What GPU do I need to run microsoft/Phi-3.5-mini-instruct?
A 3.8B language model for chat and instruction-following. 3.8B parameters, published in BF16. View on Hugging Face
Phi-3.5-mini-instruct is published by microsoft on Hugging Face, with 300,703 downloads and 1,072 likes to date. It's a Phi3ForCausalLM model built for text-generation, published natively in BF16.
What Phi-3.5-mini-instruct is
Phi-3.5-mini-instruct is a 3.8B-parameter language model published by Microsoft on Hugging Face. It is released under MIT.
License note: permissive: allows commercial use, modification and redistribution. Facts in this section are sourced from Phi-3.5-mini-instruct's Hugging Face model card, not benchmarked by Aquanode.
What it's used for
- Chat assistants
- Instruction following
- Synthetic data generation
VRAM required & cheapest live GPU fit
Required VRAM = weight size at each precision, plus a fixed overhead for KV-cache, activations, and fragmentation. Full formula and assumptions: methodology.
| Precision | Weight size | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| BF16 | 7.1 GB | 8.5 GB | RTX 5060 Ti | 1 | $0.110/hr |
| FP8 (quantized) | 3.6 GB | 4.3 GB | RTX 5060 Ti | 1 | $0.110/hr |
| INT4 (quantized) | 1.8 GB | 2.1 GB | RTX 5060 Ti | 1 | $0.110/hr |
A GPU is only matched to a row if its hardware supports that precision, and the primary recommendation is always a single-GPU fit when one exists.
INT4 requires a quantized checkpoint actually published for this model, check its Hugging Face page before relying on this row.
Cheapest way to run Phi-3.5-mini-instruct at its published (BF16) precision: 1× RTX 5060 Ti, at $0.110/hr per GPU ($0.110/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
Phi-3.5-mini-instruct: common questions
Does Phi-3.5-mini-instruct fit on a 12 GB GPU?
Yes. At BF16 it needs 8.5 GB of VRAM, so a 12 GB card holds it with 3.5 GB to spare. An 8 GB card is not enough for it at BF16.
What is the least VRAM Phi-3.5-mini-instruct can run in?
2.1 GB, at INT4 (quantized), which fits a 6 GB card, against 8.5 GB at BF16. That row assumes an INT4 checkpoint has actually been published for this model, so check its Hugging Face page before planning around it.
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 5060 Ti 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 podWeight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More Phi-3.5 models
- Phi-3.5-vision-instruct (4.1B, BF16)
- VLM2Vec-Full (4.1B, BF16)
- Phi-3.5-MoE-instruct (41.9B, BF16)
Related reading: H100 pricing and specs, The best GPUs for AI, ranked, and Best GPU for LLM inference.
