GPU Performance Engineering

TechnoLynx stands out through pioneer-level leadership and proprietary innovation. Our founder, Balázs Keszthelyi, architected the VC-6 codec and built the first OpenCL benchmark adopted by major GPU vendors. This deep-tech heritage allows us to solve optimization challenges that standard engineering firms cannot.

TechnoLynx provides cross-platform optimization for a wide range of hardware and frameworks, including:

• Frameworks: CUDA, OpenCL, Vulkan, Metal, and DirectX.
• Hardware: NVIDIA, AMD, Apple Silicon, and edge devices.

We ensure high-performance execution for AI and computer vision pipelines regardless of the underlying architecture.

We achieve low-latency AI inference through three core techniques: 1. Model Quantisation: Reducing precision without losing accuracy. 2. Pruning: Removing redundant parameters. 3. Custom Pipelines: Utilizing TensorRT and ONNX for maximum throughput on specific GPU architectures.

Yes, TechnoLynx designs future-proof, vendor-agnostic solutions. We ensure your GPU software is portable across different vendors (e.g., migrating from NVIDIA to AMD) and operating systems, providing long-term flexibility and scalability.

Yes, our XR services include:

• GPU-accelerated high-fidelity rendering.
• Real-time tracking optimization.
• Immersive simulation and digital twins for industrial training.

Yes, we tune pipelines for the entire AI lifecycle. This includes multi-GPU setups for scalable training and using optimized runtimes like TensorRT for efficient deployment and inference.

We architect distributed solutions that achieve near-linear scaling. By optimizing inter-GPU communication, we enable high throughput for large-scale simulations, massive rendering tasks, and complex AI models.

We treat memory as a primary resource. Using advanced profiling, we analyze memory access patterns to minimize latency and eliminate bottlenecks, which is critical for processing large datasets in real-time.

Yes. Our auditing services include kernel profiling, shader analysis, and end-to-end system stress testing. We provide clients with actionable data and recommendations to maximize their hardware investment.

Most of the time — once a workload is profiled honestly. Micro-optimisations rarely beat changing the algorithm or the data layout, because the biggest GPU gains usually come from removing whole memory round-trips or restructuring the dependency graph rather than from squeezing a few percent out of a single kernel. We always profile first to identify which level of leverage actually applies — see how to profile GPU kernels and when algorithmic restructuring beats kernel tuning.