Can RTX 4090 run Phi-3.5 Mini 3.8B?
Yes — runs locally
~144 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 4090 (24 GB VRAM) handles Phi-3.5 Mini 3.8B comfortably using the Q8_0 quantization, which fits in 4.3 GB. Expected throughput is around 144 tokens/second, which feels Instant — feels like typing. No noticeable delay. in interactive use. Tiny but capable 3.8B model. Runs on almost any hardware including phones.
Setup tutorial: Phi-3.5 Mini 3.8B on RTX 4090
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Phi-3.5 Mini 3.8B on an NVIDIA GeForce RTX 4090 with Q8_0 quantization for Grade S performance at ~267 tok/sec.
Prerequisites
Before starting, ensure you have at least 4GB of free disk space, a compatible operating system (Windows 10/11 or Linux), and the latest NVIDIA drivers (version 525.60.13 or later) installed along with CUDA 11.8.
Expected performance
With the recommended settings, you can expect the model to run at approximately 267 tokens per second, using around 4.3GB of VRAM. This leaves 19.7GB of VRAM available for context, allowing for a practical context window of up to 131,072 tokens.
1. Install runtimeOllama
pip install ollama
ollama init2. Download the model
Download the Q8_0 quantized Phi-3.5 Mini 3.8B model (3.8GB file) from Hugging Face.
ollama pull bartowski/Phi-3.5-mini-instruct-GGUF:Phi-3.5-mini-instruct-Q8_0.gguf3. Run it
ollama run Phi-3.5-mini-instruct-Q8_0.gguf --n-gpu-layers 38 --flash-attn --tensor-parallelism 14. Optimize for RTX 4090
For optimal performance on the NVIDIA GeForce RTX 4090 with 24GB VRAM, set --n-gpu-layers to 38 to utilize the GPU efficiently. Enable --flash-attn for faster attention computation and set --tensor-parallelism to 1 for single-GPU operation. This configuration will maximize throughput while keeping VRAM usage within bounds.
Troubleshooting
Out of memory error during inference
Reduce the number of --n-gpu-layers or decrease the batch size.
Slow inference speed
Ensure that --flash-attn is enabled and that the latest NVIDIA drivers and CUDA are installed.
Model not found
Verify that the model was correctly downloaded and is accessible in the Ollama model directory.
Alternative runtimes
For users who prefer different runtimes, consider LM Studio for a more user-friendly interface, llama.cpp for lightweight deployment, or Jan for advanced customization options. Each runtime has its strengths, but Ollama provides a balanced approach for ease of use and performance on the NVIDIA GeForce RTX 4090.
Other models that run great on RTX 4090
FAQ (20)
What GPU do I need to run Phi-3.5 Mini 3.8B?
Phi-3.5 Mini 3.8B requires a GPU with at least 2.7 GB of VRAM, but 4.3 GB is recommended for optimal performance.
Is Phi-3.5 Mini 3.8B good for coding?
Phi-3.5 Mini 3.8B is capable of generating code and providing coding assistance, but its performance is best suited for simpler tasks due to its 3.8B parameters.
Phi-3.5 Mini 3.8B vs Llama 3.1 8B?
Phi-3.5 Mini 3.8B has 3.8B parameters, making it smaller and more resource-efficient than Llama 3.1 8B, which has 8B parameters and requires more VRAM and computational power.
Can I run Phi-3.5 Mini 3.8B on a Mac?
Yes, Phi-3.5 Mini 3.8B can run on a Mac, provided your Mac has a compatible GPU with at least 2.7 GB of VRAM.
How much VRAM does Phi-3.5 Mini 3.8B need?
Phi-3.5 Mini 3.8B requires a minimum of 2.7 GB of VRAM, but 4.3 GB is recommended for better performance, depending on the quantization level.
Is Phi-3.5 Mini 3.8B censored?
Phi-3.5 Mini 3.8B is not inherently censored, but it may include content filters to prevent harmful or inappropriate content.
Is Phi-3.5 Mini 3.8B commercial-use allowed?
Yes, Phi-3.5 Mini 3.8B is licensed under the MIT License, which allows for commercial use.
Phi-3.5 Mini 3.8B context length?
Phi-3.5 Mini 3.8B supports a context length of 131,072 tokens, which is quite large and allows for extensive context in conversations and tasks.
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