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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AI infrastructure and production SaaS
Cost-cuts, eval harnesses, runtime porting, and LLM evidence packs for SaaS teams running real production workloads.
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Tell us the workload
You ship a product with an AI feature or two. It works in the demo, in CI, and at the design-partner stage. Then production usage grows and the cost line, the latency tail, the model-swap question, or the on-call incident catches up. The team that knows your customers best is rarely the team free to absorb a serving-stack rebuild or a runtime migration. That gap is where we work.
Where AI Work Gets Stuck in Production
Two failure shapes recur. The inference bill grows faster than usage and a model release stalls because the unit economics no longer work, or an AI feature passed launch, drifted, and now shows up as a customer ticket rather than a dashboard alert.
The other shape is reach. A model needs to run somewhere it does not run today (a new accelerator, an edge device, the browser) or procurement, a security review, or a board-level governance question needs structured evidence of which model does which task and why, not a slide deck.
Four Ways We Engage
Four Packs Built for SaaS Teams
Cost, reliability, portability, and trust are different engineering problems with different failure modes, so we run each as a separate fixed-scope engagement, every one ending in a deliverable your team can re-run without us.
Inference Cost-Cut Pack
Cost
Profile-first cost and p95-latency cuts inside the serving stack on the workload you actually run.
Production AI Monitoring Harness
Reliability
Eval harness, slice-level regression, drift checks, and release gates that catch the regression before a customer does.
AI Porting & Deployment Pack
Portability
Get the workload running on a new accelerator, edge device, or browser, with a benchmark and runbook on the target.
LLM Selection Pack
Trust
The eval suite and structured comparison an approval committee can sign against, with a re-run script for every model swap.
Production Inference Cost & Latency
The cheapest AI-cost wins are usually inside the serving stack, not in the model. We profile the workload first (batch sizes, caching, routing, quantisation, kernel choices, serving topology) and surface the changes that move the unit-economics line on the requests you actually run.
Lands in the Inference Cost-Cut Pack: 4–8 weeks, milestone or fixed-price.
Production Reliability & the Eval Harness
Most production regressions are not model-accuracy problems: they are missing evals, missing release gates, missing drift checks, or a workflow that turns a model-version bump into a customer ticket. We build the production-side infrastructure that catches the regression before the customer does.
Lands in the Production AI Monitoring Harness: 4–10 weeks, milestone or fixed-price. Building the underlying eval/benchmark methodology yourself, sustained throughput per precision under a declared optimisation budget, is LynxBenchAI territory.
Runtime, Browser & Silicon Porting
A model that runs on the training cluster is not the same artefact as one that runs on the device, the browser, or a constrained edge box. When the AI path on the target does not exist yet, we run a gated feasibility → porting engagement that ends with a working workload, a benchmark on the target, and a runbook your team can re-run.
Lands in the AI Porting & Deployment Pack: feasibility 2–4 weeks, porting of one workload 4–10 weeks, target-dependent.
LLM Selection & Approval Evidence
Procurement, customer security, and board-level governance increasingly ask the same thing: which model, on which task, with what evidence? We build the eval suite and the structured comparison the approval committee can sign against, and the reproducible re-run script that turns the next model swap into a rerun rather than a fresh engagement.
Lands in the LLM Selection Pack: 3–6 weeks, fixed-price.
Areas of Expertise
Serving-Stack Cost Optimisation
GPU Profiling
Eval Harness Engineering
Slice-Level Regression
Drift Detection
Runtime & Silicon Porting
LLM Evaluation
Approval-Evidence Packs
Featured Case Studies
Production AI engineering, from GPU inference performance modelling to cross-API porting and LLM architecture comparison.
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Enterprise AI Search: Why Retrieval Architecture Matters More Than Model Choice
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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.
Production SaaS AI Engineering FAQ
Why profile the workload before swapping the model or moving cloud?
The cheapest production-AI savings usually live inside the serving stack (batch sizes, caching, routing, quantisation, kernel choices, serving topology), not in the model. We measure the workload first and surface the changes that move the unit-economics line on the requests you actually run, with a measured baseline and a defensible delta rather than a vendor pitch.
How do you catch an AI feature that regresses silently in production?
Most production-AI regressions are not model-accuracy problems: they are missing evals, missing release gates, or a model-version bump that turns into a customer ticket instead of a dashboard alert. We build the eval harness, slice-level regression coverage, drift checks, and release gates that surface the regression before the customer does.
What does it take to run our model on a new accelerator, edge device, or the browser?
A model that runs on the training cluster is a different artefact from one that runs on the device, the browser, or a constrained edge box. When the AI path on the target does not exist yet, we run a gated feasibility → porting engagement that ends with a working workload, a benchmark on the target, and a runbook your team can re-run.
