| Model Name | Size (4-bit Quantization) | Recommended GPUs | Tokens/s |
|---|---|---|---|
| phi:2.7b | 1.6GB | P1000 < GTX1650 < GTX1660 < RTX2060 < RTX5060 | 19.46~132.97 |
| phi3:3.8b phi4-mini:3.8b | 2.2GB | P1000 < GTX1650 < GTX1660 < RTX2060 < RTX5060 | 18.87-75.94 |
| phi3:14b | 7.9GB | A4000 < V100 | 38.46-67.51 |
| phi4:14b | 9.1GB | A4000 < V100 | 30.20-48.63 |
| Model Name | Size (16-bit Quantization) | Recommended GPU(s) | Concurrent Requests | Tokens/s |
|---|---|---|---|---|
| microsoft/Phi-3.5-vision-instruct | ~8.8GB | V100 < A5000 < RTX4090 | 50 | ~2000-6000 |
Express GPU VPS - 2GB
Lite Dedicated GPU Server - P600
Express Dedicated GPU Server - P1000
Basic Dedicated GPU Server - K80
Basic GPU VPS - RTX 5060
Basic Dedicated GPU Server - T1000
Basic Dedicated GPU Server - GTX 1650
Professional GPU VPS - RTX Pro 2000
Basic Dedicated GPU Server - GTX 1660
Professional GPU VPS - RTX A4000
Basic Dedicated GPU Server - RTX 4060
Basic Dedicated GPU Server - RTX 5060
Professional Dedicated GPU Server - P100
Professional Dedicated GPU Server - RTX 2060
Advanced GPU VPS - RTX Pro 4000
Advanced Dedicated GPU Server - RTX 2060
Advanced Dedicated GPU Server - RTX 3060 Ti
Advanced Dedicated GPU Server - RTX A4000
Advanced Dedicated GPU Server - V100
Advanced Dedicated GPU Server - RTX A5000
Advanced GPU VPS - RTX Pro 5000
Advanced GPU VPS - RTX 5090
Enterprise Dedicated GPU Server - RTX 4090
Enterprise Dedicated GPU Server - RTX A6000
Enterprise Dedicated GPU Server - A40
Enterprise Dedicated GPU Server - RTX 5090
Enterprise GPU VPS - RTX Pro 6000
Enterprise Multi-GPU Dedicated Server - 3xV100
Enterprise Multi-GPU Dedicated Server - 3xRTX A5000
Enterprise Dedicated GPU Server - A100
Enterprise Multi-GPU Dedicated Server - 2xRTX 4090
Enterprise Multi-GPU Dedicated Server - 2xRTX 5090
Enterprise Multi-GPU Dedicated Server - 3xRTX A6000
Enterprise Multi-GPU Dedicated Server - 4xRTX A6000
Enterprise Dedicated GPU Server - A100(80GB)
Enterprise Multi-GPU Dedicated Server - 4xA100
Enterprise Dedicated GPU Server - H100
Microsoft Phi Hosting is to the deploy and serve of Microsoft’s lightweight language models—such as Phi-3, Phi-3.5, Phi-4, Phi-4-Mini, and Phi-4-Reasoning—on dedicated infrastructure or cloud environments. These models are optimized for reasoning, efficiency, and fast inference, making them ideal for lightweight AI applications.
Self-hosted Phi Hosting means running the models on your own servers or edge devices. You can use tools like Ollama, vLLM, or Transformers to serve Phi models with full control over hardware, latency, data privacy, and model behavior.
In contrast, Phi as a Service lets you access the Phi models via public cloud APIs—typically through providers like Azure, Hugging Face Inference Endpoints, or hosted APIs by third parties.
✅ High-memory GPUs: RTX 4090, A5000, or A100 40GB for full-precision or concurrent workloads
✅ CPU: Multi-core (8+ cores) for fast data loading and support processes
✅ RAM: 32GB+ system memory recommended to support model loading and runtime stability
✅ Storage: NVMe SSD for fast model loading (at least 50–100GB free space for multiple variants)
✅ Model Format: Hugging Face Transformers, GGUF (for llama.cpp/Ollama), or AWQ/GPTQ quantized weights
✅ Inference Engine: vLLM, Ollama, llama.cpp
✅ Serving Tools: FastAPI, OpenAI-compatible APIs, TGI (Text Generation Inference), Docker
✅ Optional Add-ons: LoRA fine-tuning loaders, quantization tools (AutoAWQ, GPTQ), monitoring stack (Prometheus, Grafana)
| Feature | Self-hosted Phi Hosting | Phi as a Service |
|---|---|---|
| Infrastructure Ownership | You own and manage the server and GPU resources | Fully managed by third-party providers |
| Model Control & Customization | Full control over model version, quantization, and config | Limited or no control over model internals |
| Latency & Performance | Optimized for local or on-prem use, low latency possible | May experience higher latency due to remote hosting |
| Privacy & Data Security | High — data stays on your hardware | Depends on provider policies and cloud environment |
| Scalability | Manual — add more hardware or scale vertically | Easy to scale — provider handles infrastructure |
| Initial Setup Complexity | Requires setup: GPU drivers, inference engines, etc. | No setup needed — ready-to-use APIs |
| Operating Cost | Higher upfront cost, lower long-term cost | Pay-as-you-go; higher cost over time |
| Ideal For | Developers, startups, enterprises with infra expertise | Prototyping, low-traffic apps, quick deployments |
| Example Tools | vLLM, Ollama, Hugging Face Transformers, llama.cpp | Azure AI Studio, Hugging Face Inference Endpoints |
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