Can RTX 3090 Ti run Qwen 2.5 Coder 7B?

Yes — runs locally

~60 tok/sec · Instant — feels like typing. No noticeable delay.

Your VRAM

24 GB

Model size

7.6B

Best quant

Q8_0

VRAM needed

8.0 GB

The verdict

The RTX 3090 Ti (24 GB VRAM) handles Qwen 2.5 Coder 7B comfortably using the Q8_0 quantization, which fits in 8.0 GB. Expected throughput is around 60 tokens/second, which feels Instant — feels like typing. No noticeable delay. in interactive use. Strong 7B code model rivaling larger coding models. Excellent for local development.

Setup tutorial: Qwen 2.5 Coder 7B on RTX 3090 Ti

AI-generated, GPU-specific. Verified commands for your exact hardware.

TL;DR

Run Qwen 2.5 Coder 7B on your NVIDIA GeForce RTX 3090 Ti with Q8_0 quantization for Grade S performance at ~123 tok/sec.

Prerequisites

Before starting, ensure you have at least 10GB of free disk space, a 64-bit version of Windows or Linux, NVIDIA driver version 470.82.01 or later, and CUDA 11.4 or later installed.

Expected performance

With the Q8_0 quantization, you can expect ~123 tok/sec performance, using approximately 8.0GB of VRAM. This leaves 16.0GB of VRAM for context, allowing for a practical context window of around 20,000 tokens.

1. Install runtimeOllama

pip install ollama
ollama init

2. Download the model

Download the Qwen 2.5 Coder 7B Q8_0 quantized model (7.5GB file) from Hugging Face.

ollama pull Qwen/Qwen2.5-Coder-7B-Instruct-GGUF:qwen2.5-coder-7b-instruct-q8_0.gguf

3. Run it

ollama run Qwen/Qwen2.5-Coder-7B-Instruct-GGUF --model qwen2.5-coder-7b-instruct-q8_0.gguf --interactive

4. Optimize for RTX 3090 Ti

For optimal performance on the NVIDIA GeForce RTX 3090 Ti with 24GB VRAM, set --n-gpu-layers to 70 to utilize most of the VRAM while leaving some headroom. Enable flash attention (--flash-attn) to speed up inference. Given the 24GB VRAM, you can achieve a practical context window of around 20,000 tokens without running out of memory.

Troubleshooting

Out of memory errors during inference

Reduce the number of layers on the GPU using --n-gpu-layers 50 or lower.

Slow inference speed

Ensure flash attention is enabled with --flash-attn. If not, add the flag and restart the model.

Model not loading

Verify the model file is correctly downloaded and not corrupted. Re-run the download command if necessary.

Alternative runtimes

For users preferring a different runtime, consider LM Studio for a more graphical interface, llama.cpp for lightweight and portable deployment, or Jan for advanced multi-GPU setups. Choose based on your specific needs and environment constraints.

Full Qwen 2.5 Coder 7B details →

Other models that run great on RTX 3090 Ti

FAQ (20)

What GPU do I need to run Qwen 2.5 Coder 7B?

To run Qwen 2.5 Coder 7B, you need a GPU with at least 4.9 GB of VRAM, but 8.0 GB is recommended for better performance, especially with higher quantization levels.

Is Qwen 2.5 Coder 7B good for coding?

Yes, Qwen 2.5 Coder 7B is specifically designed for coding tasks and performs well in generating and understanding code, making it an excellent choice for local development.

Qwen 2.5 Coder 7B vs Llama 3.1 8B?

Qwen 2.5 Coder 7B has 7.6 billion parameters and is optimized for coding, while Llama 3.1 8B has more parameters and is more general-purpose. Qwen 2.5 Coder 7B may outperform Llama 3.1 8B in specialized coding tasks.

Can I run Qwen 2.5 Coder 7B on a Mac?

Yes, you can run Qwen 2.5 Coder 7B on a Mac, provided your Mac has a compatible GPU with sufficient VRAM (at least 4.9 GB).

How much VRAM does Qwen 2.5 Coder 7B need?

Qwen 2.5 Coder 7B requires between 4.9 GB and 8.0 GB of VRAM, depending on the quantization level used.

Is Qwen 2.5 Coder 7B censored?

Qwen 2.5 Coder 7B is not censored; however, it adheres to ethical guidelines and community standards to ensure responsible use.

Is Qwen 2.5 Coder 7B commercial-use allowed?

Yes, Qwen 2.5 Coder 7B is licensed under the Apache-2.0 license, which allows for both commercial and non-commercial use.

Qwen 2.5 Coder 7B context length?

Qwen 2.5 Coder 7B supports a context length of up to 32,768 tokens, allowing for handling large codebases and complex programming tasks.

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