Can RTX 4080 SUPER run OLMoE 1B-7B?
Yes — runs locally
~78 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 4080 SUPER (16 GB VRAM) handles OLMoE 1B-7B comfortably using the Q8_0 quantization, which fits in 7.3 GB. Expected throughput is around 78 tokens/second, which feels Instant — feels like typing. No noticeable delay. in interactive use. Fully open MoE — 7 B total, only 1.3 B active per token. Tiny footprint, surprisingly capable.
Setup tutorial: OLMoE 1B-7B on RTX 4080 SUPER
AI-generated, GPU-specific. Verified commands for your exact hardware.
The OLMoE 1B-7B model runs at Grade S on the NVIDIA GeForce RTX 4080 SUPER with the Q8_0 quantization, achieving ~92 tok/sec.
Prerequisites
Before starting, ensure you have at least 10GB of free disk space, a compatible operating system (Windows 10/11 or Linux), and the latest NVIDIA drivers (version 525.60.11 or later) with CUDA 11.8 installed.
Expected performance
With the Q8_0 quantization, you can expect the OLMoE 1B-7B model to run at approximately 92 tokens per second, using around 7.3GB of VRAM. The remaining 8.7GB of VRAM provides ample headroom to maintain a context window of up to 4096 tokens, ensuring smooth and efficient inference.
1. Install runtimeOllama
pip install ollama
ollama init2. Download the model
Download the Q8_0 quantized version of OLMoE 1B-7B (6.9GB file size) from Hugging Face.
ollama pull bartowski/OLMoE-1B-7B-0924-Instruct-GGUF:OLMoE-1B-7B-0924-Instruct-Q8_0.gguf3. Run it
ollama run OLMoE-1B-7B-0924-Instruct-Q8_0 --n-gpu-layers 32 --flash-attn
ollama chat4. 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 the GPU efficiently. Enable --flash-attn for faster and more efficient attention computation. With 7.3GB VRAM used by the model, you have 8.7GB of VRAM headroom for larger context windows, allowing for a practical context length of up to 4096 tokens.
Troubleshooting
Out of memory error during inference
Reduce the number of GPU layers using --n-gpu-layers or decrease the context length to fit within the available VRAM.
Slow inference speed
Ensure that --flash-attn is enabled and that the CUDA toolkit is correctly installed and up-to-date.
Model fails to load
Verify that the model file has been downloaded correctly and that the Ollama runtime is properly installed.
Alternative runtimes
For users preferring different runtimes, consider LM Studio for a more user-friendly interface, llama.cpp for low-level customization, or Jan for lightweight deployment. Each runtime has its strengths, but Ollama is optimized for ease of use and performance on the NVIDIA GeForce RTX 4080 SUPER.
Other models that run great on RTX 4080 SUPER
FAQ (20)
What GPU do I need to run OLMoE 1B-7B?
To run OLMoE 1B-7B, you need a GPU with at least 4.4 GB of VRAM for the smallest quantized version, up to 7.3 GB for the full model.
Is OLMoE 1B-7B good for coding?
OLMoE 1B-7B is versatile and can handle coding tasks well, though it may not be as specialized as models specifically trained for code generation.
OLMoE 1B-7B vs Llama 3.1 8B?
OLMoE 1B-7B has fewer parameters (6.9B) compared to Llama 3.1 8B, but it uses a more efficient MoE architecture, making it lighter and potentially faster in certain tasks.
Can I run OLMoE 1B-7B on a Mac?
Yes, you can run OLMoE 1B-7B on a Mac with an M1 or M2 chip, provided you have the necessary VRAM and system resources.
How much VRAM does OLMoE 1B-7B need?
The VRAM requirement for OLMoE 1B-7B ranges from 4.4 GB to 7.3 GB, depending on the quantization level used.
Is OLMoE 1B-7B censored?
OLMoE 1B-7B is not inherently censored, but its responses can be filtered or moderated using external tools to ensure appropriate content.
Is OLMoE 1B-7B commercial-use allowed?
Yes, OLMoE 1B-7B is licensed under Apache-2.0, which allows for commercial use without additional fees.
OLMoE 1B-7B context length?
OLMoE 1B-7B supports a context length of 4096 tokens, which is suitable for handling longer conversations and documents.
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