How to Hire the Right CUDA/GPU Engineering Partner (and What to Ask Them)
A practical evaluation guide for outsourced CUDA/GPU engineering partners: what to look for, what to ask, and the red flags to avoid.
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Custom CUDA Kernel Development: When Off-the-Shelf Libraries Aren't Enough
Where library defaults leave performance on the table, and the profiling-driven process that makes custom kernel development worthwhile.
Read article →CUDA for Computer Vision: Real-Time Video AI at Scale
Why real-time video AI pipelines need the whole pipeline tuned, not just the model, and where CUDA acceleration matters most.
Read article →CUDA Profiling with Nsight: Finding and Fixing GPU Bottlenecks
A practical workflow for using Nsight Systems and Nsight Compute to find the real bottleneck before optimizing anything.
Read article →vLLM in Production: Deployment and Performance Tuning on H100/B200
The tuning decisions vLLM's defaults leave on the table, from batching configuration to quantization, on both H100 and B200.
Read article →FP4 Precision Inference: What It Is and When to Use It on Blackwell
FP4's memory and throughput appeal on Blackwell, what you give up in quality, and how to validate it before shipping it to production.
Read article →GB200 NVL72 Tuning: Getting Rack-Scale Performance Right
The rack-scale problems that determine GB200 NVL72 throughput, from NVLink topology to Grace CPU-GPU coordination.
Read article →H100 vs B200: A CTO's Guide to the Right GPU Investment in 2026
How to weigh B200's performance case against H100's maturity and availability, and why tuning what you already have usually comes first.
Read article →B100 vs B200 vs B300: Which Blackwell GPU Should You Optimize For?
The differences between B100, B200, and B300 that matter for procurement and tuning, not just which one is fastest on paper.
Read article →B200 Tuning Playbook: Getting Maximum Throughput from NVIDIA Blackwell
Where to focus tuning effort on a B200, from FP4 quantization to batch size, so expensive hardware converts into usable throughput.
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CUDA Optimization Explained: The Techniques That Actually Move the Needle
The specific set of techniques CUDA optimization really covers, and where the real gains tend to come from, starting with profiling instead of guessing.
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TensorRT-LLM Optimization: FP16, FP8, and FP4 Inference Tuning
Getting the most out of NVIDIA's TensorRT-LLM engine requires tuning decisions at nearly every layer: precision, batching, KV cache, and engine builds.
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Cutting AI Inference Costs: A GPU Cost Optimization Framework for 2026
Why your GPU bill is high even when utilization looks fine, and a framework, ordered by effort, for bringing it down without re-architecting your model.
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Migrating from H100 to B200: A Practical Playbook
The infrastructure, software, and re-tuning work a straight H100-to-B200 hardware swap doesn't automatically account for.
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H100 Optimization: The Complete 2026 Performance Tuning Guide
How to close the gap between the H100's theoretical throughput and what your workload actually achieves, without buying new hardware.
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What Is CUDA and Why Should You Care? A Plain-English Primer
A jargon-free explanation of what CUDA is and why parallel GPU computing matters for modern workloads.
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Why Your AI Model Is Wasting GPU Memory (And How to Fix It)
The usual culprits behind wasted GPU memory in AI serving and the fixes that reclaim it.
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Stop AI Overthinking: Controlling Inference Compute at Runtime
How to stop reasoning models from wasting compute on easy inputs using token budgets and adaptive reasoning.
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Real-Time AI Thinking: Changing Model Behaviour Mid-Inference
Techniques to steer an LLM while it generates, from logit control to dynamic stopping, for adaptive real-time output.
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GPU Rendering Optimisation: The Engineering Playbook
Practical techniques to cut frame time in GPU rendering: batching, culling, and pass-level profiling.
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Spend X, Save Y: The GPU Investment ROI for ML Teams
A framework for deciding when spending on GPU optimization pays for itself against ongoing infrastructure costs.
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CUDA Memory Management: From Basics to Production Patterns
Understand the CUDA memory hierarchy and the pooling and coalescing patterns that keep production kernels fast.
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Profiling CUDA Workloads: Finding the Real Bottleneck
Use Nsight to distinguish compute-bound, memory-bound, and CPU-stalled workloads before you optimize anything.
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Multi-GPU CUDA: Scaling Beyond One Card
A guide to scaling CUDA workloads across multiple GPUs without letting communication overhead eat your gains.
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Building a Production CUDA Inference Pipeline: End-to-End Guide
How to take a model from a notebook to a production CUDA inference service with batching, concurrency, and low latency.
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