Keep Your GPUs Busy.
Your GPUs are idle. Not because they’re slow. Because your data can’t keep up.
Your dataloader stalls. Data sits in object storage. Batches arrive late.
Most large-scale training jobs run at 10–20% utilization because data loading becomes the bottleneck.
LanceDB serves training data directly from the same table you curated and enriched at low latency and high throughput, without a separate caching layer.
Your dataloader stalls. Data sits in object storage. Batches arrive late.
Most large-scale training jobs run at 10–20% utilization because data loading becomes the bottleneck.
LanceDB serves training data directly from the same table you curated and enriched at low latency and high throughput, without a separate caching layer.
Feed GPUs Without Bottlenecks.
Your dataloader stalls while GPUs wait on data.
LanceDB delivers low-latency random access with a built-in training cache, so data flows continuously into your training loop.
No object store throttling. No extra systems.
Just sustained throughput from the same table you already use.
LanceDB delivers low-latency random access with a built-in training cache, so data flows continuously into your training loop.
No object store throttling. No extra systems.
Just sustained throughput from the same table you already use.
Turn Idle Time Into Useful Compute.
Every drop in utilization is wasted GPU spend.
Most training pipelines lose efficiency to I/O stalls, synchronization delays, and data movement.
LanceDB removes those bottlenecks by serving data directly, keeping your GPUs doing actual compute.
More useful work per run.
Fewer wasted cycles.
Faster iteration on models.
Most training pipelines lose efficiency to I/O stalls, synchronization delays, and data movement.
LanceDB removes those bottlenecks by serving data directly, keeping your GPUs doing actual compute.
More useful work per run.
Fewer wasted cycles.
Faster iteration on models.
Train On Exactly The Data You Want.
Control what flows into your training loop, without rebuilding datasets.
Use SQL filters, full-text search, or vector queries to stream precise subsets of data at train time.
Focus on hard negatives.
Adjust distributions on the fly.
Experiment without regenerating data.
Fully queryable, even during training.
Use SQL filters, full-text search, or vector queries to stream precise subsets of data at train time.
Focus on hard negatives.
Adjust distributions on the fly.
Experiment without regenerating data.
Fully queryable, even during training.
Train With Your Existing PyTorch Loop.
You shouldn’t have to rewrite your training loop just to use a new data system.
Plug LanceDB directly into your PyTorch dataloader.
No format conversion. No intermediate datasets.
Just point your dataloader at the table and start training.
Plug LanceDB directly into your PyTorch dataloader.
No format conversion. No intermediate datasets.
Just point your dataloader at the table and start training.
Global Shuffling, No Preprocessing.
Efficient random access enables true global shuffling across massive datasets, without staging or preprocessing.
No reshuffling jobs. No intermediate storage.
Just better randomness, directly from the source.
No reshuffling jobs. No intermediate storage.
Just better randomness, directly from the source.
Every Idle GPU Cycle Is Wasted Budget.
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