Platform
Different accelerators. One unified memory pool.
OpenMycelium brings compatible infrastructure together as one logical memory pool, with capacity and control at the center.
Conceptual visualization
Cross-vendor by design
Engineered to connect. Working today.
Our work explores how heterogeneous infrastructure can support more flexible AI execution.
- NVIDIA
- AMD
- Intel
- Apple
- Qualcomm / Snapdragon
- Other accelerators
Inference · Training · Fine-tuning
Cross-vendor by design — available capacity varies by device and workload.
Text equivalent of this visualization
Six accelerator sources — NVIDIA, AMD, Intel, Apple, Qualcomm / Snapdragon, Other accelerators — are drawn toward a central OpenMycelium emblem, which joins them into one logical memory pool. Workload packets travel inward, the connections illuminate, and packets depart toward inference, training and fine-tuning. Named platforms indicate the ecosystems this work spans. They do not indicate partnership or endorsement.
Unified logical memory pool
Memory across different accelerators is presented as one logical pool, so infrastructure that used to work in isolation works together.
Capacity-aware operation
Capacity sits at the center. Work is arranged around what the workload asks for and what the pooled resources can offer.
Operator visibility
Operators stay in control, with a clear view of the arrangement rather than a black box.
Vendor-neutral by design. Available capacity varies by hardware and workload.
High-level view
From workload to pooled capacity.
A single picture of how work moves: your workload enters, memory across accelerators is presented as one logical pool, and inference, training and fine-tuning run on top of it.
Start with the work you want to run — the model and the job, not the hardware catalogue.
High-level view only — available capacity varies by device and workload.
From one node to many
Start on a single machine. Grow to multiple clusters.
The same logical memory pool scales with you — from the accelerators inside one machine, to many machines working together, to separate clusters coordinated as one.
01 · One node
Start with a single machine.
The accelerators inside one machine are brought together as a single logical memory pool.
02 · Many nodes
Grow across machines.
Additional machines join in, and their resources participate in the same logical pool.
03 · Multiple clusters
Extend across clusters.
Separate groups of machines can be coordinated together, with capacity and control at the center.
High-level view — capacity and control stay at the center at every stage.