Autoscaling Autoresearch: Give your agents elastic GPUs on Modal

Modal presents its serverless GPU platform as a way for AI research agents to dynamically choose both the scale and type of computing resources needed for experiments, avoiding the cost of idle clusters while enabling parallel work beyond a single workstation’s capacity. In a demonstration using Claude Code and OpenAI’s Parameter Golf challenge, an agent reportedly conducted 113 experiments over 15 hours and 238 GPU-hours, moving between single-GPU pipeline tests, roughly 40 parallel hyperparameter trials, five simultaneous 8×H100 validation runs, serial debugging, and later large-scale optimization. The agent improved its bits-per-byte score from 1.42 to approximately 1.12 by testing model and training configurations, while resolving a major CPU-based quantization bottleneck by rewriting it for GPU execution. Modal attributes the reported fivefold speedup in core training over an 8×H100 workstation and improved resource efficiency relative to a continuously provisioned 40-GPU cluster to its ability to rapidly provision, scale, and automatically release GPU jobs, sandboxes, storage, and parallel tasks through code-oriented tools and agent guidance.

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