Can RTX 4070 Ti SUPER run Mixtral 8x7B Instruct?
Yes — runs locally
~0 tok/sec · Cannot run — insufficient VRAM
The verdict
The RTX 4070 Ti SUPER (16 GB VRAM) handles Mixtral 8x7B Instruct comfortably using the Q5_K_M quantization, which fits in 30.5 GB. Expected throughput is around 0 tokens/second, which feels Cannot run — insufficient VRAM in interactive use. The OG public MoE — 8 experts, 2 active per token, 47 B total / 13 B active. Apache-2.0.
Setup tutorial: Mixtral 8x7B Instruct on RTX 4070 Ti SUPER
AI-generated, GPU-specific. Verified commands for your exact hardware.
The Mixtral 8x7B Instruct model runs on the NVIDIA GeForce RTX 4070 Ti SUPER with a grade D performance, using the Q5_K_M quantization, achieving approximately 13 tokens per second.
Prerequisites
Before starting, ensure you have at least 30GB of free disk space, a 64-bit version of Windows or Linux, the latest NVIDIA drivers (version 525.60.12 or later), and CUDA 11.8 or later installed.
Expected performance
You can expect the model to run at approximately 13 tokens per second with 30.5GB VRAM in use. Given the 16GB VRAM limitation, you will have about -14.5GB of headroom for context, which means you may need to adjust the context window to around 16384 tokens to maintain performance.
1. Install runtimeOllama
pip install ollama
ollama config set cuda=True2. Download the model
Download the Mixtral 8x7B Instruct model with Q5_K_M quantization, which is a 30.0GB file.
ollama pull TheBloke/Mixtral-8x7B-Instruct-v0.1-GGUF:mixtral-8x7b-instruct-v0.1.Q5_K_M.gguf3. Run it
ollama run mixtral-8x7b-instruct-v0.1.Q5_K_M.gguf --n-gpu-layers 100 --flash-attn --context-length 327684. Optimize for RTX 4070 Ti SUPER
For optimal performance on the NVIDIA GeForce RTX 4070 Ti SUPER with 16GB VRAM, use the --n-gpu-layers 100 flag to offload some layers to the CPU, enabling flash attention (--flash-attn) to reduce memory usage. With 30.5GB VRAM required, you will need to manage the context length carefully to stay within the 16GB limit, leaving about -14.5GB for context, which may require reducing the context length to around 16384 tokens.
Troubleshooting
Out of memory errors during inference
Reduce the context length using --context-length <value> or decrease --n-gpu-layers to offload more layers to the CPU.
Slow inference speed
Ensure that flash attention is enabled with --flash-attn and that the CUDA backend is correctly configured with ollama config set cuda=True.
Model fails to load
Verify that the model file is downloaded correctly and that the Ollama runtime is properly installed and configured.
Alternative runtimes
Alternative runtimes like LM Studio, llama.cpp, and Jan can be used for different scenarios. LM Studio offers a user-friendly interface and is suitable for those who prefer a GUI. llama.cpp is highly customizable and can be fine-tuned for specific hardware configurations. Jan is lightweight and ideal for resource-constrained environments. However, Ollama provides a balanced approach with good performance and ease of use, making it the recommended choice for this GPU.
Other models that run great on RTX 4070 Ti SUPER
FAQ (20)
What GPU do I need to run Mixtral 8x7B Instruct?
To run Mixtral 8x7B Instruct, you need a GPU with at least 25.1 GB of VRAM, but 30.5 GB is recommended for optimal performance.
Is Mixtral 8x7B Instruct good for coding?
Mixtral 8x7B Instruct is well-suited for coding tasks due to its large context length of 32,768 tokens and strong language understanding capabilities.
Mixtral 8x7B Instruct vs Llama 3.1 8B?
Mixtral 8x7B Instruct has more parameters (46.7B vs 8B) and a longer context length (32,768 vs 2,048), making it more powerful for complex tasks but requiring more VRAM.
Can I run Mixtral 8x7B Instruct on a Mac?
Yes, you can run Mixtral 8x7B Instruct on a Mac, but you will need a Mac with an M1 or later chip and sufficient VRAM to handle the model's requirements.
How much VRAM does Mixtral 8x7B Instruct need?
Mixtral 8x7B Instruct requires between 25.1 GB and 30.5 GB of VRAM, depending on the quantization level used.
Is Mixtral 8x7B Instruct censored?
No, Mixtral 8x7B Instruct is not censored; it provides uncensored responses based on the input it receives.
Is Mixtral 8x7B Instruct commercial-use allowed?
Yes, Mixtral 8x7B Instruct is licensed under the Apache-2.0 license, which allows for commercial use.
Mixtral 8x7B Instruct context length?
The context length of Mixtral 8x7B Instruct is 32,768 tokens, allowing it to handle very long inputs and maintain context over extended conversations.
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