Built for the model
that stopped changing.
When your production inference workload is locked in, commodity GPUs start to look like the wrong tool. Procunit designs custom accelerators matched to your model's exact compute graph — 4x throughput-per-watt, data center scale.
Your stable model is paying for hardware that never stops changing.
Once a production model is frozen, commodity GPU clusters become structurally over-engineered for your workload — optimized for training flexibility you no longer need.
How Procunit Works
From your frozen model graph to evaluation silicon — a four-stage engineering engagement, not a sales process.
Analysis
Extract compute primitives from your frozen model — op types, tensor dimensions, data dependencies.
Synthesis
Map your model's op sequence to a custom dataflow topology — no unused transistors.
Characterization
Simulate and verify performance projections. Report: throughput/watt, latency floor, batch efficiency.
Run
Low-volume evaluation silicon or pre-release eval board — real hardware, real performance validation.
Why custom silicon wins at inference scale.
General-purpose accelerators are engineered for the broadest possible workload. Procunit is engineered for yours.
Accelerator topology mirrors your model's exact layer structure — not a general-purpose matrix engine optimized for every possible op combination.
Engineering projections show 4x improvement over A100-class hardware on stable inference workloads. Your data center power budget goes further.
PCIe Gen 5 x16 form factor. Standard 1U/2U rack mount. OS-level driver stack compatible with your existing ML serving infrastructure.
Full-stack inference, model to metal.
Every layer of the stack is co-designed with the hardware — not adapted from a general-purpose GPU driver model.
Who is Procunit built for?
Production ML teams whose compute budget is dominated by a model that isn't changing anymore.
Stable GPT-style inference, 50M+ queries per day. Model frozen for 8+ months. GPU cluster running at 45% utilization.
Real-time video classification, power-constrained data center. Thermal envelope is the bottleneck, not compute throughput.
Low-latency ranking model, compute budget under pressure. Batch efficiency ceiling reached on GPU — needs model-native hardware.
Is your workload ready for custom silicon?
Start with an evaluation — no commitment, no sales layer.
Request Evaluation