Why it matters for an office
Model size is mostly limited by memory. Unified memory is the cheapest way to run 27B to 70B-class models in a small box.
ANVE: the private AI office, on the record. See what that means
ANVEPrivate AI glossary · updated 2026-10-16
Unified memory.
Defined in plain words, for offices deciding how to use AI.
Short answer. Unified memory is a design where the CPU, GPU and NPU share one pool of RAM instead of the GPU having its own separate video memory. For AI, it means a model can use most of the system memory: a machine with 128 GB of unified memory can hold models far larger than a typical 24 GB graphics card. Apple silicon, AMD Strix Halo and NVIDIA’s GB10 in the DGX Spark use this approach. The trade-off is memory bandwidth, which is usually lower than a high-end GPU’s.
How ANVE uses it
ANVE One is specified around AMD unified memory (Strix Halo, 64 GB). ANVE Office and Office Pro use a 24 GB NVIDIA RTX PRO 4000 Blackwell graphics card with separate system memory instead, which holds a 27B-class model on the card.
Questions people ask
- Is unified memory better than a GPU for AI?
- It fits bigger models per dollar; a discrete GPU is usually faster for models that fit in its VRAM.
What is not real yet
No sealed ANVE unit exists; every image of the appliance on this site is a labelled render. The software runs today on a development machine. No customers yet, no certifications, no delivery date, and the final price is not announced.
Reservations are ₹999 in India or US$12 elsewhere, refundable in full until you confirm an order in writing (terms). Offices that cannot use cloud AI can apply to be one of ten design partners.