Alloc profiles your training jobs to predict VRAM, runtime, and cost. It recommends the right hardware and finds bottlenecks. Built for ML engineers. Bought for enterprise savings.
$ alloc run python train.py
✔ ghost: peak_vram=72.4GB strategy=FSDP recommended
✔ forecast: runtime=14.2h cost=$426 (A100-80GB)
⚠ optimize: gpu_util=34% likely IO bottleneck in DataLoader
→ suggestion: num_workers=8, prefetch_factor=4, pin_memory=true
$ _
THE PROBLEM
Rogue runs
A single mis-sized run can burn $200 to $2,000 before failing.
Someone launched 8xH100s for a BERT-base finetune. Nobody noticed for two days. That's $1,536 for a job that needed one A10G.
The ablation tax
Finding the right config means launching 10-30 runs to narrow it down.
Every ablation study is a grid search over your cloud bill. Most of those runs were always going to OOM or underutilize — you just couldn't know which ones until they ran.
Launch and pray
Most teams have no idea what a job will cost until the invoice arrives.
"How much VRAM does this need?" "I don't know, let me just request 4xA100s and see." — the most expensive sentence in ML engineering.
Representative ranges; varies by model, cluster, and pricing.
SOUND FAMILIAR?
"I launched a 70B finetune on Friday evening. By Monday morning it had burned $3,200 and was stuck at 12% GPU util because of a DataLoader bottleneck."
— Senior MLE, Series B startup
"We ran 24 ablation runs to find the right learning rate and batch size. 19 of them OOM'd in the first 5 minutes. We still got billed for provisioning."
— Research Engineer, university lab
"Our team has a shared GPU cluster. Nobody knows who's running what. Last month someone left an eval job running for 11 days on 4xA100s. It finished in hour one."
— ML Platform Lead, Fortune 500
"Every time we try a new model architecture, it's the same ritual: guess the GPU, launch it, wait for OOM, bump to a bigger instance, repeat. We call it 'GPU roulette.'"
— Founding Engineer, AI startup
These are composites, but if they feel specific, that's because this happens to every team eventually. Alloc exists so it doesn't have to.
WHAT ALLOC DOES
Predict VRAM, runtime, and cost before launch. Know what hardware you need before the job hits the queue.
Pick the smallest hardware that meets your SLA. Stop over-provisioning H100s when an A100 or L40S would do the job at half the cost.
Find data pipeline stalls, communication overhead, and low GPU utilization. Get actionable fixes, not just charts.
PRODUCT TIERS
Predict peak VRAM, minimum viable GPU, and feasible strategy (DDP/FSDP) before the job hits the queue. No more expensive failures or "Pending forever" jobs.
VRAM forecast, feasibility check, and minimum GPU recommendation. Runs locally in seconds. No GPUs burned.
Run 10-50 steps on real hardware to measure actual utilization and find bottlenecks. Ground truth for your workload.
Opt-in budget policies and audit trail. Continuous sidecar monitoring prevents runaway jobs and enforces spend limits in real-time.
THE DIFFERENCE
INTEGRATIONS
Alloc plugs into the tools you already use. No rip-and-replace.
GET STARTED
You wouldn't deploy to production without CI. Why launch a $2,000 training run without checking if it'll fit? Ghost Scan is free, runs in seconds, and catches failures before they cost you.