Can RTX 4090 run Qwen3 8B Base?

Yes — runs locally

~96 tok/sec · Instant — feels like typing. No noticeable delay.

Your VRAM

24 GB

Model size

Best quant

Q4_K_M

VRAM needed

5.3 GB

The verdict

The RTX 4090 (24 GB VRAM) handles Qwen3 8B Base comfortably using the Q4_K_M quantization, which fits in 5.3 GB. Expected throughput is around 96 tokens/second, which feels Instant — feels like typing. No noticeable delay. in interactive use. Official Qwen3 8B foundation model — pretrained only, no RLHF or refusal training. The 'naturally uncensored' option: no abliteration needed because alignment was never applied. Apache 2.0.

Setup tutorial: Qwen3 8B Base on RTX 4090

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

TL;DR

Run Qwen3 8B Base on an NVIDIA GeForce RTX 4090 with Q4_K_M quantization for Grade S performance at ~185 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 525.60 or later, and CUDA 11.8 or later installed.

Expected performance

With the Q4_K_M quantization, you can expect the model to run at ~185 tok/sec, utilizing 5.3GB of VRAM. Given the remaining 18.7GB of VRAM, you can achieve a practical context window of up to 32768 tokens, ensuring efficient and fast inference.

1. Install runtimeOllama

pip install ollama
ollama config set runtime cuda

2. Download the model

Download the Qwen3 8B Base model with Q4_K_M quantization (4.8GB file).

ollama pull bartowski/Qwen3-8B-Base-GGUF:Qwen3-8B-Base-Q4_K_M.gguf

3. Run it

ollama run Qwen3-8B-Base-Q4_K_M --n-gpu-layers 32 --flash-attn
ollama chat Qwen3-8B-Base-Q4_K_M

4. Optimize for RTX 4090

For optimal performance on the NVIDIA GeForce RTX 4090 with 24GB VRAM, use --n-gpu-layers 32 to offload layers to the GPU, enable --flash-attn for faster attention computation, and consider using tensor parallelism if running multiple instances. This configuration will utilize approximately 5.3GB of VRAM, leaving 18.7GB for context and other tasks.

Troubleshooting

Insufficient VRAM during inference.

Reduce --n-gpu-layers to 16 or 8 to lower VRAM usage.

Slow token generation speed.

Ensure --flash-attn is enabled and check that CUDA is properly configured.

Model fails to load.

Verify the model file integrity and try re-downloading the model using the 'ollama pull' command.

Alternative runtimes

For users preferring different runtimes, consider LM Studio for a more user-friendly GUI, llama.cpp for fine-grained control over quantization and performance settings, or Jan for integration with other AI tools. Each runtime has its strengths, but Ollama provides a balanced approach for ease of use and performance on the NVIDIA GeForce RTX 4090.

Full Qwen3 8B Base details →

Other models that run great on RTX 4090

FAQ (20)

What GPU do I need to run Qwen3 8B Base?

To run Qwen3 8B Base, you need a GPU with at least 5.3 GB of VRAM for the lowest quantization level, up to 16.5 GB for the highest. NVIDIA GPUs like the RTX 3060 or higher are recommended.

Is Qwen3 8B Base good for coding?

Qwen3 8B Base is suitable for coding tasks, offering strong natural language understanding and code generation capabilities, though it may not be as specialized as models trained specifically for coding.

Qwen3 8B Base vs Llama 3.1 8B?

Qwen3 8B Base has a larger context length (32,768 tokens) compared to Llama 3.1 8B, which typically has a shorter context length. Qwen3 8B Base also uses the Apache 2.0 license, making it more permissive for commercial use.

Can I run Qwen3 8B Base on a Mac?

Yes, you can run Qwen3 8B Base on a Mac, but you will need a Mac with an M1 or later chip and sufficient VRAM. You may also need to install additional software like Docker or a compatible GPU driver.

How much VRAM does Qwen3 8B Base need?

The VRAM requirement for Qwen3 8B Base ranges from 5.3 GB to 16.5 GB, depending on the quantization level used. Lower quantization levels require less VRAM but may have a slight impact on performance.

Is Qwen3 8B Base censored?

No, Qwen3 8B Base is not censored. It is a foundation model without alignment or refusal training, allowing for more natural and uncensored responses.

Is Qwen3 8B Base commercial-use allowed?

Yes, Qwen3 8B Base is licensed under Apache 2.0, which allows for commercial use, modification, and distribution without restrictions.

Qwen3 8B Base context length?

Qwen3 8B Base has a context length of 32,768 tokens, which is significantly longer than many other models, allowing for more extensive and coherent conversations.

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