High Memory Bandwidth GPU
GPU Hardware & ComputeGPUs equipped with HBM, stacked memory technology that feeds GPU cores data at speeds conventional memory cannot match.
The B300 GPUs in Eleveight AI's cluster feature HBM3e, the highest memory bandwidth available in any commercial GPU today, critical for large model training and inference.
Overview
A counter-intuitive truth about modern AI is that it is usually starved for memory bandwidth rather than raw arithmetic. The cores can compute faster than data can be delivered to them, so the bottleneck is the pipe, not the engine. High Bandwidth Memory, or HBM, attacks this directly by stacking memory in layers right beside the GPU die and connecting it through an extraordinarily wide interface.
How it works
Rather than placing memory chips around the board and linking them over a comparatively narrow bus, HBM stacks them vertically and joins them to the GPU through a silicon interposer carrying thousands of connections at once. The B300 uses the latest generation, HBM3e, reaching up to 8TB/s of bandwidth per GPU, against roughly 1TB/s for the GDDR6X found in conventional graphics cards.
Why it matters
That roughly eight-fold advantage is not an abstract benchmark; it converts almost directly into results. More bandwidth means more tokens generated per second during inference and shorter, faster iteration cycles during training. On models large enough to be limited by memory rather than compute, HBM is frequently the single component that decides whether the work is practical at all.
Use cases
- Running large language models at practical inference speeds
- Training models where memory bandwidth is the primary bottleneck
- Real-time AI with tight token generation latency requirements