Using StarCoder2 15B on NVIDIA NIM
Implementation guide · StarCoder 2 · ServiceNow Research
Quick Start
- 1
- 2Use the NVIDIA NIM SDK or REST API to call
starcoder2-15b— 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 |
Capabilities
About StarCoder2 15B
StarCoder2-15B is a sophisticated large language model, expertly crafted for code generation and understanding. Developed by the BigCode project, it features 15 billion parameters and is trained on The Stack v2, a vast dataset of over 4 trillion tokens from more than 600 programming languages. Its advanced transformer decoder architecture, equipped with a grouped-query and sliding window attention mechanism and a Fill-in-the-Middle training objective, allows a context window of 16,384 tokens. In addition to generating and completing code, the model excels in tasks like code summarization and retrieving relevant snippets through natural language queries. The training leveraged NVIDIA's NeMo framework and the Eos Supercomputer, while usage is governed by the BigCode Open RAIL-M license, supporting royalty-free and commercial use.