Qualcomm · Cloud AI · Layer 2
Qualcomm Cloud AI 100 Ultra
Inference where watts are the budget. 150 W against 700-1,000 W for a GPU is the entire argument.
buy You can purchase this outright.
Specifications
- Memory
- 128 GB LPDDR
- Bandwidth
- Below HBM parts
- Compute
- Inference-optimised
- Power
- 150 W
- Interconnect
- PCIe
- Software
- Qualcomm AI Stack
- Form factor
- accelerator
- Workload
- inference
Verified 2026-09-09. Fields reading “not published” are exactly that — we do not estimate a figure a vendor withholds.
The catch
LPDDR bandwidth limits throughput on large models. It wins on tokens per watt, not tokens per second.
The economics
The right part when your constraint is a power envelope rather than a latency target — increasingly common, given the Energy layer.
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
- Qualcomm Cloud AI 100 Ultra vs NVIDIA H200 — 150 W against 700 W. Tokens per watt is a different contest from tokens per second.Compare with NVIDIA H200
Related silicon
- NVIDIA DGX Spark — 128 GB unified LPDDR5X, buy
- AWS Inferentia 2 — Not published in directly comparable terms, cloud only
- Cerebras WSE-3 — 44 GB on-chip SRAM, rent
- Google TPU v7 (Ironwood) — 192 GB HBM3E per chip, cloud only
- Groq LPU — 230 MB SRAM per chip, no HBM, rent
- Intel Gaudi 3 — 128 GB HBM2E, buy
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.
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