Push your compute to the limit — Fixstars Performance Engineering
Performance engineering is the systematic pursuit of software speed and efficiency — translated directly into business outcomes.
More throughput. Faster response. Less power for the same work. Lower compute cost. Optimization pays off in many shapes.
Raise data processing bandwidth and increase throughput.
Shorten user response time and cut latency.
Improve power efficiency. Get more performance per watt.
Improve cost performance. Lower TCO.
From embedded devices to supercomputers — at every scale, performance engineering is a core technology that can make or break a product or service.
Performance work isn't new. But several shifts are converging, and getting performance out of software matters more than ever.
AI is being built into every product and service, and the compute it needs keeps growing. Meeting that demand with limited compute and power takes efficient software.
AI processors are diversifying fast — from GPUs to NPUs to custom chips. The same AI model needs very different optimization depending on the chip it runs on.
AI agents are changing how software gets built. Now that the measure-and-improve loop can be handed off to agents, performance work is easier to take on.
From autonomous driving to inspection systems, more AI now runs directly on devices. Getting the required performance out of limited compute takes real expertise.
Measure right, then fix efficiently. That loop is what pushes performance up. Agentic engineering is a development approach where AI agents write the code while humans direct the work and own its quality. With agents running the cycle, you can reach your performance target in less time.
The theoretical peak is the ceiling, and the closer you get, the harder each gain becomes. So decide up front what you're aiming for.
Measure in an environment identical to production — or as close to it as possible.
Minimize measurement overhead — or account for it explicitly when interpreting results.
Decide in advance how to handle noise. Identify the cause first; if it's truly measurement error, summarize with the right statistic (median or mean).
Because the measurements show whether each change helped, this cycle can run without waiting on human judgment.
Optimizing code that isn't the bottleneck pays off little. Find the bottleneck and focus your effort there.
Sometimes the biggest win isn't a faster path — it's removing the step entirely. Whether it can be removed is a call for someone who knows the spec.
Agents chase the target number, so they may change measurement conditions or give up accuracy just to make the numbers look better. Reviewers make sure no such changes slip through.
Changes are merged into the production codebase only after they pass review.
Performance engineering for AI/LLM development and deployment — delivered as services and as products. From embedded systems to GPU workstations, pick what fits your problem.
Optimization and embedded device porting, delivered in one-third the usual time — in a secure environment.
Build an embedded software development environment where AI runs without sending code outside the company.
Install on your GPU servers. AIBooster continuously profiles AI/LLM workloads, surfaces bottlenecks, and reduces GPU cost.
Our engineers are fluent in the architectures of CPU, GPU, FPGA, and other processors — and they take the whole journey: code analysis, bottleneck identification, algorithmic refinement. Cloud provider infrastructure support and SI work usually stop short of optimizing inside the software itself. Fixstars has specialized in software acceleration for 20+ years, and our core strength is pulling hardware to its limit. The result: 100+ clients, 99%+ continued-engagement rate.
Step one is measuring GPU utilization accurately and identifying the bottleneck. In many cases GPU compute units are underused, and inefficiency hides in data transfer and memory access patterns. Fixstars AIBooster installs on your GPU servers, continuously profiles AI/LLM workloads, and auto-detects bottlenecks. Use that data to drive optimization — GPU utilization goes up and infrastructure cost goes down.
Combine model compression (quantization, distillation, pruning) with runtime optimization. We design around the model architecture and the deployment target (GPU, CPU, edge) at the same time, holding accuracy loss to a minimum. For generative AI services and real-time systems, this delivers response times that don't compromise user experience.
Training speedups come from several layers: compute optimization, data pipeline efficiency, and communication optimization for distributed training. Fixstars combines hardware-aware kernel optimization, memory access pattern improvements, and the right numerical precision — cutting training time without sacrificing accuracy. The model development cycle accelerates, time-to-market shortens, and GPU cost drops at the same time.
ROI is high. In most cases, the infrastructure cost savings far exceed the investment. Improving GPU utilization means the same work runs on fewer GPUs. Optimizing inference reduces server count. Either way, the monthly cloud bill comes down significantly. Real numbers depend on the workload and the environment — ask us about your specific case.
Automotive (autonomous-driving AI), life sciences (medical imaging AI), finance (risk computation, high-frequency trading), manufacturing (inspection, simulation), logistics (blending optimization), and more. The common thread: domains where heavy compute directly drives competitive advantage. From AI/deep learning acceleration to quantum computing enablement, we contribute to business outcomes by pulling everything out of the machine.
