AI Research Infrastructure

6/30/2026

This post details the integration of Modal's AI research infrastructure with Anthropic's Claude Science, enabling life sciences researchers to run demanding computational workloads directly from a conversational interface. It highlights how Modal provides scalable compute for tasks like data processing pipelines, structure prediction models, and molecule design campaigns. Specific technical capabilities emphasized include fan-out for highly parallel workloads (e.g., virtual screening), GPU access per step for heterogeneous pipelines, shared storage via Modal Volumes for large datasets, and reproducible environments via Modal Images. The post also provides examples of how this integration facilitates complex biological analyses, such as engineering enzymes, designing genome-wide CRISPR knockout screens, and performing single-cell analysis at scale.

6/1/2026

This post details the infrastructure challenges and solutions for Reinforcement Learning (RL) post-training of LLMs. It highlights the three core infrastructure problems: training at scale (multi-node, RDMA, delta compression), efficient rollouts from high-performance inference engines, and managing isolated environments for concurrent actions. Modal's platform is presented as a solution that abstracts away the complexity of maintaining glue code, queuing for cluster time, and under-utilizing GPUs. Specific features like `modal.experimental.clustered` with `rdma=True` for multi-node training, fast container boot times, and scalable Sandboxes for concurrent rollouts are discussed. The post also emphasizes the importance of open-source RL frameworks and Modal's contributions to them, such as delta compression for slime.

5/20/2026

This post details how Applied Compute leverages Modal's platform for Reinforcement Learning (RL) training. It highlights the use of Modal Sandboxes for creating complex, high-fidelity training environments, Modal Functions for massively parallel CPU computation in the grading layer, and Modal's fast container startup and caching for efficient GPU utilization during rollouts. The post emphasizes Modal's ability to provide distinct infrastructure profiles for each RL loop component (rollouts, evals, inference) while maintaining low-cost boundaries between them.

4/29/2026

This post details the implementation of an RL theorem-proving workflow on Modal, leveraging its infrastructure for both GPU-intensive LLM inference (using vLLM) and CPU-bound Lean verification. It highlights the use of per-function images for distinct environments, `.map()` for parallel fan-out of ES perturbations, and Modal Sandboxes for isolated Lean verification. The post also describes a stateless checkpointing mechanism using seed histories and Volumes for base model storage, enabling efficient reconstruction of model states for ES updates.

4/14/2026

This post details how Modal's autoscaling GPU capabilities are applied to AI research, specifically for autoresearch agents. It highlights the dynamic provisioning of GPUs (e.g., H100s) from single instances to large clusters based on workload demands, enabling agents to scale compute resources elastically. The post demonstrates this with the OpenAI Parameter Golf challenge, showing how agents can provision and release GPUs via simple API calls, scale from single-GPU debugging sandboxes to multi-GPU validation runs, and automatically scale down to zero when idle. This provides significant speedups and efficiency gains compared to fixed clusters or single workstations.

2/25/2026

This post introduces Modal's role in accelerating AI research by providing scalable, reproducible, and isolated infrastructure. It highlights how the platform supports AI researchers in areas like automated kernel generation (KernelBench), test-time training for AI systems (TTT-Discover), and automating the machine learning lifecycle (RL-4-MLE). The post emphasizes Modal's GPU fleet, consistent benchmarking capabilities, and container image system as key enablers for these research efforts.

7/11/2025

Introduced multi-node training clusters with the `@clustered` decorator, enabling linear scaling of training runs across dozens of GPUs on multiple hosts via high-speed RDMA interconnect. Added support for NVIDIA B200 and H200 GPUs for serverless LLM inference, offering significant speedups over H100s. Released version 1.0 of the Modal client, emphasizing API stability and predictability, with specific updates including `modal.Volume.read_only`, `--secret` option for `modal shell`, timezone support for `Cron` schedules, and a `--timestamps` flag for `modal app logs`.