Can RTX 4080 SUPER run Qwen3 8B Base?
Yes — runs locally
~78 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 4080 SUPER (16 GB VRAM) handles Qwen3 8B Base comfortably using the Q4_K_M quantization, which fits in 5.3 GB. Expected throughput is around 78 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 4080 SUPER
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Qwen3 8B Base on your NVIDIA GeForce RTX 4080 SUPER with Q4_K_M 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, the latest NVIDIA drivers (version 525.60.13 or later), and CUDA 11.8 installed.
Expected performance
With the Q4_K_M quantization, you can expect the model to run at ~123 tok/sec, using 5.3GB of VRAM. This leaves 10.7GB of VRAM available for context, enabling you to handle large inputs and maintain high performance.
1. Install runtimeOllama
pip install ollama
ollama init2. Download the model
Download the Qwen3 8B Base model with Q4_K_M quantization (4.8GB file size) from the Hugging Face repository.
ollama pull bartowski/Qwen3-8B-Base-GGUF:Qwen3-8B-Base-Q4_K_M.gguf3. Run it
ollama run Qwen3-8B-Base-Q4_K_M --n-gpu-layers 32 --flash-attn --context-length 327684. Optimize for RTX 4080 SUPER
For optimal performance on the NVIDIA GeForce RTX 4080 SUPER with 16GB VRAM, set --n-gpu-layers to 32 to utilize most of the GPU memory while leaving some headroom. Enable --flash-attn for faster attention computation. With 5.3GB VRAM used by the model, you will have approximately 10.7GB of VRAM left for context, allowing for a practical context window close to the maximum 32768 tokens.
Troubleshooting
The model runs out of VRAM during inference.
Reduce the --n-gpu-layers value to 24 or 16 to lower VRAM usage.
Inference is slower than expected.
Ensure that --flash-attn is enabled and that your CUDA installation is up-to-date.
The model fails to load.
Check that the model file has been downloaded correctly and that there are no issues with the Ollama installation.
Alternative runtimes
If you prefer a different runtime, consider LM Studio for a more user-friendly interface, llama.cpp for fine-grained control over optimizations, or Jan for cloud-based deployment. Each has its own strengths, but Ollama provides a balanced approach for local deployment on the RTX 4080 SUPER.
Other models that run great on RTX 4080 SUPER
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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