XFLOPS helps enterprises build AI applications with open tools, frameworks, and best practices. Flame is the community's core project: a distributed engine for secure, cost-effective, and high-performance elastic AI workloads.
Scale AI workloads dynamically based on demand with runtime-aware scheduling and resource optimization.
Use session-based access, isolated runtime environments, and secure component communication for elastic workloads.
Optimize resource utilization and workload distribution so AI systems can run efficiently at scale.
Support varied infrastructure, including CPU, GPU, and accelerator-backed environments.
Improve throughput and roundtrip time for task-heavy AI systems through distributed execution.
Build portable runtime infrastructure for cloud, on-premise, and hybrid deployments.
Flame is the core XFLOPS project for elastic AI workloads. It provides the distributed runtime mechanisms behind sessions, task scheduling, executor reuse, object caching, and secure service integration for agents, reinforcement learning, generated-code execution, and more.
Technical walkthroughs and examples from the XFLOPS community and Flame project.
A walkthrough of the Candle Based Rust example in Flame, showing that the main code change is wrapping local inference in a Flame service macro, while flmctl deploy handles packaging and registration.
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A technical blog on the TorchRL DQN example in Flame, covering distributed collectors, Flame-backed TorchRL replay storage, sharded sampling, and an Apple M4 Podman test run.
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A technical blog on Flame's replay-buffer example, covering patch_object delta updates, parallel sampling, and the handler-plus-data programming model.
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