Quick Start
- 1
- 2Use the NVIDIA NIM SDK or REST API to call
llava-1.6-mistral-7b— 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
No model capability flags are currently sourced.
About LLaVA 1.6 Mistral 7B
LLaVA-v1.6 Mistral-7B is an open-source, multimodal language model capable of processing text and images. Built on the Mistral-7B-Instruct-v0.2 base, it combines a large language model with a vision encoder to enhance reasoning, optical character recognition, and world understanding. Trained on substantial datasets, including image-text pairs from LAION/CC/SBU, GPT-generated data, and VQA data, it was evaluated against 12 benchmarks. The model improves upon LLaVA-1.5 with higher image resolution processing and better reasoning, offering bilingual support and commercial licensing. It finds use in applications like chatbots, image captioning, and visual QA tasks but requires significant computational resources for high-res images.