Unstable GPU utilization
GPUs sit at 20–40% while CPUs are pegged at 90%. We analyze your pipeline to understand where the stalls actually happen.
GPU Performance Rescue for Real-World Workloads
When your GPUs are on fire, you don’t have weeks to debug.
We provide on-call GPU performance troubleshooting and optimization for production workloads – from rendering farms to inference clusters.
We stay online, so your systems stay alive.
GPU Services · Performance rescue & optimization
We help teams that rely on GPUs – for rendering, VFX, games, and AI inference – diagnose bottlenecks and ship more stable performance without replacing hardware.
Unpredictable latency
P95 / P99 latency spikes that only happen under “real” traffic. We trace the full path across kernels, memory, and IO.
Cross-GPU performance variance
Identical jobs, different GPUs, 2× difference in throughput. We look at driver, NUMA, scheduling and kernel behavior side by side.
Kernel-level bottlenecks
Hand-written or vendor kernels that behave differently in your actual workload. We profile at kernel level and build a concrete change list.
How we work
-
Step 1 – Remote profiling & log collection
We review your stack, collect traces and metrics (GPU / CPU / IO), and instrument the minimal set of probes needed to see real behavior.
-
Step 2 – Bottleneck analysis
We locate bottlenecks across kernels, data movement, memory layout, and scheduling. The goal is a clear, reproducible explanation of “why it’s slow”.
-
Step 3 – Actionable change list & pair debugging
You get a concrete list of changes with estimated impact, plus remote pair-debugging support while your team applies them.
-
Step 4 – Optional follow-up optimization sprint
For teams that want to go further, we can run a focused sprint to squeeze extra performance out of the pipeline once it’s stable.
Featured GPU case studies
8×A100 inference cluster: from unstable to predictable
How we stabilized utilization and reduced P95 latency on a multi-node A100 inference cluster without rewriting the core model.
4090 video pipeline: fixing jitter and VRAM spikes
Eliminating frame jitter, VRAM fragmentation, and kernel fragmentation in a real-time video processing pipeline.
Mixed rendering farm: balancing load across GPUs
Making a heterogeneous rendering farm behave like a single, well-balanced pool of GPU capacity.
Technical insights
A few notes from real troubleshooting work – written for engineers, not marketing.
How we diagnose GPU-bound vs IO-bound behavior
A practical workflow to separate GPU saturation from data loading and network issues, using the metrics you already have.
Common anti-patterns in real inference pipelines
Patterns we repeatedly see in PyTorch / TensorRT / custom CUDA setups – and small changes that usually pay off quickly.
How we diagnose GPU-bound vs IO-bound behavior
In real systems, low GPU utilization rarely means “the GPU is slow”. It usually means the GPU is waiting – on data, on synchronization, or on upstream work.
We start by mapping three curves over the same time window: GPU utilization, CPU utilization, and queue depth / request rate. From there, we look at:
- Host-to-device and device-to-host transfer patterns
- Data loader throughput vs kernel run time
- How back-pressure propagates through the system
The goal is not a “perfect” model of your system, but a clear decision: do we spend the next few days on kernels, or on data and orchestration.
Common anti-patterns in real inference pipelines
Many production inference stacks grow organically over time. We often see:
- Batch sizes chosen by gut feel, not by measurement
- Multiple tiny kernels with high launch overhead
- Data transformations happening on the wrong side of the PCIe bus
We walk through the pipeline hop by hop, turning these into concrete changes: fewer kernel launches, better batching, and less unnecessary data movement.
UI & visual demos
We also build internal visual demos and UI experiments for GPU-powered content – WebGL, Canvas, and brand visualizations.
These are not our primary service line, but they help teams imagine what their GPU-backed experiences can look and feel like.
About & contact
AuGPU.AI is a small, engineering-driven studio focused on GPU performance troubleshooting and optimization.
- Based in Vancouver, Canada
- Focused on GPU performance troubleshooting & optimization
- Work with teams in North America and Europe
Project email
Best for structured project discussions, requirements, and sharing materials.
EMAIL
linzheng0428@gmail.com
In your message, you can briefly describe the project background, timelines, and the main performance issues you’re seeing.
Live chat (bottom-right)
Useful for early-stage questions: feasibility, time frames, and possible ways of working together.
For deep technical debugging, we will switch to email or a scheduled call so we can keep a clear record of steps and findings.
How to start
If you already have a concrete need, preparing the points below will help us move faster:
- Project type (e.g. rendering farm, VFX pipeline, inference cluster)
- Rough scale (GPU count, model types, or page complexity)
- Preferred time frame and critical milestones
With this information we can propose a realistic collaboration model and schedule.