Can RTX 3090 Ti run Phi-3.5 Mini 3.8B?
Yes — runs locally
~96 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 3090 Ti (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 96 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 3090 Ti
AI-generated, GPU-specific. Verified commands for your exact hardware.
The Phi-3.5 Mini 3.8B model runs at Grade S on an NVIDIA GeForce RTX 3090 Ti with Q8_0 quantization, achieving ~267 tok/sec.
Prerequisites
Before starting, ensure you have at least 5GB of free disk space, a compatible operating system (Windows or Linux), and the latest NVIDIA drivers (version 525.60.13 or later) installed along with CUDA 11.8.
Expected performance
You can expect the model to run at approximately 267 tokens per second, using around 4.3GB of VRAM. With 19.7GB of VRAM remaining, you can achieve a practical context window of up to 131072 tokens, making it highly efficient for long-form content generation.
1. Install runtimeOllama
curl -L https://ollama.ai/install.sh | bash
ollama install2. Download the model
Download the Q8_0 quantized model (3.8GB) 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 --n-gpu-layers 38 --flash-attn
ollama chat Phi-3.5-mini-instruct-Q8_04. Optimize for RTX 3090 Ti
For optimal performance on the NVIDIA GeForce RTX 3090 Ti with 24GB VRAM, set --n-gpu-layers to 38 to fully utilize the GPU. Enable --flash-attn to speed up attention calculations. With 4.3GB VRAM used by the model, you have 19.7GB of VRAM left for context, allowing for a large practical context window of up to 131072 tokens.
Troubleshooting
Out of memory errors during inference
Reduce the number of --n-gpu-layers or decrease the context length to fit within the available VRAM.
Slow inference times
Ensure that --flash-attn is enabled and that your CUDA drivers are up to date.
Model not found
Verify that the model was successfully downloaded and is listed in the Ollama models directory using 'ollama list'.
Alternative runtimes
Alternative runtimes like LM Studio, llama.cpp, and Jan can be used for more advanced customization or different use cases. LM Studio is ideal for a graphical interface, llama.cpp offers fine-grained control over inference parameters, and Jan is suitable for lightweight deployments. However, Ollama provides a balanced approach with ease of use and good performance on the NVIDIA GeForce RTX 3090 Ti.
Other models that run great on RTX 3090 Ti
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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