Using Gemma 2B Instruct on NVIDIA NIM
Implementation guide · Gemma · Google DeepMind
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 Gemma 2B Instruct
Gemma 2B Instruct is a large language model developed by Google, designed to balance performance and accessibility with its 2 billion parameters. Derived from the Gemini family, it excels in tasks such as text generation, code interpretation, and mathematical problem-solving. Built on a transformer decoder architecture, it features multi-query attention, RoPE, GeGLU activations, and RMSNorm. Trained on approximately 6 trillion tokens, including web documents, code, and mathematical content, it uses SFT and RLHF for instruction-tuning. Notable for its lightweight design permitting deployment on consumer-grade hardware, it's open-source and optimized for dialogue applications. Despite its capabilities, limitations include potential biases, factual inaccuracies, and challenges with complex reasoning.