LLM
How to deploy Phi-3.5-mini-instruct on a GPU cloud
A 3.8B language model for chat and instruction-following. Full specs, license and use cases.
Phi-3.5-mini-instruct size and hardware requirements
3.8B
Total parameters
Dense (no MoE)
Architecture
BF16
Published precision
8.5 GB
Min VRAM (native)
| Precision | Weight size on disk | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| BF16 | 7.1 GB | 8.5 GB | RTX 4070 Super | 1 | $0.110/hr |
| FP8 (quantized) | 3.6 GB | 4.3 GB | RTX 4070 Super | 1 | $0.110/hr |
| INT4 (quantized) | 1.8 GB | 2.1 GB | RTX 4070 Super | 1 | $0.110/hr |
How to run Phi-3.5-mini-instruct
Run Phi-3.5-mini-instruct with vLLM
Generic example, not from the model's own docs: adjust flags (quantization, context length, parallelism) for your setup.
vllm serve microsoft/Phi-3.5-mini-instruct --tensor-parallel-size 1Run Phi-3.5-mini-instruct with GGUF quantizations
Prebuilt GGUF weights published at bartowski/Phi-3.5-mini-instruct-GGUF. Run with llama.cpp's llama-server or load the repo directly in LM Studio.
llama-server -hf bartowski/Phi-3.5-mini-instruct-GGUFSource: https://huggingface.co/bartowski/Phi-3.5-mini-instruct-GGUF
Deploy Phi-3.5-mini-instruct on Aquanode
Aquanode has no one-click deploy template for Phi-3.5-mini-instruct; you install the inference engine yourself with the commands below. Aquanode sells GPU pods billed per second, not a hosted inference API.
- Launch a bare GPU pod sized to the requirement above (1× RTX 4070 Super or larger).
- Open a terminal on the pod, or save one of the commands above as a startup script so it runs automatically the first time the pod boots.
- Run the command and connect to the resulting endpoint.
Launch a GPU pod