Using Phi-3 Vision on NVIDIA NIM
Implementation guide · Phi-3 · Microsoft Research
Quick Start
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Code Examples
About NVIDIA NIM
NIM packages inference runtimes and model profiles into containers that expose standard API surfaces such as chat completions, completions, model listing, tokenization, health, and management endpoints. The hosted API path is useful for prototyping and catalog discovery, while the NGC/container path is the self-hosted route for teams that want GPU-hour infrastructure control, private-network deployment, Kubernetes scaling, or NVIDIA AI Enterprise support. Per-token pricing is not a universal provider-level claim in the current seed data; pricing should stay attached to sourced model-provider rows or NVIDIA's current catalog terms.
NVIDIA NIM is NVIDIA's deployment platform for GPU-accelerated inference microservices. Developers can try hosted NIM APIs through the NVIDIA API Catalog on build.nvidia.com, then move the same model families into self-hosted NIM containers on NVIDIA GPUs in a data center, private cloud, public cloud, or workstation. The catalog positions NIM around optimized open and NVIDIA models, including chat, coding, reasoning, retrieval, vision, speech, and safety use cases, with downloadable model cards and API endpoints where NVIDIA exposes them.
Pricing on NVIDIA NIM
| Type | Rate |
|---|---|
| GPU Hour Rate | $1.00/GPU·hr |
| GPU Config | 1xH100 |
Capabilities
About Phi-3 Vision
Phi-3 Vision is a sophisticated multimodal AI model from Microsoft, designed to adeptly integrate language and vision capabilities. Unlike traditional language models, it processes both text and images and can perform tasks such as optical character recognition, chart analysis, and image interpretation. Its architecture features an image encoder, a text-image connector, a projector for mapping image features, and the Phi-3 Mini language model. Despite its relatively small size of 4.2 billion parameters, it competes with larger models and suits devices with limited computational power. Phi-3 Vision's ability to handle up to 128K tokens supports complex multimodal reasoning. It draws upon high-quality and synthetic data for training while incorporating essential safety measures.