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Apply for a GPU community grant: Company project
We are building a public Hugging Face Space that enables real-time observability and optimization of large language model inference.
The Space allows users to interactively compare models and configurations while streaming live telemetry, including latency, throughput (tokens/sec), and GPU memory usage. Users can toggle optimization techniques such as quantization, batching, and context window size to directly observe their impact on performance and cost.
The goal is to provide a transparent, educational, and practical tool for developers and researchers to understand how to deploy LLMs efficiently. This directly supports the Hugging Face ecosystem by improving model usability, reducing deployment costs, and encouraging best practices in inference optimization.
The project will be fully open-source and designed as a reusable reference for the community.
Why GPU resources are required
The Space performs repeated real-time inference across multiple configurations and models, requiring GPU acceleration to provide meaningful, low-latency comparisons and accurate performance measurements.
Expected impact
Help developers choose optimal deployment configurations
Provide a benchmark reference for LLM performance on Hugging Face
Encourage efficient and sustainable use of compute resources