/s | |
| 20.3 GB | 25.3 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 16K context · ~32 tok/s | |
| 24.0 GB | 29 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 2K context · ~27 tok/s | |
| 27.7 GB | 32.7 GB | 6-bit, very good — 6-bit quantization, very good quality | Does not fit: needs 30 GB | |
| 33.4 GB | 38.4 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Does not fit: needs 35.8 GB |
Selected Q4_K_M
4-bit medium quantization22.6GBat 16K context
- Weights
- 19.7 GB
- KV cache
- 2.7 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 1.4 GB free · ~32 tok/s
≥50 tok/s needs ≥1.56 TB/s · RTX 5090 ~57
Which Hardware Can Run Qwen3 32B?
Q4_K_M · 20.3 GBQwen3 32B (Q4_K_M) requires 20.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 27+ GB is recommended. Using the full 41K context window can add up to 6.4 GB, bringing total usage to 26.7 GB. 7 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 122 devices with unified memory can run Qwen3 32B, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Studio M1 Max (32 GB).
20 GB
Q4_K_M · best fit Q3_K_L (19.8 GB)
1 GPU
RX 7900 XT~28Simulate output on RX 7900 XT32–40 GB
fits · 9.4 GB free · up to 40K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~57Simulate output on RTX 5090M5 Max36 GB~16Simulate output on Mac Studio M5 Max (36 GB)M532 GB~5Simulate output on MacBook Air 13" M5 (32 GB)Orin~7Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~4Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
How good is Qwen3 32B?
The llmrun Score merges 8 public benchmark boards into a single figure, where 100 represents the average for open models.
Score vs VRAM in the 24 GB tier
- Qwen3 32B
- Other models
- Best Score for its VRAM
#11 of 36 that fit 24 GB
Each dot is a model at the best quant that fits a 24 GB GPU. The line steps up to the best Score available at each amount of VRAM; a dot below it is beaten by a model that needs no more memory.
Full 24 GB ranking →Benchmarks
The 6 boards that weigh most in the llmrun Score, of 9 with a result for this model; badges show who ran the test.
- GPQA Diamond
Epoch AI · Apr 2025Independent
Independent65.7%
#39/100 open
- LMArena Text
LMArena · Oct 2026Arena
Arena1346.8
#80/188 open
- AIME 2024/2025
Epoch AI · Apr 2025Independent
Independent66.9%
#33/88 open
- Aider Polyglot
Epoch AI · Apr 2025Independent
Independent40.0%
#11/18 open
- SciCode
Epoch AI · Apr 2025Lab-reported
Lab-reported35.4%
#54/72 open
- CritPt
Epoch AI · Apr 2025Lab-reported
Lab-reported0.3%
#50/84 open
In the 24 GB class
36 models fit 24 GBSame Score, only models that fit 24 GB at their best quant: the ranking a 24 GB owner actually chooses from.
- #1
Qwen3.8 27BAlibaba
122.0
~38 tok/s · 17.9 GB
- #2
Qwen3.6 27BAlibaba
111.2
~38 tok/s · 17.9 GB
- #3
Qwen3.6 35B A3BAlibaba
106.8
~151 tok/s · 22.0 GB
- #4
Gemma 4 31B ITGoogle
106.7
~32 tok/s · 21.7 GB
- #5
Gemma 4 26B A4B ITGoogle
105.8
~171 tok/s · 16.5 GB
- #6
Qwen3.5 35B A3BAlibaba
105.8
~151 tok/s · 22.0 GB
Alternatives to Qwen3 32B
Qwen 3 family · 79 models →Official models with an llmrun Score, each answering one question; numbers are at the best quant that fits a 24 GB GPU.
