Using Baichuan 2 13B Chat on NVIDIA NIM
Implementation guide · Baichuan 2 · Baichuan Intelligent Technology
Quick Start
- 1
- 2Use the NVIDIA NIM SDK or REST API to call
baichuan-2-13b-chat— 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 Baichuan 2 13B Chat
Baichuan 2 13B Chat is a large language model from Baichuan Intelligence, designed to excel in text generation, dialogue systems, question answering, and code generation. Built on Transformer architecture, it uses PyTorch 2.0 enhancements for efficient performance. Trained on a vast dataset of 2.6 trillion tokens, it demonstrates strong cross-lingual functionalities, notably in Chinese and English. Its notable features include open-source access, a commercially usable license, and quantized versions for resource efficiency. Despite its power and versatility, it shares common LLM limitations, such as potential bias and specific knowledge gaps.