See What Alloc Does

Name: Alloc
Author: Alloc Labs

From pre-flight VRAM checks to run analysis, code diagnosis, and cost tracking. Try the interactive scan, then scroll to see what a real Alloc workflow looks like.

Pre-flight check: will your model fit?

Pick a model and GPU below. Alloc estimates VRAM, cost, and feasibility instantly.

Model

GPU

Strategy

GPUs

Precision

Live|8.03B params

GPU Comparison

You just saw the pre-flight. Here's what happens when you train.

Run Analysis

Representative example

After training, Alloc surfaces bottlenecks, phase breakdowns, and right-sizing recommendations.

alloclabs.com/runs/llama3-8b-finetune

llama3-8b-finetune

completedUnderutilizedStep Timing

4x A100-80GB · FSDP · feat/llama-finetune

Peak VRAM

51.2 GB

/ 80 GB per GPU

GPU Busy %

47%

across 4 GPUs

Step Time (p50)

284 ms

p90: 312 ms

Dataloader Wait

42%

of step time

Step Phase Breakdown

42%

31%

22%

Dataloader 42%

Forward 31%

Backward 22%

Optimizer 5%

GPU Utilization

46%

VRAM Usage (GB)

51 GB

Recommendations

DataLoader bottleneck detected

high

42% of step time spent waiting on data loading. Your GPUs are idle during this time.

Set num_workers=8 (currently 0)
Enable pin_memory=True
Set prefetch_factor=4

Strategy optimization available

medium

FSDP with gradient checkpointing could reduce per-GPU VRAM by ~30%, enabling larger batch sizes.

Enable gradient checkpointing
Consider FSDP cpu_offload for optimizer states

Consider GPU right-sizing

low

Peak VRAM is 51.2 GB on 80 GB GPUs (64% utilization). With FSDP sharding across 4 GPUs, per-GPU usage drops to ~13 GB — an A10G (24 GB) could handle this at lower cost.

Run pre-flight scan on L40S

Config Comparison

GPU	Strategy	Est. Cost	Status
4x A100-80GB	FSDP	$12.40/hr	current
4x A10G-24GB	FSDP	$5.40/hr	explore
2x A100-80GB	FSDP	$6.20/hr	in fleet
4x H100-80GB	FSDP	$16.80/hr	in fleet

Now let's catch issues before they cost you GPU hours.

Code Diagnosis

Representative example

Alloc statically analyzes your training code to find common performance issues and suggests improvements.

terminal

$ alloc diagnose train.py

⚠DL001num_workers=0 (default)hightrain.py:47

⚠PREC001No mixed precision detectedhightrain.py:82

✔DIST002FSDP configured correctlytrain.py:31

2 issues found, 1 check passed

Run alloc diagnose --diff for patches

train.pyDL001 fix

@@ -45,3 +45,5 @@

train_dataset = load_dataset("custom/data")

-train_loader = DataLoader(train_dataset, batch_size=32)

+train_loader = DataLoader(

+ train_dataset, batch_size=32,

+ num_workers=8, pin_memory=True, prefetch_factor=4

All of this rolls up into budget and savings tracking.

Budget & Cost Tracking

Representative example

Track GPU spend, see realized savings, and set budget guardrails for your team.

Monthly Budget

$2,847 / $5,000

$2,153 remaining

57% burned

Potential Savings

$1,200

Realized Savings

$680

Jobs Right-Sized

OOMs Prevented

Recent Runs

Run	GPU	Cost	Status
llama3-8b-finetune	4x A100-80GB	$4.23	underutilized
mistral-7b-eval	1x A100-80GB	$0.89	balanced
qwen-72b-pretrain	8x H100-80GB	$18.41	compute bound
llama3-70b-lora	4x A100-80GB	$6.72	failed

Ready to stop guessing?

Install Alloc in one line. Get VRAM estimates, bottleneck detection, and cost tracking from your first run.

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