Can NVIDIA GeForce RTX 4090 run Qwen2.5 32B Instruct?

Yes, at Q5_K_M (23.6 GB) or lower. Higher quantizations like Q5_K_L (24.0 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 (20.0 GB) offers the best balance of quality and VRAM usage. Q4_K_L (20.4 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 9.6 GB.

Can I run Qwen2.5 32B Instruct on a Mac?

Qwen2.5 32B Instruct requires at least 9.6 GB at IQ2_XXS, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.

Can I run Qwen2.5 32B Instruct locally?

Yes — Qwen2.5 32B Instruct can run locally on consumer hardware. At Q4_K_M quantization it needs 20.0 GB of VRAM. 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 ~146 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~33 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.

What's the download size of Qwen2.5 32B Instruct?

At Q4_K_M, the download is about 19.20 GB. The full-precision Q8_0 version is 32.00 GB. The smallest option (IQ2_XXS) is 8.80 GB.

Alibaba·Qwen 2.5·Qwen2ForCausalLM

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.

2.8M downloads 337 likesSep 202433K context

Specifications

Publisher: Alibaba
Family: Qwen 2.5
Parameters: 32B
Architecture: Qwen2ForCausalLM
Context Length: 32,768 tokens
Vocabulary Size: 152,064
Release Date: 2024-09-25
License: Apache 2.0

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HuggingFace

Qwen/Qwen2.5-32B-Instruct

How Much VRAM Does Qwen2.5 32B Instruct Need?

Select a quantization to see compatible GPUs below.

Quantization	Bits	VRAM	+ Context	File Size	Quality
IQ2_XXS	2.20	9.6 GB	17.7 GB	8.80 GB	Importance-weighted 2-bit, extreme compression — significant quality loss
IQ2_XS	2.40	10.4 GB	18.5 GB	9.60 GB	Importance-weighted 2-bit, extra small
IQ2_S	2.50	10.8 GB	18.9 GB	10.00 GB	Importance-weighted 2-bit, small
IQ2_M	2.70	11.6 GB	19.7 GB	10.80 GB	Importance-weighted 2-bit, medium
IQ3_XS	3.30	14.0 GB	22.1 GB	13.20 GB	Importance-weighted 3-bit, extra small
Q2_K	3.40	14.4 GB	22.5 GB	13.60 GB	2-bit quantization with K-quant improvements
Q3_K_S	3.50	14.8 GB	22.9 GB	14.00 GB	3-bit small quantization
IQ3_M	3.60	15.2 GB	23.3 GB	14.40 GB	Importance-weighted 3-bit, medium
Q3_K_M	3.90	16.4 GB	24.5 GB	15.60 GB	3-bit medium quantization
Q4_0	4.00	16.8 GB	24.9 GB	16.00 GB	4-bit legacy quantization
Q3_K_L	4.10	17.2 GB	25.3 GB	16.40 GB	3-bit large quantization
IQ4_XS	4.30	18.0 GB	26.1 GB	17.20 GB	Importance-weighted 4-bit, compact
Q4_K_S	4.50	18.8 GB	26.9 GB	18.00 GB	4-bit small quantization
Q4_K_M	4.80	20.0 GB	28.1 GB	19.20 GB	4-bit medium quantization — most popular sweet spot
Q4_K_L	4.90	20.4 GB	28.5 GB	19.60 GB	4-bit large quantization
Q5_0	5.00	20.8 GB	28.9 GB	20.00 GB	5-bit legacy quantization
Q5_K_S	5.50	22.8 GB	30.9 GB	22.00 GB	5-bit small quantization
Q5_K_M	5.70	23.6 GB	31.7 GB	22.80 GB	5-bit medium quantization — good quality/size tradeoff
Q5_K_L	5.80	24.0 GB	32.1 GB	23.20 GB	5-bit large quantization
Q6_K	6.60	27.2 GB	35.3 GB	26.40 GB	6-bit quantization, very good quality
Q8_0	8.00	32.8 GB	40.9 GB	32.00 GB	8-bit quantization, near-lossless

Which GPUs Can Run Qwen2.5 32B Instruct?

Q4_K_M · 20.0 GB

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Qwen2.5 32B Instruct (Q4_K_M) requires 20.0 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 28.1 GB. 5 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.

Runs great

— Plenty of headroom

NVIDIA GeForce RTX 5090~58 tok/s

Decent

— Enough VRAM, may be tight

NVIDIA GeForce RTX 3090 Ti~33 tok/s NVIDIA GeForce RTX 4090~33 tok/s NVIDIA GeForce RTX 3090~30 tok/s AMD Radeon RX 7900 XTX~26 tok/s

Which Devices Can Run Qwen2.5 32B Instruct?

Q4_K_M · 20.0 GB

21 devices with unified memory can run Qwen2.5 32B Instruct, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 Pro (24 GB).

Related Models

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Frequently Asked Questions

How much VRAM does Qwen2.5 32B Instruct need?: Qwen2.5 32B Instruct requires 20.0 GB of VRAM at Q4_K_M, or 32.8 GB at Q8_0. Full 33K context adds up to 8.1 GB (28.1 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 32B × 4.8 bits ÷ 8 = 19.2 GB
KV Cache + Overhead ≈ 0.8 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 8.9 GB (at full 33K context)
VRAM usage by quantization
Q4_K_M
20.0 GB
Q4_K_M + full context
28.1 GB
Learn more about VRAM estimation →
Can NVIDIA GeForce RTX 4090 run Qwen2.5 32B Instruct?: Yes, at Q5_K_M (23.6 GB) or lower. Higher quantizations like Q5_K_L (24.0 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 (20.0 GB) offers the best balance of quality and VRAM usage. Q4_K_L (20.4 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 9.6 GB.
VRAM requirement by quantization
IQ2_XXS
9.6 GB~53%
Q2_K
14.4 GB~75%
Q3_K_L
17.2 GB~86%
Q4_K_M ★
20.0 GB~89%
Q5_0
20.8 GB~90%
Q8_0
32.8 GB~99%
★ Recommended — best balance of quality and VRAM usage.
Learn more about quantization →
Can I run Qwen2.5 32B Instruct on a Mac?: Qwen2.5 32B Instruct requires at least 9.6 GB at IQ2_XXS, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
Can I run Qwen2.5 32B Instruct locally?: Yes — Qwen2.5 32B Instruct can run locally on consumer hardware. At Q4_K_M quantization it needs 20.0 GB of VRAM. 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 ~146 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~33 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: AMD Instinct MI300X → 5300 ÷ 20.0 × 0.55 = ~146 tok/s
Estimated speed at Q4_K_M (20.0 GB)
AMD Instinct MI300X
~146 tok/s
NVIDIA GeForce RTX 4090
~33 tok/s
NVIDIA H100 SXM
~109 tok/s
AMD Instinct MI250X
~90 tok/s
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
Learn more about tok/s estimation →
What's the download size of Qwen2.5 32B Instruct?: At Q4_K_M, the download is about 19.20 GB. The full-precision Q8_0 version is 32.00 GB. The smallest option (IQ2_XXS) is 8.80 GB.