Case-Study: Performance Modelling of AI Inference on GPUs
May 15, 2023
How TechnoLynx modelled AI inference performance across GPU architectures — delivering two tools (topology-level performance predictor and OpenCL GPU…
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Cut Inference Cost Without Guessing Where It Leaks
We profile the workload you actually run, challenge the scope before touching it, and hand you a harness that proves the saving. Not a slide that claims it.
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Tell us the workload
Production AI gets expensive in predictable ways. GPU utilisation drifts low while bills rise, latency makes a feature feel unusable, or serving cost eats margin as usage grows. The default response, swap the model, change cloud, hire a platform team, often skips the cheapest wins, which usually sit in the serving stack and the GPU performance engineering layer rather than in a bigger budget. We profile first, and if the real fix is smaller than the brief, we will tell you. You leave with fewer dollars and fewer milliseconds per request, and a harness that reproduces the numbers.
How the Engagement Runs
An Audit On-Ramp, Then the Optimisation Sprint
Two phases, one workload. The Audit finds and ranks where the cost leaks and, if the real fix is smaller than the brief, says so before you spend a sprint. The Optimisation Sprint is where the implementation happens: it builds only the high-confidence changes the Audit ranked. Pricing is fixed on the Audit and milestone or fixed-price on the Sprint, a measured delta on a defined workload, not engineer-weeks against a backlog.
Audit On-Ramp
2–8 weeks
The low-commitment way in. We profile the workload and hand back a ranked optimisation backlog, an ROI model, and a reproducible baseline harness.
Optimisation Sprint
4–8 weeks
Where the implementation happens. We build the high-confidence changes the Audit ranked, then hand over a before/after delta report and the harness that produced the numbers.
What You Keep
A reproducible before/after report on a fixed workload: cost-per-request delta, p95 latency delta, and GPU utilisation delta where it applies, plus the harness that produced the numbers. You re-run it on representative inputs after handover and the numbers reproduce within an agreed tolerance, or we are not done. It survives the engagement, binds future regressions, and protects you from optimisation work that leaves behind a faster system and nothing replayable.
What the Harness Is
For the buyer, the harness turns "we optimised it" into something an engineering organisation can defend, regress against, and reuse: a reproducible workload definition with version pins, a replay script that runs the workload against a target deployment, profiler traces for the baseline and the post-change run, before/after logs on the metric set agreed at Audit time, and a short README that lets a different engineer re-run the rig. That is the deliverable the pack is priced against. The discipline behind holding those numbers steady, performance read as a property of the whole stack under sustained load, is the measurement methodology we develop in the open at LynxBenchAI.
What This Pack Covers
Serving-Stack Optimisation
Batching & Caching
Request Routing
Quantisation
Kernel Selection
GPU Profiling
Cost-per-Request Targets
p95 Latency Targets
AI Video & Transcoding Pipelines
Not Sure This Is the Right Pack?
If the runtime, silicon, or form factor has no working AI path yet, that is the AI Porting & Deployment Pack. If the model gives wrong answers or there are no release gates catching regressions, that is the Production AI Monitoring Harness. If a committee needs LLM model-comparison evidence, that is the LLM Selection Pack. If the question is whether you are ready to deploy at all, that is the AI Readiness Scorecard.
How We Know This Works
Low-level inference-performance engineering, from GPU cost modelling to embedded video coding. These engagements pre-date the packaged Sprint and stand as bridged proof.
Case Study - Embedded Video Coding on GPU (Under NDA)
Apr 15, 2020
TechnoLynx built a CUDA-based H.264 encoder on a Jetson Nano-class embedded GPU for an automotive edge startup, targeting ≤5% CPU usage across 4+…
Read moreFeatured Articles
How inference cost actually comes down: profiling first, cost-per-request benchmarking, and latency measurement that holds up.
How to Improve GPU Performance: A Profiling-First Approach to Compute Optimization
May 5, 2026
Profiling must precede GPU optimisation. Memory bandwidth fixes typically deliver 2-5x more impact than compute-bound fixes for AI workloads.
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Inference Benchmarking Examples: Cost-Per-Request Comparisons That Actually Decide
Jun 12, 2026
How to benchmark LLM inference serving configs on cost-per-request and p95 latency, not tokens-per-second, so the comparison maps to margin.
Read more
Latency Testing for AI Inference: A Methodology Beyond Best-Case Numbers
May 13, 2026
How to design a latency-testing protocol that exposes batch, concurrency, and tail-percentile behavior under realistic AI inference load.
Read moreClient Testimonials
TechnoLynx delivered the project on time and provided quality outputs that met the client's expectations. The team was proactive in providing ideas and suggestions, and they were careful at properly planning the tasks. The client also praised the team's expertise in GPU programming and AI.
TechnoLynx's skill in low-level software development was impressive. TechnoLynx was able to create four prototypes with common components and an interface for easy maintenance. The client was extremely happy with the solution's speed. Moreover, their communication was seamless and straightforward.
TechnoLynx's unique aspect is that they're able to transform complex theories into practicable and applicable results. TechnoLynx provides research reports and architecture planning documents. The team is able to transform complex theories into practicable and applicable results. TechnoLynx's project management is strong and delivers work on time without hardware issues, being responsive through virtual meetings.
I’m delighted with our collaboration with their team. Thanks to TechnoLynx's work, the client has been able to co-author two patents. They lead responsive project management to solve problems quickly. The team also praises their skilled and knowledgeable team.
We had high-efficiency meetings. TechnoLynx’s work resulted in a successful breakthrough, and their input improved the client’s app. Their flexible and organised project management cultivated a healthy collaboration experience. Ultimately, their professionalism and commitment were impressive.
Inference Cost-Cut Pack FAQ
Why profile the workload before swapping the model or changing cloud?
The cheapest wins are usually inside the serving stack: batch sizes, caching, routing, quantisation, kernel choices, serving topology, not in model substitution or a cloud move. We measure the workload first and surface the changes that carry the economic weight on the requests you actually run, with a measured baseline and a defensible delta.
What does the before/after report actually measure?
A cost-per-request delta, a p95 latency delta, and a GPU utilisation delta where it applies, on one fixed workload, plus workload-specific extras such as cost-per-token or transcoding frame-rate. The harness that produced the numbers ships with the report.
Do I keep the harness after the engagement?
Yes. The harness is the deliverable: a reproducible workload definition, a replay script, profiler traces, before/after logs on the agreed metric set, and a README a different engineer can follow. You re-run it on representative inputs after handover and the numbers reproduce within an agreed tolerance, or we are not done.
Is the Pack priced as engineer-weeks?
No. Pricing is fixed against the Audit and milestone or fixed-price against the Optimisation Sprint. The Optimisation Sprint is a measured delta on a defined workload, not engineer-weeks against a backlog. When a deep-GPU sub-scope genuinely needs time-and-materials, we record it as a separate scope rather than re-pricing the headline.
What if the AI path doesn't exist on my target yet?
That is porting, not cost-cutting. If the runtime, silicon, or form factor has no working AI path yet, the right engagement is the AI Porting & Deployment Pack. The Inference Cost-Cut Pack assumes the workload already runs in production on a mature stack.
Start a Conversation
If your workload runs in production on a mature stack and you have access to traces, representative inputs, and cost data, the Audit is the right entry point. The AI-infrastructure / SaaS, media & telecom, and retail crosswalks all route inference-cost work through this pack.
Tell us the workload shape, the runtime, and what “good” looks like for cost and p95 latency. If the cheapest fix turns out smaller than the brief, that is the first thing you will hear. Start a conversation.
