Microsoft · Maia · Layer 2
Microsoft Maia 200
Azure workloads, where Microsoft chooses to place them. Not a part you specify.
cloud only Cannot be bought. Exists only inside one cloud, so choosing it is choosing that cloud.
Specifications
- Memory
- Not published
- Bandwidth
- Not published
- Compute
- Not published
- Power
- Not published
- Interconnect
- Not published
- Software
- Azure-internal toolchain
- Form factor
- accelerator
- Workload
- both
Verified 2026-09-09. Fields reading “not published” are exactly that — we do not estimate a figure a vendor withholds.
The catch
Microsoft publishes very little. Treat any comparison table that gives Maia 200 precise specs with suspicion — including ours, which says 'not published' because that is the truth.
The economics
Its purpose is Microsoft's own cost base, not your price list. The benefit reaches you only as Azure pricing.
Cost per million tokens is the metric that decides most real purchases, and there is no neutral benchmark for it — every published figure comes from a company selling one side of the comparison. Read all of them, ours included, as claims rather than measurements.
The power question underneath this
A rack of frontier accelerators draws 120–200 kW against a 2026 average of about 27 kW, and the US grid interconnection queue exceeds 2,600 GW with waits approaching five years. Whether you can energise this part is now a harder question than whether you can buy it.
Layer 1 — Energy · The interconnection queue · Rack power density · Tokens per watt
Where you actually rent this
Choosing the part is the easy half. The same accelerator rents for wildly different money depending only on who you rent it from — the cheapest and dearest published on-demand price for an H100 sit 5.6× apart — and the provider, not the chip, is what decides whether you can leave later.
Compared against
- Microsoft Maia 200 vs Meta MTIA v2 — Two chips nobody outside their owners can touch. They matter as the GPU orders that never get placed.Compare with Meta MTIA v2
Related silicon
- AWS Trainium 2 — Not published in directly comparable terms, cloud only
- AWS Trainium 3 — 144 GB HBM3e per chip, cloud only
- Google TPU v6e (Trillium) — Not published in directly comparable terms, cloud only
- AMD Helios (Instinct MI455X) — 31 TB of HBM4 per rack across 72 MI455X GPUs; 432 GB of HBM4 per GPU., rent
- AMD Instinct MI300X — 192 GB HBM3, rent
- AMD Instinct MI350X — 288 GB HBM3E, rent
Sources
Want this whole layer as one table?
The accelerators on these pages as a single CSV — memory, bandwidth, compute, power, interconnect, software stack, availability, who each part is for and the catch — ready to sort, filter or load into a notebook.
Get the silicon table — free for research and evaluation
Four short lines and the download starts at once (CC-BY-NC-4.0, built from these pages the moment you ask). What you type is used only to answer the request and is never sold. Prefer Hugging Face? Request it there.
Using it inside a product, a client report or anything you sell? That needs a commercial licence.