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 2026-09-06. 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
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