Using PaliGemma 3B 896 on NVIDIA NIM
Implementation guide · PaliGemma · Google DeepMind
Quick Start
- 1
- 2Use the NVIDIA NIM SDK or REST API to call
paligemma-3b-896— see the documentation for request format. - 3
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 PaliGemma 3B 896
PaliGemma 3B 896 is a versatile and lightweight vision-language model developed by Google, designed to process and integrate both images and text. Inspired by the PaLI-3 model, it employs components like the SigLIP vision model and the Gemma-2B language model, featuring a linear projection layer for seamless integration of visual and textual inputs. Capable of handling tasks such as image captioning, visual question answering, object detection, and segmentation, it supports multilingual text processing. Despite requiring task-specific fine-tuning for optimal performance, PaliGemma highlights strong capabilities across various vision-language applications, although it may encounter challenges with contextual understanding, biases, and computational demands 124.