JD Cloud's 100,000-Card Cluster Bets on Moore Threads: Domestic GPUs Enter a Top AI Cloud's Core Compute Base for the First Time
This article is based on official announcements from the 2026 JD Global Technology Explorer Conference (September 9) and public market information; order values and revenue forecasts are brokerage/media estimates, not company announcements.
On September 9, at the 2026 JD Global Technology Explorer Conference, JD Cloud announced a milestone decision: partnering with Moore Threads to build a 100,000-card full-function GPU cluster, creating hyperscale domestic intelligent computing infrastructure.
Two keywords in this sentence deserve amplification: "full-function GPU" and "100,000-card-class core cluster." The former means it must run not just inference but the front lines of large model training, inference, and embodied AI; the latter means a domestic GPU has, for the first time, been placed at the core of a top AI cloud provider's compute base — not a pilot, not an adaptation, not an all-in-one appliance, but 100,000 cards.
1. Partnership Details: From 10,000 to 100,000 Cards — How Big Is the Order?
- Prior foundation: JD Cloud has already built a domestic 10,000-card cluster with Moore Threads and other partners; this move is a magnitude leap from 10,000 to 100,000 cards
- Order scale: According to market and brokerage information, GPU modules are supplied exclusively by Moore Threads, with a total order value of about RMB 20-30 billion; revenue can be recognized as early as next year according to delivery cadence. Some brokerages have raised Moore Threads' revenue expectation for next year to RMB 15-20 billion (this estimate is not a company announcement; refer to official disclosures)
- Partnership depth: Full-stack coordination from chips and cloud platform to model training, supporting the iteration of JD's JoyAI model family and forming a closed loop of "data, training, simulation, deployment"
- Openness: Compute is open to all industries, focused on large model training, inference, and embodied AI
Moore Threads founder Zhang Jianzhong was direct on stage: "The Scaling Law still holds — 100,000-card clusters are an inevitable trend." JD Cloud President Cao Peng positioned domestic compute as a core pillar of JD's physical AI strategy.
2. Why Moore Threads? Three Calculations Behind the Procurement Logic
Tech companies buy cards with no sentiment involved. JD's choice of Moore Threads comes down to three calculations that all add up:
1. The stability calculation: Moore Threads has commercially deployed thousand-card and 10,000-card large clusters under a single network, and has achieved breakthroughs in core training scenarios such as foundation models, embodied brains, and world models — the engineering validation of a 10,000-card cluster is the prerequisite for 100,000 cards; this is not a cold start.
2. The integration calculation: Full-function GPUs can plug into existing IT systems and cloud platform scheduling, and the MUSA software stack's adaptation to large model frameworks has already passed training-grade workloads.
3. The ROI calculation: This is the most critical shift. Buyers of domestic GPUs used to be mostly "policy-friendly" projects; JD writing a 100,000-card cluster into its capital expenditure means the product's return on investment can now stand up to investor scrutiny — the buyer structure shifting from "daring to use" to "rushing to use" is the hallmark of commercial maturity for domestic GPUs.
3. S6000: Next-Gen Chip Taped Out and Back, with a Dual-Supply Safeguard
According to market information, Moore Threads' next-generation chip, the S6000, has successfully come back from the fab and been distributed to vendors for testing, with ample FAB and memory supply guarantees. Note that the S6000 has not been officially released and its specifications are not public; this article makes no speculation. The on-sale flagship MTT S5000 has on-site data of 400 TFLOPS FP16 and 80GB of memory (the 1.6TB/s bandwidth is HBM-class).
Regarding HBM supply constraints, Moore Threads' response strategy is reportedly "next-generation product iteration + multi-source supply chain safeguards" — until domestic HBM capacity ramp-up is complete, this is the same problem every domestic GPU vendor must solve.
4. "100,000 Cards" Is Not One Company's Game: The Domestic GPU Cluster Landscape
It is worth widening the view — 100,000 cards is now a collective goal for domestic compute:
| Player | 100,000-Card Move | Compute Base |
|---|---|---|
| JD Cloud | Announced co-built 100,000-card cluster on September 9 | Moore Threads full-function GPU |
| Sugon 8000 | Released at WAIC in July, completed in Zhengzhou; a fully domestic 100,000-card AI compute cluster | Hygon DCU (of the Shensuan BW1000 family) |
| Huawei | Atlas 950 SuperPoD super node (8,192 cards); 100,000-card super node in 2027 | Ascend 950/960 |
Two chip routes (full-function GPU vs DCU vs NPU) and two paths (commercial cloud vs national supercomputing) point to the same validation question: can domestic compute reliably run real training workloads at 100,000-card scale. It is worth emphasizing that there is currently no public third-party benchmark comparison for the Moore Threads x JD cluster, and no completion timetable — going from "usable" to "running well" at 100,000 cards still requires engineering validation.
5. The Capital Markets Perspective
Moore Threads listed on the STAR Market in December 2025: an IPO price of RMB 114.28, closing at RMB 600.5 on the first day; 2025 revenue of RMB 1.505 billion (up 243.37% year over year), with a net loss of about RMB 1.001 billion. If the brokerage-raised revenue expectation of RMB 15-20 billion materializes, 2027 will be the key inflection point from "high-growth loss-making" to "profitable at scale" — which would also become the first complete answer sheet for the domestic GPU business model.
Summary
From debuting with DeepSeek all-in-one appliances in 2025 to entering a 100,000-card core cluster in 2026, domestic GPUs completed the cognitive leap from "usable" to "commercially viable at scale" in under two years. The value of JD Cloud's order lies not in its amount but in its significance as a sample: when top internet buyers begin procuring domestic GPUs on ROI logic, substitution is no longer a policy narrative but a commercial fact.
(Order values, revenue forecasts, and supply chain status come from public market information and brokerage research estimates; refer to company announcements; chip specification data is available in the on-site full comparison table.)