Can RTX 4070 Ti SUPER run Phi-3.5 MoE?
Yes — runs locally
~0 tok/sec · Cannot run — insufficient VRAM
The verdict
The RTX 4070 Ti SUPER (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 4070 Ti SUPER
AI-generated, GPU-specific. Verified commands for your exact hardware.
Phi-3.5 MoE runs on the NVIDIA GeForce RTX 4070 Ti SUPER with a grade D, using the Q4_K_M quantization, achieving ~17 tok/sec.
Prerequisites
Before starting, ensure you have at least 25GB of free disk space, a compatible operating system (Windows or Linux), the latest NVIDIA driver (version 525.60.12 or later), and CUDA 11.8 installed.
Expected performance
With the Q4_K_M quantization, you can expect the model to run at approximately 17 tokens per second, using around 24.1GB of VRAM. This leaves about -8.1GB of VRAM for context, allowing for a practical context window of up to 131,072 tokens, though actual context may be limited by the remaining VRAM.
1. Install runtimeOllama
pip install ollama
ollama init2. Download the model
Download the Phi-3.5 MoE model with Q4_K_M quantization (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.gguf --n-gpu-layers 28 --flash-attn
ollama chat Phi-3.5-MoE-instruct-Q4_K_M.gguf4. 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 28 to maximize the number of layers offloaded to the GPU. Enable --flash-attn to reduce memory usage and improve speed. Tensor parallelism is not necessary for this model and GPU combination.
Troubleshooting
Out of memory errors during inference
Reduce --n-gpu-layers to 20 or lower and disable --flash-attn.
Slow inference speed
Ensure CUDA and the NVIDIA driver are up to date. Try increasing --n-gpu-layers to 32 if your VRAM allows it.
Model fails to load
Verify the integrity of the downloaded model file and try re-downloading it.
Alternative runtimes
Alternative runtimes include LM Studio, llama.cpp, and Jan. LM Studio offers a more user-friendly interface and is suitable for those who prefer a graphical environment. llama.cpp is highly optimized for low-memory systems and can be a good choice if you need to further reduce VRAM usage. Jan is another lightweight option that supports a wide range of models but may not offer the same level of performance tuning as Ollama.
Other models that run great on RTX 4070 Ti SUPER
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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