Qwen2.5 32B Instruct — Hardware Requirements & GPU Compatibility
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Qwen2.5 32B Instruct is a 32-billion parameter instruction-tuned model from Alibaba Cloud's Qwen 2.5 family. It occupies a practical sweet spot between the 14B and 72B variants, offering strong reasoning and multilingual capabilities while remaining feasible to run on a single high-end consumer GPU with 24GB or more of VRAM at reduced precision. The model supports a 128K token context window and is optimized for conversational use, instruction following, and structured output generation. It is a popular choice for local inference when the 72B model is too demanding but users need more capability than the 14B variant. Released under the Apache 2.0 license.
Runs well on a 24 GB GPU
Runs from 16 GB (IQ2_M · 16K)Best on 24 GB (Q4_K_M · ~32 tok/s · 8K)Macs from 18 GB (IQ2_XS · ~10 tok/s)
Score 80.4#88 of 106 open#25 of 36 that fit 24 GBApache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
llmrun Composite Score →
80.4
- 90% range 75.0–85.9
- #88 of 106 open models
- #25 of 36 that fit 24 GB
Run Qwen2.5 32B Instruct
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 qwen2.5:32bQ4_K_M · needs ~24.3 GB · 16K context
Download: Qwen2.5-32B-Instruct-Q4_K_M.gguf19.85 GBbartowski
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) (7 GB headroom) · Q4_K_M
- Generation speed
- ~57 tok/s
- generation speed
- Cost per 1M output tokens
- $2.43
- per 1M output tokens
How Much VRAM Does Qwen2.5 32B Instruct Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 14.8 GB | 22.8 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 32K context · ~44 tok/s | |
| 15.2 GB | 23.2 GB | 3-bit small — 3-bit small quantization | Fits: 32K context · ~43 tok/s | |
| 16.8 GB | 24.9 GB | 3-bit medium — 3-bit medium quantization | Fits: 16K context · ~39 tok/s | |
| 17.2 GB | 25.3 GB | 4-bit · legacy — 4-bit legacy quantization | Fits: 16K context · ~38 tok/s | |
| 20.5 GB | 28.6 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 8K context · ~32 tok/s | |
| 24.2 GB | 32.2 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Does not fit: needs 27.9 GB | |
| 27.9 GB | 35.9 GB | 6-bit, very good — 6-bit quantization, very good quality | Does not fit: needs 31.6 GB | |
| 33.6 GB | 41.7 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Does not fit: needs 37.4 GB |
Selected Q4_K_M
4-bit medium quantization24.3GBat 16K context
- Weights
- 19.7 GB
- KV cache
- 4.3 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · fits up to 8K context · ~32 tok/s
≥50 tok/s needs ≥1.57 TB/s · RTX 5090 ~57
Which Hardware Can Run Qwen2.5 32B Instruct?
Q4_K_M · 20.5 GBQwen2.5 32B Instruct (Q4_K_M) requires 20.5 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 33K context window can add up to 8.1 GB, bringing total usage to 28.6 GB. 7 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 122 devices with unified memory can run Qwen2.5 32B Instruct, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Studio M1 Max (32 GB).
20 GB
Q4_K_M · best fit IQ3_M (19.3 GB)
1 GPU
RX 7900 XT~31Simulate output on RX 7900 XT32–40 GB
fits · 7.7 GB free · up to 32K 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 Qwen2.5 32B Instruct?
The llmrun Score merges 3 public benchmark boards into a single figure, where 100 represents the average for open models.
Score vs VRAM in the 24 GB tier
- Qwen2.5 32B Instruct
- Other models
- Best Score for its VRAM
#25 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
Here are all 4 boards that have results for this model, sorted by their weight in the llmrun Score; badges show who ran the test.
- GPQA Diamond
Epoch AI · Sep 2024Independent
Independent46.1%
#65/100 open
- AIME 2024/2025
Epoch AI · Sep 2024Independent
Independent7.4%
#67/88 open
- MMLU-Pro
TIGER-LabIndependent
Independent69.2%
#39/146 open
- MATH Level 5
Epoch AI · Sep 2024Independent
Independent56.1%
#13/39 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 Qwen2.5 32B Instruct
Qwen 2.5 family · 44 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
- 41.6 higher Score
- 5.2 GB less VRAM
- 4 tok/s faster
Score 122.0 · 17.9 GB · ~38 tok/s
Much faster, similar quality
Qwen3.6 35B A3B
Alibaba
- 26.4 higher Score
- 1 GB less VRAM
- 117 tok/s faster
Score 106.8 · 22 GB · ~151 tok/s
Fits in less VRAM
GPT OSS 20B
OpenAI
- 16 higher Score
- 9.9 GB less VRAM
- 156 tok/s faster
Score 96.4 · 13.1 GB · ~190 tok/s
Other vendor
Gemma 4 31B IT
Google
- 26.3 higher Score
- 1.3 GB less VRAM
- 2 tok/s slower
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 8BAlibaba | 93.9 | 7.6 GB | ~119 | similar score |
| Phi 4Microsoft | 83.9 | 12.5 GB | ~69 | similar score |
| Gemma 3 12B ITGoogle | 74.2 | 10.9 GB | ~82 | similar score |
| Gemma 2 9B ITGoogle | 68.0 | 7.3 GB | ~107 | similar score |
Specifications
- Publisher
- Alibaba
- Family
- Qwen 2.5
- Parameters
- 32.8B
- Architecture
- Qwen2ForCausalLM
- Context Length
- 32,768 tokens
- Vocabulary Size
- 152,064
- Release Date
- 2024-09-17
- License
- Apache 2.0
Frequently Asked Questions
How much VRAM does Qwen2.5 32B Instruct need?
Qwen2.5 32B Instruct requires 20.5 GB of VRAM at Q4_K_M, or 66.4 GB at BF16. Full 33K context adds up to 8.1 GB (28.6 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 32.8B × 4.8 bits ÷ 8 = 19.7 GB
KV Cache + Overhead ≈ 0.8 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 ≈ 8.9 GB (at full 33K context)
VRAM usage by quantization
Can NVIDIA GeForce RTX 4090 run Qwen2.5 32B Instruct?
Yes, at Q5_K_S (23.4 GB) or lower. Higher quantizations like Q5_K_M (24.2 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
What's the best quantization for Qwen2.5 32B Instruct?
For Qwen2.5 32B Instruct, Q4_K_M (20.5 GB) offers the best balance of quality and VRAM usage. Q4_K_L (20.9 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 9.8 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Qwen2.5 32B Instruct on a Mac?
Yes — MacBook Pro 14" M3 Pro (18 GB) and 108 other Macs can run Qwen2.5 32B Instruct, the smallest at IQ2_XS. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 20.5 GB of usable unified memory (RAM minus macOS overhead), from Mac Mini M5 Pro (24 GB).
Can I run Qwen2.5 32B Instruct locally?
Yes — Qwen2.5 32B Instruct can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Qwen2.5 32B Instruct?
At Q4_K_M, Qwen2.5 32B Instruct can reach ~234 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 ÷ 20.5 × 0.65 = ~254 tok/s
Estimated speed at Q4_K_M (20.5 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Qwen2.5 32B Instruct?
At Q4_K_M, the download is about 19.66 GB. The full-precision BF16 version is 65.53 GB. The smallest option (IQ2_XXS) is 9.01 GB.
Which GPUs can run Qwen2.5 32B Instruct?
7 consumer GPUs can run Qwen2.5 32B Instruct 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 Qwen2.5 32B Instruct?
122 devices with unified memory can run Qwen2.5 32B Instruct at Q4_K_M (20.5 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.