Ensigncode's GPU, CUDA, and HPC practice builds and optimizes GPU-accelerated systems on NVIDIA hardware, from custom CUDA kernels and TensorRT inference to multi-GPU clusters and Blackwell B200 and GB200 NVL72 tuning.
Modern AI, data, simulation, and real-time workloads demand compute far beyond what CPUs deliver. We engineer the full GPU stack: CUDA kernels, profiling and optimization, inference acceleration, cluster infrastructure, and architecture-specific tuning.
Our engineering-first approach targets measurable outcomes, higher GPU utilization, lower latency, and reduced infrastructure cost, not theoretical benchmarks.
Explore GPU, CUDA & HPC services
CUDA Engineering
High-performance CUDA kernel development and GPU acceleration for production workloads.
Learn more →AI Performance Engineering
Reduce GPU costs and accelerate AI workloads with CUDA and TensorRT.
Learn more →CUDA Performance Profiling
Eliminate GPU bottlenecks with Nsight profiling and kernel optimization.
Learn more →CUDA Computer Vision Optimization
Accelerate image and video processing with CUDA and OpenCV.
Learn more →TensorRT Optimization
FP16 and INT8 inference acceleration and GPU memory reduction.
Learn more →GPU Engineering
GPU infrastructure, clusters, and high-performance computing.
Learn more →High-Performance Computing (HPC)
Scientific computing, simulations, and parallel architectures.
Learn more →AI Inference Optimization
Faster AI inference with lower GPU infrastructure costs.
Learn more →LLM Inference Infrastructure
Private LLM hosting, vLLM deployment, and scalable serving.
Learn more →NVIDIA Blackwell B200 Optimization
Maximize performance from NVIDIA Blackwell B200 infrastructure.
Learn more →GB200 NVL72 System Tuning
Optimize GB200 NVL72 for maximum AI performance.
Learn more →FP4 Precision Inference
Cut inference costs with FP4 low-precision optimization.
Learn more →What we deliver across the GPU stack
- Custom CUDA kernel development in C and C++
- Nsight profiling and bottleneck analysis
- TensorRT, FP16, INT8, and FP4 inference optimization
- Multi-GPU cluster setup and NVIDIA server configuration
- LLM inference infrastructure with vLLM
- HPC architectures combining CUDA and OpenMP
- Blackwell B200 and GB200 NVL72 system tuning
- Computer vision acceleration with OpenCV and YOLO
FAQ
Everything you need to know
What GPU services does Ensigncode offer?
We cover CUDA engineering, GPU profiling, TensorRT and inference optimization, GPU infrastructure and clusters, HPC solutions, LLM inference, and tuning for NVIDIA Blackwell B200 and GB200 NVL72.
How much faster can GPU optimization make my workload?
It depends on how parallel the workload is. Typical gains range from 3x to 15x for data processing, 5x to 20x for AI training, and 10x to 50x for image and video pipelines.
Which NVIDIA hardware do you work with?
We work with H100, A100, RTX, and DGX systems, and we provide architecture-specific tuning for Blackwell B200 and GB200 NVL72 platforms.
Can you reduce our GPU inference costs?
Yes. Through TensorRT, quantization, vLLM, and better GPU utilization, we raise throughput per GPU so you can serve the same traffic on fewer GPUs.
Let us build it together
Maximize Performance. Minimize GPU Costs.
Whether you are optimising CUDA kernels, scaling multi-GPU clusters, or deploying LLM inference, our engineers help you ship faster and spend less. Get a free performance assessment of your current setup.