AI ENGINEERING

Inference Optimization

Lower serving costs. Balance throughput and latency.

As AI workloads grow, inference efficiency becomes increasingly important to operating costs. Effective optimization means serving more requests efficiently while meeting the response times your application requires.

FROM ANALYSIS TO IMPLEMENTATION

Where we can help.

Tune for your workload

For autoregressive language models, prefill is typically compute-bound, while decode is often limited by GPU memory bandwidth. Their balance varies with the model, input and output lengths, and request concurrency.

Serving stacks such as vLLM and SGLang provide a strong foundation. Tuning them for your workload can still unlock substantial gains.

We optimize batch scheduling, KV cache storage and reuse, and performance-critical kernels—including custom kernels where needed. Every change is evaluated against your latency, throughput, and cost targets under realistic workloads.

Choose hardware for cost efficiency

The highest-end GPU is not always the most cost-effective choice. Depending on your model and workload, a less expensive GPU can deliver better value while meeting your performance requirements.

We benchmark hardware options against your actual serving needs, considering memory capacity, bandwidth, compute capability, and utilization. Choosing the right GPU and configuration can make a substantial difference to serving costs.

LET'S WORK TOGETHER

Experience built over years

Our specialists have been optimizing inference since before vLLM was introduced. We bring that experience to your project, from workload analysis and hardware selection through implementation, helping turn performance improvements into lower operating costs.

Discuss your project

Projects are scoped and quoted individually.