Can RTX 4070 Ti SUPER run OLMoE 1B-7B?

Yes — runs locally

~70 tok/sec · Instant — feels like typing. No noticeable delay.

Your VRAM

16 GB

Model size

6.9B

Best quant

Q8_0

VRAM needed

7.3 GB

The verdict

The RTX 4070 Ti SUPER (16 GB VRAM) handles OLMoE 1B-7B comfortably using the Q8_0 quantization, which fits in 7.3 GB. Expected throughput is around 70 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 4070 Ti SUPER

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

TL;DR

The OLMoE 1B-7B model runs at Grade S on an NVIDIA GeForce RTX 4070 Ti SUPER with the Q8_0 quantization, achieving ~92 tokens per second.

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 525.60.11 or later), and CUDA 11.8 or later 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 support a full context window of 4096 tokens, ensuring smooth and efficient operation.

1. Install runtimeOllama

pip install ollama
ollama init

2. 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.gguf

3. Run it

ollama serve
curl -X POST -H 'Content-Type: application/json' -d '{"prompt": "Hello, how are you?", "max_tokens": 4096}' http://localhost:8000/v1/completions

4. Optimize for RTX 4070 Ti SUPER

For optimal performance on the NVIDIA GeForce RTX 4070 Ti SUPER with 16GB VRAM, set --n-gpu-layers to 32 to utilize the GPU effectively. Enable flash attention (--flash-attn) to reduce memory usage and improve speed. With 7.3GB VRAM used by the model, you have 8.7GB of VRAM left for context, allowing for a practical context window close to the maximum 4096 tokens.

Troubleshooting

Model fails to load due to insufficient VRAM

Reduce the number of GPU layers with --n-gpu-layers 24 or lower, or enable flash attention with --flash-attn.

Performance is below 92 tok/sec

Ensure that CUDA and NVIDIA drivers are up to date, and try increasing the batch size or enabling flash attention.

Out of memory errors during inference

Decrease the context length or reduce the number of GPU layers with --n-gpu-layers 24.

Alternative runtimes

Alternative runtimes like LM Studio, llama.cpp, and Jan can be used for more advanced customization or different deployment scenarios. LM Studio offers a user-friendly interface and is suitable for those who prefer a GUI. llama.cpp is ideal for low-level control and optimization, while Jan is a lightweight option for quick testing and prototyping. Choose based on your specific needs and preferences.

Full OLMoE 1B-7B details →

Other models that run great on RTX 4070 Ti 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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