GPU-native compiler infrastructure that makes AI inference orders of magnitude faster. No code changes. No compromises.
Trusted by teams in AI infrastructure, autonomous systems, and frontier model development
Traditional compilers weren't designed for the irregular, dynamic computation graphs of modern AI. We built one from the ground up—a compiler that reasons about tensor shapes, memory hierarchies, and GPU microarchitecture at compile time.
1 import ahri
2
3 # Load any model, any framework
4 model = load_model("my-model-70b")
5
6 # One call. GPU-native compiled output.
7 model = ahri.compile(model, target="cuda")
8
9 # Orders of magnitude faster. Zero code changes.
10 output = model.generate(prompt, max_tokens=4096)One function call replaces months of kernel engineering. Ahri ingests your model directly, applies 200+ optimization passes, and emits machine code tuned to your exact GPU.
Fuses operations across attention, MLP, and normalization layers at compile time. Eliminates the memory round-trips that dominate GPU latency.
Profiles SM occupancy, memory hierarchy, and tensor core availability. Generates execution schedules that saturate every compute unit on your specific GPU.
Context-sensitive precision scaling that adapts per-layer, per-head, per-token. Preserves quality with provable bounds—not a static INT8 hammer.
Automatic sharding and pipeline parallelism. The compiler reasons about inter-GPU topology and minimizes PCIe/NVLink traffic at the IR level.
Internal benchmarks across model architectures. Full methodology available under NDA.
Point Ahri at any model from any major framework. 200+ optimization passes. GPU-native machine code out.
Cycle-accurate visibility into kernel execution, memory bandwidth, and compute bottlenecks. Down to individual tensor ops.
Self-contained binaries. Zero runtime dependencies. Native runtime, REST API, or gRPC service with automatic batching.
Multi-GPU orchestration, auto-scaling, intelligent routing. Pay per token. We handle the infrastructure.
Our team has published extensively at top systems and ML venues. We don't just use the state of the art—we advance it. Select papers from our research agenda:
Compiler engineers, GPU architects, and ML researchers with decades of combined experience at leading chip companies, hyperscalers, and research labs.
10+ years leading GPU compiler teams at a top-3 chip company. Deep expertise in CUDA-level optimization and instruction scheduling.
Open-source compiler infrastructure contributor. Previously architected ML compiler backends serving hundreds of millions of users.
Scaled inference infrastructure from zero to billions of daily requests at two of the largest AI labs. Distributed systems specialist.
Quantization and numerical methods researcher. 30+ publications at top-tier systems and ML conferences.
We're onboarding design partners for private beta. Drop your email and we'll be in touch within 24 hours.
No credit card required · Enterprise-grade security