GPU Performance Engineering

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2019
Founded in
95%+
Client Satisfaction Rate
20+
Successful Projects Delivered

Why Choose Us?

Tailored solutions, not one-size-fits-all.

We're not just your tech team — we're your thought partner. Every collaboration begins with deep understanding, followed by sharp execution.

Custom Models

Founder-Led GPU Expertise

GPU

Balázs Keszthelyi built the first OpenCL benchmark adopted by major GPU vendors and architected the VC-6 codec. With decades of GPU-first innovation, TechnoLynx is led by one of the field’s most credible pioneers.

Supervised Design

Algorithm Redesign for Speed

GPU

We don’t just move code to GPUs, we rethink the algorithm. From simulation engines to custom AI pipelines, we redesign logic to unlock real-world speedups that straight-up GPU porting can’t deliver.

Cross-Disciplinary

Full-Stack Performance Tuning

GPU

GPU speed means nothing if the rest of your stack lags. We optimise across CPU, memory, and I/O to eliminate bottlenecks and ensure your system performs as a whole.

Scalable Solutions

AI + GPU: Smarter, Faster Systems

GPU

We blend custom-coded logic with AI inference, optimised for GPU performance engineering. The result: intelligent systems that are fast, efficient, and ready for real-time deployment.

Frictionless Onboarding

Cross-Platform GPU Porting

GPU

From CUDA to Metal, OpenCL to Vulkan: we make your code run fast on any GPU. We’ve helped clients unlock Apple silicon, AMD, and NVIDIA platforms with precision.

Multi-GPU Optimisation

Visual Computing, Not Just Compute

GPU

We don’t just accelerate code, we visualise it. From GPU-accelerated simulations to 3D rendering and XR, we bring deep graphics expertise to projects that need both performance and visual clarity.

Area of Expertise

GPU Algorithm Redesign
Full-Stack Performance Tuning
Cross-Platform GPU Porting
Real-Time Rendering
AI-Accelerated Visual Computing
XR Simulation & 3D Graphics

Technology Stack

CUDA
OpenCL
Vulkan
Metal
DirectX
TensorRT
ONNX
SYCL
HIP
DX12
WebCompute
WebGL

Client Testimonials

Frequently Asked Questions

What makes TechnoLynx unique in GPU performance engineering?

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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.

Which GPU hardware and frameworks does TechnoLynx optimize?

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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.

How does TechnoLynx accelerate AI models for real-time inference?

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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.

Does TechnoLynx support cross-platform GPU deployment?

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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.

Does TechnoLynx develop GPU-accelerated XR (AR/VR/MR) applications?

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Yes, our XR services include:

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

Does TechnoLynx optimize both AI training and inference?

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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.

How does TechnoLynx handle multi-GPU and distributed systems?

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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.

How does TechnoLynx optimize GPU memory and bandwidth?

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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.

Does TechnoLynx offer GPU benchmarking and auditing?

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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.

Featured Case Studies

Explore our latest thought leadership on innovation, technology, and industry best practices.

Case-Study: V-Nova - GPU Porting from OpenCL to Metal

Case-Study: V-Nova - GPU Porting from OpenCL to Metal

Dec 15, 2023

Case study on moving a GPU application from OpenCL to Metal for our client V-Nova. Boosts performance, adds support for real-time apps, VR, and machine learning on Apple M1/M2 chips.

Read more
Case-Study: Performance Modelling of AI Inference on GPUs

Case-Study: Performance Modelling of AI Inference on GPUs

May 15, 2023

Learn how TechnoLynx helps reduce inference costs for trained neural networks and real-time applications including natural language processing, video games, and large language models.

Read more
Case Study - Accelerating Physics -Simulation Using GPUs (Under NDA)

Case Study - Accelerating Physics -Simulation Using GPUs (Under NDA)

Jan 23, 2020

TechnoLynx used GPU acceleration to improve physics simulations for an SME, leveraging dedicated graphics cards, advanced algorithms, and real-time processing to deliver high-performance solutions, opening up new applications and future development potential.

Read more

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Precision Choices Are Constrained by Hardware Architecture

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Mixed Precision Works by Exploiting Numerical Tolerance

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Quantization Is Controlled Approximation, Not Model Damage

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