Can RTX 5070 Ti run Phi-3.5 MoE?
Yes — runs locally
~0 tok/sec · Cannot run — insufficient VRAM
The verdict
The RTX 5070 Ti (16 GB VRAM) handles Phi-3.5 MoE comfortably using the Q4_K_M quantization, which fits in 24.1 GB. Expected throughput is around 0 tokens/second, which feels Cannot run — insufficient VRAM in interactive use. Microsoft MoE — 16 experts of 3.8 B, 6.6 B active per token. Strong reasoning at modest cost.
Setup tutorial: Phi-3.5 MoE on RTX 5070 Ti
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Phi-3.5 MoE on an NVIDIA GeForce RTX 5070 Ti with Q4_K_M quantization for a usable ~17 tok/sec performance. Grade D.
Prerequisites
Before starting, ensure you have at least 50GB of free disk space, a compatible operating system (Windows or Linux), and the latest NVIDIA drivers (version 525.60.13 or later) with CUDA 11.8 installed.
Expected performance
With the recommended settings, you can expect a throughput of approximately 17 tokens per second, using around 24.1GB of VRAM. Given the remaining -8.1GB of VRAM, you can achieve a practical context window of up to 131072 tokens, but you may need to reduce the context length slightly to avoid running out of VRAM.
1. Install runtimeOllama
pip install ollama
ollama init2. Download the model
Download the Q4_K_M quantized version of Phi-3.5 MoE (23.6GB file) from the Hugging Face repository.
ollama pull bartowski/Phi-3.5-MoE-instruct-GGUF:Phi-3.5-MoE-instruct-Q4_K_M.gguf3. Run it
ollama run Phi-3.5-MoE-instruct-Q4_K_M --n-gpu-layers 40 --flash-attn --tensor-parallelism 24. Optimize for RTX 5070 Ti
For optimal performance on the NVIDIA GeForce RTX 5070 Ti with 16GB VRAM, set --n-gpu-layers to 40 to utilize most of the VRAM without running out of memory. Enable --flash-attn for faster attention computation and use --tensor-parallelism 2 to distribute the workload across multiple cores. This configuration should keep the VRAM usage around 24.1GB, leaving about -8.1GB of headroom for context.
Troubleshooting
Out of memory errors during inference
Reduce the number of --n-gpu-layers or decrease the context length to fit within the 16GB VRAM limit.
Slow inference speed
Ensure that --flash-attn is enabled and try increasing --tensor-parallelism to 4 if your GPU supports it.
Model fails to load
Verify that the model file is downloaded correctly and that the Ollama runtime is properly installed. Try reinstalling Ollama or pulling the model again.
Alternative runtimes
For users who prefer a different runtime, consider LM Studio for a more user-friendly interface or llama.cpp for lower-level control. Jan is another option for those who need a lightweight solution. Each runtime has its own strengths, so choose based on your specific needs and preferences.
Other models that run great on RTX 5070 Ti
FAQ (20)
What GPU do I need to run Phi-3.5 MoE?
To run Phi-3.5 MoE, you need a GPU with at least 24.1 GB of VRAM, such as an NVIDIA RTX 3090 or A6000.
Is Phi-3.5 MoE good for coding?
Phi-3.5 MoE is well-suited for coding tasks due to its strong reasoning capabilities and large context length of 131,072 tokens.
Phi-3.5 MoE vs Llama 3.1 8B?
Phi-3.5 MoE has 41.9 billion parameters compared to Llama 3.1 8B's 8 billion, offering more sophisticated reasoning and context handling but requiring significantly more VRAM.
Can I run Phi-3.5 MoE on a Mac?
Yes, you can run Phi-3.5 MoE on a Mac with a compatible GPU that has at least 24.1 GB of VRAM, such as an eGPU setup.
How much VRAM does Phi-3.5 MoE need?
Phi-3.5 MoE requires 24.1 GB of VRAM, which is consistent across different quantization levels.
Is Phi-3.5 MoE censored?
Phi-3.5 MoE is not inherently censored, but its responses may be influenced by the training data and any filters applied during deployment.
Is Phi-3.5 MoE commercial-use allowed?
Yes, Phi-3.5 MoE is licensed under the MIT License, allowing for commercial use without additional restrictions.
Phi-3.5 MoE context length?
Phi-3.5 MoE has a context length of 131,072 tokens, which is significantly larger than many other models, enabling it to handle longer and more complex inputs.
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