PagedAttention + Continuous Batching in Rust. Achieve <5% memory waste with 50% higher throughput.
Modern inference techniques implemented in Rust for maximum performance and reliability.
Block-based KV Cache management with on-demand allocation. Eliminates memory fragmentation and enables efficient memory sharing.
Dynamic prefill/decode scheduling with priority awareness. Maximizes GPU utilization while maintaining low latency.
Configurable OOM prevention with graceful degradation. Production-ready error handling and monitoring.
Trait-based abstractions for easy customization. Clean separation between CPU scheduler and GPU executor.
121 tests including unit, property-based, and integration tests. Property tests verify critical invariants.
Zero-cost abstractions with memory safety. No GC pauses, predictable performance for serving workloads.
See how PagedAttention compares to traditional allocation strategies.
Install and run your first inference with just a few commands.
Explore our comprehensive documentation to get the most out of Hetero-Paged-Infer.
Get up and running with step-by-step installation and first run instructions.
→Deep dive into the system design, components, and design principles.
→Complete API documentation with examples and use cases.
→Best practices for deploying to production environments.
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