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

Yes, at Q5_0 (21.3 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 Coder 32B Instruct?

For Qwen2.5 Coder 32B Instruct, Q4_K_M (20.5 GB) offers the best balance of quality and VRAM usage. Q5_0 (21.3 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 14.8 GB.

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

Qwen2.5 Coder 32B Instruct requires at least 14.8 GB at Q2_K, 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 Coder 32B Instruct locally?

Yes — Qwen2.5 Coder 32B Instruct can run locally on consumer hardware. At Q4_K_M quantization it needs 20.5 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.

How fast is Qwen2.5 Coder 32B Instruct?

At Q4_K_M, Qwen2.5 Coder 32B Instruct can reach ~142 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~32 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.

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

At Q4_K_M, the download is about 19.66 GB. The full-precision Q8_0 version is 32.76 GB. The smallest option (Q2_K) is 13.92 GB.

Alibaba·Qwen 2.5·Qwen2ForCausalLM

Qwen2.5 Coder 32B Instruct — Hardware Requirements & GPU Compatibility

ChatCode

Qwen2.5 Coder 32B Instruct is a 32.8-billion parameter code-specialized model from Alibaba Cloud, instruction-tuned for programming assistance and code generation. It is trained on a large corpus of source code alongside natural language data, making it highly capable for tasks such as code completion, debugging, code explanation, and software engineering dialogue. The model supports a 128K token context window and delivers code generation quality competitive with the best open-weight coding models at any scale. It requires a GPU with at least 24GB of VRAM for quantized inference. Released under the Apache 2.0 license.

761.3K downloads 2.0K likesJan 202533K context

Specifications

Publisher: Alibaba
Family: Qwen 2.5
Parameters: 32.8B
Architecture: Qwen2ForCausalLM
Context Length: 32,768 tokens
Vocabulary Size: 152,064
Release Date: 2025-01-12
License: Apache 2.0

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HuggingFace

Qwen/Qwen2.5-Coder-32B-Instruct

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

Select a quantization to see compatible GPUs below.

Quantization	Bits	VRAM	+ Context	File Size	Quality
Q2_K	3.40	14.8 GB	22.8 GB	13.92 GB	2-bit quantization with K-quant improvements
Q3_K_M	3.90	16.8 GB	24.9 GB	15.97 GB	3-bit medium quantization
Q4_0	4.00	17.2 GB	25.3 GB	16.38 GB	4-bit legacy quantization
Q4_K_M	4.80	20.5 GB	28.6 GB	19.66 GB	4-bit medium quantization — most popular sweet spot
Q5_0	5.00	21.3 GB	29.4 GB	20.48 GB	5-bit legacy quantization
Q5_K_M	5.70	24.2 GB	32.2 GB	23.34 GB	5-bit medium quantization — good quality/size tradeoff
Q6_K	6.60	27.9 GB	35.9 GB	27.03 GB	6-bit quantization, very good quality
Q8_0	8.00	33.6 GB	41.6 GB	32.76 GB	8-bit quantization, near-lossless

Which GPUs Can Run Qwen2.5 Coder 32B Instruct?

Q4_K_M · 20.5 GB

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Qwen2.5 Coder 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. 5 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.

Runs great

— Plenty of headroom

NVIDIA GeForce RTX 5090~57 tok/s

Decent

— Enough VRAM, may be tight

NVIDIA GeForce RTX 3090 Ti~32 tok/s NVIDIA GeForce RTX 4090~32 tok/s NVIDIA GeForce RTX 3090~30 tok/s AMD Radeon RX 7900 XTX~26 tok/s

Which Devices Can Run Qwen2.5 Coder 32B Instruct?

Q4_K_M · 20.5 GB

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

Related Models

Derivatives (7)

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Frequently Asked Questions

How much VRAM does Qwen2.5 Coder 32B Instruct need?: Qwen2.5 Coder 32B Instruct requires 20.5 GB of VRAM at Q4_K_M, or 33.6 GB at Q8_0. 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)
KV Cache + Overhead ≈ 8.9 GB (at full 33K context)
VRAM usage by quantization
Q4_K_M
20.5 GB
Q4_K_M + full context
28.6 GB
Learn more about VRAM estimation →
Can NVIDIA GeForce RTX 4090 run Qwen2.5 Coder 32B Instruct?: Yes, at Q5_0 (21.3 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 Coder 32B Instruct?: For Qwen2.5 Coder 32B Instruct, Q4_K_M (20.5 GB) offers the best balance of quality and VRAM usage. Q5_0 (21.3 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 14.8 GB.
VRAM requirement by quantization
Q2_K
14.8 GB~75%
Q4_0
17.2 GB~85%
Q4_K_M ★
20.5 GB~89%
Q5_0
21.3 GB~90%
Q5_K_M
24.2 GB~92%
Q8_0
33.6 GB~99%
★ Recommended — best balance of quality and VRAM usage.
Learn more about quantization →
Can I run Qwen2.5 Coder 32B Instruct on a Mac?: Qwen2.5 Coder 32B Instruct requires at least 14.8 GB at Q2_K, 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 Coder 32B Instruct locally?: Yes — Qwen2.5 Coder 32B Instruct can run locally on consumer hardware. At Q4_K_M quantization it needs 20.5 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Qwen2.5 Coder 32B Instruct?: At Q4_K_M, Qwen2.5 Coder 32B Instruct can reach ~142 tok/s on AMD Instinct MI300X. 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: AMD Instinct MI300X → 5300 ÷ 20.5 × 0.55 = ~142 tok/s
Estimated speed at Q4_K_M (20.5 GB)
AMD Instinct MI300X
~142 tok/s
NVIDIA GeForce RTX 4090
~32 tok/s
NVIDIA H100 SXM
~106 tok/s
AMD Instinct MI250X
~88 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 Coder 32B Instruct?: At Q4_K_M, the download is about 19.66 GB. The full-precision Q8_0 version is 32.76 GB. The smallest option (Q2_K) is 13.92 GB.