What evidence does an LLM procurement or security review actually need?
Procurement, customer security, and board-level governance ask the same thing: which model, on which task, with what evidence? We build the eval suite and the structured comparison an approval committee can sign against, plus a reproducible re-run script that turns the next model swap into a rerun rather than a fresh engagement.
How is a fixed-scope pack different from buying engineer-weeks?
A pack has a fixed scope, a price tied to the outcome, and ends in a deliverable your team keeps and can re-run: a benchmark replay, an eval re-run script, a deployment runbook, an evidence map. We do not sell engineer-weeks against a backlog; if the question you need to close does not match a pack, we say so.
How We Work With SaaS Teams
Each pack has a fixed scope and a price tied to the outcome, and ends in something your team keeps and can re-run: a benchmark replay, an eval re-run script, a deployment runbook, an evidence map. We do not sell engineer-weeks against a backlog; if the question you need to close does not match a pack, we say so.
Heading into a cost review, a reliability incident, a new deployment target, or an LLM approval? The named pack page is the entry point, or contact us with the question itself and we will route you to the right one.
Keystone deep dives
- Why Cost-Per-Request Is the Right Production AI Optimisation Target
- When Is an AI Feature Ready to Ship? A Release-Readiness Decision Framework
- How to Run a Task-Specific LLM Evaluation That Survives a Procurement Review
- Unit Economics for Production AI: What It Means in Practice
- Inference Benchmarking Examples: Cost-Per-Request Comparisons That Actually Decide
- Release Engineering for AI Features: What It Means in Practice
- LLM Evaluation Metrics: Which Ones Actually Defend a Procurement Choice
- LLM Evaluation Benchmarks Explained: Public Leaderboards vs Task-Specific Evals
- MLPerf Benchmark Explained: What It Measures and Where Task-Specific Evals Take Over
- MLPerf Benchmarks Explained — What They Measure and Where They Fall Short for LLM Selection
- MLPerf Inference Explained: What Its Numbers Mean for Model Procurement
- What an MLPerf Result Tells You (and What It Can't) in an LLM Procurement Eval
- MLPerf Results: How to Read Them, and Where the Benchmark Stops Short
- Multimodal LLM Leaderboard: What Public Rankings Miss for Your Task
- Open-Source Benchmarks: What They Measure and Where They Fall Short in Production
Show all 99 deep dives
- RAG AI Example: How a Retrieval-Augmented Pipeline Works and Gets Optimized
- What an LLM Evaluation Framework Is — Components, Layers, and How It Works
- What Is Chatbot Arena — and Why It Can't Replace a Spec-Driven Eval
- ROC-AUC vs PR-AUC: Which to Report When Evaluating a Model for Procurement
- Machine Learning Compilers: How They Cut Cost-Per-Request in Production AI
- Machine Learning Experiment Tracking: What It Is and How It Feeds Release Readiness
- Machine Learning Explainability: What It Means in a Procurement-Grade LLM Eval
- Machine Learning Model Explainability: What It Means in Practice
- Machine Learning Model Metrics: Which Ones Actually Decide a Serving Config
- Machine Learning Model Monitoring: What It Means in Practice
- Machine Learning Model Monitoring Framework: What Metrics Miss After Deployment
- Machine Learning Model Monitoring Tools: What They Track in Production
- Machine Learning Model Optimization — How It Works and What It Measures
- Spec-ing the Compute Behind a Production AI Feature: Cost-Per-Request Reality
- Speculative Inference (Spec Int): What It Means for AI Unit Economics
- Benchmark Spec Org: Structuring Serving Configs So Cost-Per-Request Comparisons Are Fair
- Spec Power in AI Release Engineering: Defining What a Release Must Prove
- Spec Processor: Turning Eval Requirements Into a Runnable Metric Set
- Spec Rating for LLMs: What a Model Spec Sheet Actually Tells You
- The Spec Web: How an Evaluation Spec Links Task, Dataset, Scoring, and Run Conditions
- Attu for Milvus: Inspecting a Vector Store's Cost-Per-Query in Practice
- Beam Search Decoding: How It Works and What It Means for LLM Output Quality
- LLM Elo Ratings Explained: What Elo Means for a Model Choice
- LMSYS Chatbot Arena Leaderboard: What It Measures for Model Procurement
- LMArena Style Control Explained: How It Corrects Human-Preference Leaderboards
