Production-scale inference teams, built for the long run.
Procunit is designed for ML infrastructure teams who have cleared the model experimentation phase. Not teams training new architectures — teams serving a stable model at production load, where every watt and every millisecond of latency has an economic cost attached to it.
The teams we're building for.
Customer details are confidential (NDA culture). These profiles represent workload archetypes from our evaluation engagements.
Serving a production text classification and summarization model — GPT-2-scale architecture, frozen at 1.3B parameters. 50M+ queries per day. Model has been in stable production for 9 months with no architectural changes planned. GPU cluster operating at 47% average utilization with full cost capitalization. The VP of Infrastructure's mandate: reduce TCO without a throughput regression.
Classifying real-time video frames from 80,000+ concurrent streams in a power-constrained colocated data center. A100 PCIe cards are running at 400W average draw executing a ResNet-variant model that has not changed architecturally in 14 months. The thermal ceiling is hit — adding GPU capacity requires a facility power upgrade that isn't in budget. Power-per-classification is now the primary engineering metric.
Two-tower retrieval plus cross-encoder re-ranking at sub-60ms p99 latency, serving 200M daily active users. The p99 latency constraint caps effective batch size at 16–32 queries — which means GPU SM utilization never exceeds 35% during peak load. Each marginal throughput improvement requires disproportionate hardware spend. Infrastructure cost is growing faster than the revenue line it supports.
Where custom silicon makes the clearest case.
When your language model stopped training but never stopped costing.
A100-class GPUs are architected for the full matrix operation space of transformer training. For a frozen inference-only model, 40–60% of that matrix engine sits idle — executing op types that simply don't appear in your inference graph.
Procunit's compiler identifies every op in your model, discards the irrelevant hardware surface, and builds a dataflow topology around what remains. For large-scale NLP workloads, this typically yields a 3.5–4.3x improvement in tokens-per-watt at comparable latency.
Power-constrained data centers and the thermal ceiling problem.
Vision inference workloads are particularly power-sensitive because they often run in constrained rack environments — smaller facilities without hyperscale power delivery. When a 400W GPU is running at 40% utilization to execute a 10ms-latency vision task, you're paying 250W in wasted capacity.
A model-specialized ASIC sized to your model's working set draws a fraction of that TDP. The 75W Procunit variant targets data center operators where rack power density is the binding constraint.
The batching efficiency ceiling on GPU.
Recommendation and ranking models have strict latency requirements that limit effective batch size. When you can only batch 8–32 queries at a time due to p99 latency constraints, GPU SM utilization drops significantly below the peak numbers in marketing materials.
A dataflow architecture designed around your model's actual batch size and query pattern doesn't pay the scheduling overhead tax that GPU warps impose at small batch sizes. This is where model-specialized silicon shows the clearest advantage.
Does your workload fit the profile?
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