Memory intelligence for AI infrastructure.
Compute is useless if you can't feed it. HBM measures, verifies and optimizes the memory behind every GPU in your fleet.
- GPUs
- 4,182
- Memory capacity
- 327TB
- Effective bandwidth
- 8.4PB/s
- Utilization
- 71.8%
- Memory-bottlenecked
- 638GPUs
- Underperforming
- 81GPUs
- Idle VRAM
- 42.7TB
ModelMemory pressure
- Llama-70B94%
- Qwen-72B88%
- Embeddings31%
- Image inference64%
- Training, cluster 0797%
A product preview with illustrative numbers.
The questions HBM answers
Which GPUs are memory-starved?
Bandwidth demand against measured supply, per device.
Which models are saturating bandwidth?
Memory pressure per workload, not just per machine.
Which machines underperform their class?
Every GPU against the measured profile of identical hardware.
Where is expensive VRAM sitting idle?
Capacity allocated against capacity used, across the fleet.
Which workload belongs on which memory?
Compute-bound or memory-bound, and what moving it would change.
What can this cluster actually do?
Verified capacity and bandwidth. Measured, not specified.
From one GPU to the fleet
The same measurement that runs in a browser today scales to servers, racks and clusters.
Live today
- Your GPU
- Proof of Memory
- MEM Score
- HBM Network
Where it goes
- 1 GPU
- 1 server
- 1 rack
- 10,000 GPUs
- Control plane
Measured, not specified
Across thousands of machines, measured memory performance becomes a dataset: real-world bandwidth by hardware, driver, workload, region, cloud, configuration and model.
| Hardware class | Median read, GB/s | Published peak, GB/s | Runs |
|---|---|---|---|
| RTX 4060 | 251.3 | 272 | 4 |
| UHD Graphics | 40.0 | — | 3 |
| RTX 3080 | 610.0 | — | 2 |
| GTX 1080 Ti | 427.3 | 484 | 2 |
| RTX 5060 | 397.3 | 448 | 1 |
| GTX 1070 Ti | 209.3 | 256 | 1 |
Datacenter classes join as fleets connect.
Then, optimize
Once memory is measured reliably, HBM can say what to change.
- This workload is memory-bound. A more expensive GPU class won't materially raise its throughput.
- 74 GPUs are running 18% below the expected memory profile of their class.
- This workload is compute-bound. It can move to a cheaper memory tier without slowing down.
And finally, allocate
With availability, bandwidth, utilization, requirements and price in one place, HBM can route each workload to the memory that fits it. That is the control plane.
Model request
- VRAM
- 80 GB
- Bandwidth
- > 2 TB/s
- Latency
- under target
- Region
- EU
HBM
Placed on
The best available memory
Verified capacity, measured bandwidth, the right region.
Request early access
For AI labs, GPU clouds, datacenters and enterprise fleets.