DOCUMENTATION
Alloc is a training pipeline intelligence layer for ML teams. It diagnoses bottlenecks across data loading, communication, and GPU utilization, recommends parallelism strategies, estimates VRAM, and right-sizes hardware — so you stop burning money on mis-provisioned runs. No code changes required.
FROM ALLOCGUY
GPU spend is the fastest-growing line item in ML. NVIDIA's data center revenue hit $47.5B in FY2024, up 217% year over year. Cloud GPU pricing reflects this: a single H100 node runs $3–5/hr, and an 8xH100 cluster costs over $30/hr. For a 1,000-hour training run, that's $30,000+ in compute alone.
Yet most ML teams I talk to have no idea what their utilization actually is. They default to the biggest GPU they can get, launch training, and hope for the best. When I started digging into this, I found the same pattern everywhere: 30–50% of GPU spend goes to failed runs, over-provisioned instances, and idle hardware waiting on data pipelines.
Electricity alone tells the story. A single H100 SXM draws 700W at peak. Running a 128-GPU cluster for a week burns through roughly 15,000 kWh, roughly the annual electricity consumption of two US households. And data center electricity demand is growing 25%+ per year, with AI training as the primary driver. The IEA projects global data center power consumption will reach 1,000 TWh by 2026.
That's why we built Alloc. Not to sell you more GPUs, but to help you stop wasting the ones you already have.
– allocguy
QUICKSTART
Install Alloc and run your first scan. See estimated VRAM, recommended GPUs, and potential bottlenecks before you launch.
Read guideGHOST SCAN
Estimate peak VRAM requirements without running a single GPU hour. Ghost Scan analyzes your model architecture and training config to forecast memory needs.
Read guideRIGHT-SIZING
Stop overpaying for GPUs. Learn how Alloc recommends the smallest hardware that meets your training requirements, saving you money on every run.
Read guideGPU compute is the largest line item for most ML teams, yet most organizations have no visibility into whether they are using it efficiently. Common problems include:
Alloc solves this by giving ML engineers a pre-flight check for every training job. Sign up free and start scanning in under a minute.
Alloc operates as an external intelligence layer. It never modifies your training code, never changes your exit codes, and never blocks your jobs. Three steps:
Point Alloc at your model and training config. It analyzes your architecture, parameters, and data pipeline to build a workload profile.
Alloc estimates VRAM requirements, identifies potential bottlenecks, and evaluates which GPU configurations are likely to work for your workload.
Get right-sized GPU suggestions, estimated cost ranges, and actionable optimization tips, all before you provision a single machine.
Want to see it in action? Try the interactive demo
Alloc is a single pip install. Works on Linux and macOS. No root access required.
$ pip install alloc
Then follow the quickstart guide to run your first scan.
Three commands that cover the most common workflows.
Ghost Scan estimates peak VRAM from your training script without using any compute. Runs locally in seconds.
$ alloc ghost train.py
Wrap your training command with alloc run. Alloc profiles the job in the background and captures GPU metrics, utilization, and bottleneck data automatically.
$ alloc run python train.py
Static analysis of your training script. Detects DataLoader, precision, distributed, and throughput issues with actionable fixes.
$ alloc diagnose train.py
Use the built-in model catalog to estimate VRAM for popular models without needing the training script.
$ alloc scan --model llama-3-70b --gpu A100-80GB
See the full CLI reference in the quickstart guide.
Alloc is free to get started. Install the CLI, run a scan, and get bottleneck diagnosis, strategy recommendations, and GPU right-sizing in seconds.