Can RTX 4060 Ti 16GB run Phi-3.5 MoE?
Yes — runs locally
~0 tok/sec · Cannot run — insufficient VRAM
The verdict
The RTX 4060 Ti 16GB (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 4060 Ti 16GB
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Phi-3.5 MoE on a NVIDIA GeForce RTX 4060 Ti 16GB with Q4_K_M quantization for ~17 tok/sec performance (Grade D, usable).
Prerequisites
Before starting, ensure you have at least 25GB of free disk space, a 64-bit version of Windows or Linux, NVIDIA driver version 510.47 or later, and CUDA 11.4 or later installed.
Expected performance
With the recommended settings, you can expect the model to run at approximately 17 tokens per second, using around 24.1GB of VRAM. Given the remaining VRAM, a practical context window of around 131072 tokens is achievable, though it may require careful management of the context length to avoid out-of-memory errors.
1. Install runtimeOllama
pip install ollama
ollama config set cuda=True2. Download the model
Download the Phi-3.5 MoE Q4_K_M quantized model (23.6GB) from Hugging Face.
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 32 --flash-attn --tensor-parallelism 14. Optimize for RTX 4060 Ti 16GB
For optimal performance on the NVIDIA GeForce RTX 4060 Ti 16GB, use --n-gpu-layers 32 to offload layers to the GPU, enable --flash-attn for efficient attention computation, and set --tensor-parallelism 1 to match the single GPU setup. This configuration will utilize approximately 24.1GB of the 16GB VRAM, leaving about -8.1GB of headroom for context.
Troubleshooting
Out of memory error during inference
Reduce the context length or decrease --n-gpu-layers to 24.
Slow inference speed
Ensure that CUDA and the NVIDIA driver are up to date, and try increasing --tensor-parallelism to 2 if your GPU supports it.
Model fails to load
Check the integrity of the downloaded model file and try re-downloading it.
Alternative runtimes
For users preferring different runtimes, consider LM Studio for a more user-friendly interface, llama.cpp for fine-grained control over quantization and performance, or Jan for a lightweight alternative. Each runtime has its strengths, but Ollama provides a balanced approach for ease of use and performance on the NVIDIA GeForce RTX 4060 Ti 16GB.
Other models that run great on RTX 4060 Ti 16GB
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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