Next-generation inference optimization for multi-agent LLM systems. Reduce latency, maximize throughput, and unlock the full potential of collaborative AI.
Modern AI applications are evolving beyond simple chatbots to complex multi-agent workflows, but existing inference engines aren’t built for this reality.
Models idle while waiting for others, wasting expensive GPU cycles and increasing end-to-end latency.
KV caches can’t be shared between generator and verifier models, requiring redundant computation.
Static scheduling policies fail to adapt to the dynamic patterns of agentic workflows.
FluxInference optimizes inference at both the kernel and scheduling levels, purpose-built for agentic workloads.
Reduce launch overhead for variable-length requests. Perfect for agentic workflows with temporal gaps and bursty patterns.
Intelligently share KV caches between models through pointer-based sharing and synchronized eviction policies.
Stream tokens from generator to verifier in real-time. Enable spec-decoding immediately when prefix cache hits.
Dynamically detect and utilize idle models. Balance prefill and decode workloads across your infrastructure.
Seamlessly coordinate generator, verifier, and reward model workflows. Optimize for code generation, reasoning chains, and complex iterative tasks.
Fluxible topology options: same-GPU for maximum cache sharing, multi-GPU for independent execution, or hybrid approaches for optimal performance.
Works with vLLM, TensorRT-LLM, and other leading inference engines. Drop-in optimization without rewriting your stack.
Built on cutting-edge research in systems and machine learning.
Persistent kernels and megakernel fusion reduce launch overhead and improve GPU occupancy for agentic workload patterns.
Novel strategies for KV cache sharing, synchronized eviction, and cross-model prefilling in heterogeneous scenarios.
Validated on Spec-Bench, AgenticSQL, and SWE-Bench with real-world code generation and verification workloads.
Cursor, GitHub Copilot, and other coding tools with generation + verification workflows.
Complex workflows with planner, agent, and reviewer collaboration patterns.
Applications requiring iterative refinement, verification, and reward modeling.
Maximize GPU utilization and reduce serving costs for agentic AI workloads.
Join our early access program and be among the first to experience next-generation agentic inference.