Best alternative at this size
Qwen3.8 27B
Alibaba
- 23.4 higher Score
- 4.8 GB less VRAM
- 5 tok/s faster
Score 122.0 · 17.9 GB · ~38 tok/s
Much faster, similar quality
Qwen3.6 35B A3B
Alibaba
- 8.2 higher Score
- 0.6 GB less VRAM
- 119 tok/s faster
Score 106.8 · 22 GB · ~151 tok/s
Fits in less VRAM
GPT OSS 20B
OpenAI
- 2.2 lower Score
- 9.5 GB less VRAM
- 157 tok/s faster
Score 96.4 · 13.1 GB · ~190 tok/s
Other vendor
Gemma 4 31B IT
Google
- 8.1 higher Score
- 0.9 GB less VRAM
Score 106.7 · 21.7 GB · ~32 tok/s
Similar models
10| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Qwen3.6 27BAlibaba | 111.2 | 17.9 GB | ~38 | similar score |
| Gemma 4 26B A4B ITGoogle | 105.8 | 16.5 GB | ~171 | similar score |
| Qwen3.5 35B A3BAlibaba | 105.8 | 22 GB | ~151 | similar score |
| Qwen3.5 27BAlibaba | 103.8 | 17.9 GB | ~38 | similar score |
| Seed OSS 36B InstructByteDance-Seed | 102.5 | 22.8 GB | ~31 | similar score |
| Qwen3 30B A3B Thinking 2507Alibaba | 100.7 | 19.4 GB | ~173 | similar score |
| Qwen3.5 9BAlibaba | 99.4 | 6.6 GB | ~103 | similar score |
| Qwen3.5 4BAlibaba | 98.5 | 3.4 GB | ~201 | similar score |
| Phi 4Microsoft | 83.9 | 12.5 GB | ~69 | similar score |
| DeepSeek R1 Distill Qwen 32BDeepSeek | 97.2 | 23 GB | ~34 | same size |
Specifications
- Publisher
- Alibaba
- Family
- Qwen 3
- Parameters
- 32.8B
- Architecture
- Qwen3ForCausalLM
- Context Length
- 40,960 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2025-04-27
- License
- Apache 2.0
Frequently Asked Questions
How much VRAM does Qwen3 32B need?
Qwen3 32B requires 20.3 GB of VRAM at Q4_K_M, or 66.2 GB at BF16. Full 41K context adds up to 6.4 GB (26.7 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 32.8B × 4.8 bits ÷ 8 = 19.7 GB
KV Cache + Overhead ≈ 0.6 GB (at 2K context + ~0.3 GB framework)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
KV Cache + Overhead ≈ 7 GB (at full 41K context)
VRAM usage by quantization
Can NVIDIA GeForce RTX 4090 run Qwen3 32B?
Yes, at Q5_K_M (24.0 GB) or lower. Higher quantizations like Q5_K_L (24.4 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
What's the best quantization for Qwen3 32B?
For Qwen3 32B, Q4_K_M (20.3 GB) offers the best balance of quality and VRAM usage. Q4_K_L (20.7 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 9.7 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Qwen3 32B on a Mac?
Yes — Mac Mini M2 Pro (16 GB) and 133 other Macs can run Qwen3 32B, the smallest at IQ2_XXS. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 20.3 GB of usable unified memory (RAM minus macOS overhead), from Mac Mini M5 Pro (24 GB).
Can I run Qwen3 32B locally?
Yes — Qwen3 32B can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Qwen3 32B?
At Q4_K_M, Qwen3 32B can reach ~237 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~32 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 20.3 × 0.65 = ~256 tok/s
Estimated speed at Q4_K_M (20.3 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Qwen3 32B?
At Q4_K_M, the download is about 19.66 GB. The full-precision BF16 version is 65.52 GB. The smallest option (IQ2_XXS) is 9.01 GB.
Which GPUs can run Qwen3 32B?
7 consumer GPUs can run Qwen3 32B at Q4_K_M. Top options include NVIDIA GeForce RTX 5090, AMD Radeon RX 7900 XTX, NVIDIA GeForce RTX 3090. 1 GPU has plenty of headroom for comfortable inference.
Which devices can run Qwen3 32B?
122 devices with unified memory can run Qwen3 32B at Q4_K_M (20.3 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.