Inference Optimization
Our infrastructure layer optimizes model execution against the actual production objective rather than treating raw tokens-per-second as the only useful metric.
The governing metric
Optimization can include quantization, caching, batching, model selection, context reduction, and hardware tuning.
The governing metric is quality per unit of cost and latency.
Optimization surface
Each technique changes a different part of the execution path. Quantization can reduce memory. Batching can improve throughput. Caching can avoid recomputation. Context reduction can lower token processing. Model routing can prevent expensive models from handling workloads that do not require them.
- Workload
- Quality requirement
- Model + context + precision
- Batch / cache / hardware configuration
- Latency + throughput + cost
These decisions interact. A smaller context may reduce latency but omit necessary evidence. A more aggressively quantized model may save VRAM while degrading a sensitive task. Large batches can increase throughput while hurting interactive response time.
Optimize the system, not the benchmark
A serving optimization is useful only if the end-to-end workload remains correct enough. That means measuring the deployed configuration rather than relying on isolated infrastructure benchmarks.
The same model can behave differently after changes to precision, runtime, context strategy, or hardware backend.
Caching boundaries
Caching can be powerful when inputs or intermediate results repeat, but stale or permission-sensitive information should not be reused indiscriminately. The cache key and invalidation policy become part of correctness.
Economic objective
The fastest path is not always the cheapest, and the cheapest model is not always the cheapest business outcome. Our optimization layer therefore seeks the configuration that clears the workload’s reliability threshold while minimizing unnecessary compute, latency, and infrastructure consumption.
End-to-end measurement
Infrastructure optimization should be measured from the workload boundary rather than only inside the model runtime. Reducing model latency by 30% may have little user impact if retrieval or an external tool dominates the request. Conversely, shortening context construction may reduce both inference cost and model latency without changing the serving engine at all.
We therefore decompose latency and cost across the path: queue time, retrieval, preprocessing, inference, tool calls, validation, and fallback.
The same decomposition prevents optimization from moving cost invisibly elsewhere. A cheaper first-pass model that triggers substantially more retries or human review can increase total workflow cost even while its per-token price is lower. Optimization is accepted when the complete workload improves under the required reliability constraint.