Workload-specific systems work
LLM Inference Engineering
Measure, explain, and improve production inference behavior against the workload and SLO that matter to your application.
Workload first
Why workload characteristics matter
Prompt length, generated tokens, concurrency, and arrival patterns change the serving problem. A useful capacity or latency target starts with the real request distribution, not an isolated peak-throughput number.
Research evidence
A measured concurrency knee.
Our A100 investigation connects repeated vLLM serving measurements to scheduler behavior and GPU profiling. It shows why throughput and latency need to be read together.
Read the investigationCapabilities
From serving trace to operating point.
Latency and throughput
Workload characterization, p95 TTFT, TPOT, throughput, concurrency, and latency SLOs.
Serving behavior
Batching, KV-cache behavior, scheduler decisions, and vLLM or SGLang configuration.
Capacity and design
GPU utilization, hardware/capacity planning, and dedicated deployment design.
Approach
Evidence before intervention.
- 01Measure workload
- 02Identify bottleneck
- 03Tune serving stack
- 04Validate against SLO
- 05Reproduce results
Technical conversation
Discuss an inference workload.
Share the model route, workload shape, latency or throughput target, serving stack, and constraints you can disclose. We will review the context and suggest a next step.