Run LLMs with Ollama on H100 GPUs for Maximum Efficiency

Key takeaways:

• Running large language models with Ollama on an NVIDIA H100 GPU combines an easy-to-use local model deployment tool with one of the most powerful AI accelerators available, allowing even very large models to be served with exceptional speed and throughput.
• This guide shows how to provision a DigitalOcean GPU Droplet equipped with an H100 and set up Ollama to load and serve an LLM, so developers can deploy advanced models (like code assistants or chatbots based on Llama 2) on dedicated high-end hardware without complex infrastructure work.
• Using an H100 dramatically accelerates model inference (and even fine-tuning tasks) thanks to its advanced Tensor Cores and huge memory; for example, the H100’s support for FP8 precision and massive parallelism translates to lower latency responses and the ability to handle larger batch sizes or context windows compared to using smaller GPUs.
• This approach offers the flexibility of cloud scaling—spinning up a powerful GPU when needed—while Ollama provides a straightforward way to run the model, meaning you can harness cutting-edge model performance on demand without requiring deep ML operations expertise.

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