Push your compute to the limit — Fixstars Performance Engineering
Fixstars is a performance engineering company. We specialize in AI/LLM inference and training acceleration, GPU utilization improvement, and software optimization. 20+ years of experience, 100+ clients, and a 99%+ continued-engagement rate.
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 core technology that decides whether a product or service wins.
Performance work isn't new. But with generative AI, practicing it strategically — as "performance engineering" — matters more than ever.
Four forces push the bar for "faster, cheaper, more efficient" higher than ever before.
If training optimization hits the same accuracy with less compute, you can iterate more — and ship new models and services before competitors do.
Large AI model training and inference rack up huge bills. Performance optimization drives down operating cost directly — and makes the business sustainable.
In real-time applications, a small delay hurts UX — sometimes safety too.
Building systems that run on less energy is part of being a responsible company.
Generative AI splits into four patterns along the "scale × use case" axes. Each pattern has its own performance target — low latency, high throughput, low memory, or large-scale distributed training.
Autonomous driving systems, IoT device sensor processing, smartphone AI assistants.
Inference API services, generative AI services, agentic AI services.
On-device fine-tuning, federated learning.
Fine-tuning, continued pre-training, foundation model training.
Performance engineering is a cycle of measure-then-improve. Measure right first. Fix the right thing next. Repeat. That loop is what pushes performance up.
Measure on the production environment — or as close to it as possible.
Minimize measurement overhead — or account for it explicitly when interpreting results.
When you see noise, identify the cause. If it's truly measurement error, summarize with the right statistic (median or mean).
Theoretical peak is the ceiling. The closer you get, the harder each gain becomes.
Optimizing non-dominant work pays poorly. Identify the bottleneck and pour the budget there.
Sometimes the biggest win isn't a faster path — it's eliminating the work entirely.
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.
Build an embedded software development environment where AI runs without sending code outside the company.
Port, optimize, and validate AI models on target embedded hardware — in a secure environment.
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.
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.
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.
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.
