CausalFlow provides an end-to-end inference platform built for AMD GPUs. Five products connect bare-metal infrastructure, GPU kernels, cluster orchestration, model serving, and workload intelligence so teams can bring production models online, operate them at fleet scale, and support demanding agentic applications.
CF BluePrints provides validated infrastructure designs for production inference clusters. Its software-defined network fabrics align compute, memory, and interconnects with the requirements of target models, while continuous hardware telemetry helps operators detect and mitigate GPU failures.
By encoding the cluster topology and system configuration as a repeatable blueprint, teams can bring new AMD capacity online with a resilient foundation for production workloads.
CF Kernels provides highly optimized primitives that expose the performance capabilities of AMD GPUs. Low-precision and multi-GPU execution paths are tuned against production models to remove the bottlenecks that limit throughput and latency in real inference workloads.
CausalFlow also develops AveLang, an AI-powered, agentic kernel-design system that automates kernel generation, optimization, and hardware-aware tuning. AveLang targets roofline-class performance for critical inference operators, including GEMM, FlashAttention, and Mixture-of-Experts kernels on AMD GPUs.
CF InferenceOS is the cluster control plane for production inference. It pools GPU compute and memory tiers—including HBM, DRAM, and SSD—and coordinates disaggregated inference, scheduling, and hierarchical KV-cache management across the cluster.
Instead of operating each server in isolation, teams gain a shared inference fabric that can place models, absorb changing workloads, and scale execution across an AMD GPU fleet.
CF Serving exposes optimized AMD execution paths through production-ready model endpoints. It combines FP4 quantized serving, speculative decoding, and workload-aware runtime decisions with the open serving ecosystem.
Teams can bring new models online quickly while maintaining high throughput, predictable latency, and an operational path from model weights to a reliable API.
CF AgentGrid is the workload layer for production agentic applications. It combines semantic-aware model routing, instant agent sandboxes, and agentic knowledge management to coordinate stateful, multi-step workloads across models and serving paths.
Speculative execution compresses and accelerates agent workflows, enabling up to 5× more agentic requests on the same hardware resources.