- LMSYS Chatbot Arena Explained — How the LLM Leaderboard Works and What It Measures
- Sentiment Analysis with Machine Learning: What It Means When You Evaluate a Model
- SGLang DeepSeek-V3: Cost-Per-Request Serving Benchmark Walkthrough
- DAC Cables in AI Clusters: When Direct-Attach Copper Beats Optics on Cost-Per-Request
- HPL-MXP Explained: Mixed-Precision Benchmarking and Cost-Per-Request
- HSA Programming Explained: Shared-Memory GPU Compute and What It Means for Inference Cost
- LLaMA LoRA Fine-Tuning: How It Works and What It Costs Per Request
- Lookahead Decoding Explained: How It Cuts Inference Cost-per-Request
- LoRA on Llama: What Adapter Serving Does to Cost-Per-Request
- Serving gpt-oss with SGLang: What It Means for Cost-Per-Request
- SGLang PD Disaggregation: How Prefill/Decode Split Cuts Cost-Per-Request
- SGLang RL Serving: Cost-Per-Request Impact of RL-Tuned Inference Configs
- Agentic AI Benchmark: Measuring Cost-Per-Task When One Request Fans Out to Many
- Agentic AI Benchmarks: What They Measure and When to Trust Them
- Agentic Benchmarks Explained: What They Measure for Tool-Using LLM Agents
- AI Agents for Analytics: How They Work and How to Evaluate Them
- AI Chatbot Leaderboards: What They Measure and What They Miss for Procurement
- AI Models Performance Comparison: How to Compare Candidates for a Procurement Decision
- AI Performance Benchmark: What to Measure So the Number Decides Cost
- AI Performance Comparison: How to Compare Models on a Level Field
- AIME 2024 Dataset Explained: What This Math Benchmark Measures for LLM Evaluation
- AIME 2025 Dataset: What It Is and How to Use It in an LLM Eval
- The AIME Dataset Explained: What It Measures and How to Read AIME Scores in a Model Eval
- Arena Hard Explained: What the Benchmark Measures and Its Limits for Procurement
- Arena Hard Auto Explained: LLM Quality Benchmarks Alongside Cost-Per-Request
- Chatbot Arena (LMSYS): What Its Elo Rankings Do and Don't Tell a Buyer
- Arena-Hard Benchmark Explained: How It Scores LLMs and What It Misses
- What Arena-Hard Is and How It Works — An LLM Evaluation Framework Lens
- Benchmark GitHub Repos: How to Read a Public Eval Harness Before Trusting Its Numbers
- Chain-of-Thought vs Tree-of-Thought: Cost-Per-Request at the Reasoning Layer
- Chat LMSYS Org Explained: How the Chatbot Arena Leaderboard Works
- What Chatbot Arena Is and How It Works — And What It Can't Tell You About Your Workload
- Chatbot Arena Elo Explained: What It Measures and When to Trust It
- What Chatbot Arena Tells You (and What It Can't) for LLM Procurement
- Chatbot Arena Paper Explained: What Elo Rankings Measure and What They Don't
- Confusion Matrix Precision — What It Measures and How to Read It
- Confusion Matrix, Precision and Recall: How to Read the Grid Behind Your Metrics
- What an LLM Safety Benchmark Measures, and What It Can't Prove in Production
- ML Benchmarks Explained: What Public Leaderboards Do and Don't Tell You
- MLCommons and MLPerf Inference: Where Standard Benchmarks Stop Short of Cost-Per-Request
- MLPerf Explained: What Its Benchmarks Measure for AI Procurement
- AWS vs Azure vs GCP for AI and Data Workloads: A Selection Guide
- What Is Inference in AI? A Production Cost Primer
- Machine Learning for Search: How It Works and Its Inference Cost
- SPEC CPU Benchmarks Explained: What They Mean for AI Release-Readiness
- SPEC CPU Benchmark Explained: What It Measures for AI Inference
- BERT Tokenization Explained: WordPiece, Subwords, and What It Means for Evals
- DAC Cables in AI Serving Clusters: How They Work and What They Cost Per Request
- OpenCL Installation for Production AI Inference: A Practical Setup Guide
- LLM Token Calculator: Estimating Inference Cost Per Request
- SGLang OME: What It Is and Its Impact on Inference Cost-Per-Request
- Binary Cross Entropy vs Cross Entropy: What the Difference Means for Your Eval Metrics
- Machine Learning Storage: How It Works in Practice
- SPECfp Benchmarks Explained: What They Measure for Cloud AI Workloads
- AWS Migration Consulting: How It Works in Practice
- Azure Data Factory vs Databricks: Choosing the Right Cloud Data Tool
- Business Intelligence Cost: What Drives BI Spend on Cloud Platforms
- Cloud Data Warehouse Consultant: What They Do in Practice
- Is Milvus Open Source? License, Governance & Cloud Deployment Explained