You will get GPU Cost & Inference Speed Optimization
Project details
You’ll get your existing PyTorch model running faster and cheaper on GPU, with clear before/after benchmarks. I focus on real bottlenecks: batching, dataloading, dtypes (FP16/BF16), kernel usage, and practical export paths (TorchScript / ONNX / TensorRT when appropriate) – not random tweaks.
The Starter tier gives you a focused speed audit for one model: profiling, safe optimizations, and a simple benchmark table.
The Standard tier adds deeper changes (mixed precision, better batching, light refactors, optional export) plus an updated inference script ready for production.
The Advanced tier targets demanding setups: large or multi-GPU models, high-traffic services, and cost/performance tuning at the pipeline level, with detailed recommendations for your specific hardware and budget.
The Starter tier gives you a focused speed audit for one model: profiling, safe optimizations, and a simple benchmark table.
The Standard tier adds deeper changes (mixed precision, better batching, light refactors, optional export) plus an updated inference script ready for production.
The Advanced tier targets demanding setups: large or multi-GPU models, high-traffic services, and cost/performance tuning at the pipeline level, with detailed recommendations for your specific hardware and budget.
Machine Learning Tools
NVIDIA AI Platform, PyTorchWhat's included
| Service Tiers |
Starter
$50
|
Standard
$100
|
Advanced
$150
|
|---|---|---|---|
| Delivery Time | 5 days | 10 days | 15 days |
Number of Revisions | 1 | 2 | 3 |
Number of Model Variations | 1 | 2 | 2 |
Model Validation/Testing | - | - | - |
Model Documentation | - | - | - |
Data Source Connectivity | - | - | - |
Source Code | - | - | - |
Frequently asked questions
About David
Senior AI Engineer | Deep Learning | Computer Vision | NVIDIA A100GPU
Goeteborg, Sweden - 7:45 pm local time
Welcome! I'm here to help you achieve your AI goals – in a precise, quick and clear way. Whether you want a custom AI model, optimize your current AI pipeline or explore GPU possibilities, my main goal is to ensure your satisfaction by delivering robust solutions.
I’m a Deep Learning engineer (4+ years) specializing in custom AI models trained on your own data, with a strong focus on PyTorch, computer vision, and NLP (Natural Language Processing). I work directly on an NVIDIA A100–datacenter class GPU, so I can handle serious experiments, larger models, and fast iterations.
What I can help you with:
— Design and train custom models (vision, text, or tabular) on your proprietary dataset
— Fine-tune existing foundation models (for example, Vision Transformers and other transformer architectures) for your specific use cases.
— Build end-to-end training pipelines: data preprocessing, training, evaluation, and reporting
— Improve or refactor existing code for better performance, stability, and clarity
— Provide research-grade prototypes that can be turned into production systems
Why work with me?
— Strong research + engineering blend: I’m comfortable reading papers, implementing new methods, and turning them into robust, readable code.
— Proven real-world impact: For Rörstrand Museum in Sweden, I helped build an AI prototype that recognizes and dates ceramic objects from images, so staff can catalogue pieces faster, answer visitor questions more accurately, and make more of the collection available digitally.
— Serious compute, faster feedback: Access to an NVIDIA A100 GPU means quicker experiments, more iteration, and better models within the same budget.
— Clear communication: I explain trade–offs in plain language and keep you involved in key decisions, so you always know what you’re getting and why.
How I work:
— I start by clarifying your goal: what does “success” look like in business terms (not just accuracy numbers)?
— I review your data, propose a concrete plan (model choice, metrics, timeline), and break it into clear milestones.
— You get transparent updates, clean and well-documented code, and a short summary of results and next steps.
Tech stack:
— Languages: Python
— Core frameworks: PyTorch, TensorFlow, CUDA, Triton
— Domains: computer vision, NLP, classification, detection, recommendation, anomaly detection
— Compute: NVIDIA A100 GPU for training and experimentation
If you have a specific dataset or idea and want to see what is actually possible with modern deep learning, send me a short description of your use case and data, and I’ll let you know how I can help.
Steps for completing your project
After purchasing the project, send requirements so David can start the project.
Delivery time starts when David receives requirements from you.
David works on your project following the steps below.
Revisions may occur after the delivery date.
Inputs & baseline benchmark
You share the model, current inference script, target hardware, and a small test dataset. I run baseline benchmarks (latency, throughput, memory) and confirm goals.
Profiling & bottleneck analysis
I profile the model and pipeline (GPU utilization, batch behavior, I/O, precision) to identify the main performance and cost issues.