Inference optimization
More model. Less overhead.
Make inference fit your product. We tune model size and the serving path around your hardware, traffic, and quality requirements — then show you the tradeoffs against a real baseline.
Find your starting point
When to bring us in.
Start with the challenge in front of you. We'll shape the work around it.
What you get
Tangible work.
Lasting capability.
Quantization, distillation, and serving optimization for your hardware, with measured quality, latency, throughput, and memory tradeoffs.
Efficiency only matters if the model still does the job. Agree on the quality bar, then optimize around it.
A workload and hardware baseline
A profile of the current model, runtime, memory use, and request patterns. Capture the quality and performance measures that will guide optimization decisions.
An optimized model candidate
Quantized weights, a distilled model, or another agreed optimization, with its configuration and compatibility notes. Select the candidate using the quality tradeoffs you accept.
A tuned serving configuration
Runtime, batching, caching, and memory settings for the target environment. Include launch instructions and the limits observed under the tested load.
Evidence for the deployment decision
Rerunnable comparisons of output quality, latency, throughput, and memory. Any cost estimate states its hardware rates and utilization assumptions so the economics stay inspectable.
How we work
A clear path. At every step.
From the first brief to the final handoff, here's how the work takes shape.
Before we begin
The details
that matter.
More on scope, collaboration, and what comes next.
Discuss your requirementsWill optimization change model quality?
It can. Quantization and distillation may change behavior, and throughput settings can affect responsiveness. We define acceptable tradeoffs before testing and compare candidates on your tasks. A smaller footprint alone is not the acceptance criterion.
Which hardware and runtimes can we target?
Share the hardware you have or are considering, the model, and the serving stack. We assess compatibility and scope the target environment before committing to an approach. The handoff documents the exact setup tested and any constraints on portability.
Can we start with a model we already use?
Yes. An existing open-weight or custom model is a useful starting point if its license and artifacts support the proposed work. If you only have access through an external API, we first assess available alternatives and what data can be used for evaluation or distillation.
Is this the hosted inference API?
This service is a scoped engineering engagement to optimize a model and its serving environment. Hosted inference and compute capacity are separate product choices. We can help determine which deployment path matches your operational needs, then define any integration work.
How do you estimate savings and ongoing cost?
We measure the workload and compare the baseline with the candidate on the agreed setup. Estimates make request volume, hardware pricing, utilization, and operating assumptions explicit. Actual savings depend on the deployment and traffic; we do not assume a universal reduction.
Keep building