Accelerated Storage for
AI Training
Every second your GPUs wait for data is wasted money. TAG is a local caching proxy that delivers near-local throughput for AI training — with zero code changes.
Architecture
Inside the training instance.
TAG sits between your training code and cloud storage, caching hot data on local NVMe SSDs. Click each layer to explore the full stack.
Compute running on premises or any cloud, at massive scale.
Standard S3 API interface. Drop-in replacement requiring zero code changes to existing training scripts.
Intelligent caching proxy runs as a sidecar on your training instance, keeping hot data on a high-speed local NVMe SSD pool — local disk performance with cloud-scale capacity.
S3-compatible globally available storage with unlimited scalability and 99.99%+ availability.
Compute running on premises or any cloud, at massive scale.
Standard S3 API interface. Drop-in replacement requiring zero code changes to existing training scripts.
Intelligent caching proxy runs as a sidecar on your training instance, keeping hot data on a high-speed local NVMe SSD pool — local disk performance with cloud-scale capacity.
S3-compatible globally available storage with unlimited scalability and 99.99%+ availability.
Compute running on premises or any cloud, at massive scale.
Standard S3 API interface. Drop-in replacement requiring zero code changes to existing training scripts.
Intelligent caching proxy runs as a sidecar on your training instance, keeping hot data on a high-speed local NVMe SSD pool — local disk performance with cloud-scale capacity.
S3-compatible globally available storage with unlimited scalability and 99.99%+ availability.
How It Works
A local cache that speaks S3.
TAG runs as a sidecar on your training instance. Epoch 1 fetches from Tigris, epoch 2+ reads from local NVMe at disk speed. Drop-in S3 API — zero code changes required.
Near-local throughput
NVMe-speed reads after the first epoch. Training data served from local disk, not the network.
Zero code changes
Drop-in S3 API compatibility. Point your training script at TAG and it handles the rest.
Intelligent prefetching
Anticipates data access patterns to keep your GPU pipeline full and idle time near zero.
Cache Anywhere
Store once, access anywhere.
Deploy TAG across regions and clouds. Each instance caches locally while Tigris handles global replication — your training data is always close to your GPUs.
Performance
Keep GPUs saturated during training.
TAG delivers up to 200× the throughput your GPU can consume — ensuring your training pipeline is never bottlenecked by storage.
Accelerate your training pipeline.
TAG is open source and available today. Get near-local storage performance for your AI training workloads — across clouds.