AliCloud is pushing an Agentic Cloud stack for Qwen3.8 Max and KIMI K3

AliCloud unveiled an "Agentic Cloud" stack at the 2026 Apsara Conference, including AgentCore, Agent Sandbox, and a new CPFS storage layer, to support models such as Qwen3.8 Max and KIMI K3 that exceed 2T parameters. AliCloud said the updated CPFS reaches 100PiB single-file-system scale and 100TB/s throughput, cutting model startup time by 50% and boosting peak compute utilization by 30% while reducing storage costs by 69%, and that AgentCore raises task completion rates to 99% while cutting TCO by 70%. The company also detailed the Lingjun Zhenwu M890 100k-card cluster and an upcoming V900 super-node with 200Pbps bandwidth, 6us cluster latency, and single clusters scaling to 500,000 cards via SNPO all-optical interconnects.

AliCloud is pushing an Agentic Cloud stack for Qwen3.8 Max and KIMI K3 The latest updates from the 2026 Apsara Conference show AliCloud is pivoting toward "Agentic Cloud," focusing on the infrastructure needed for agents to move past simple Q&A into autonomous planning. They've released a suite of tools—AgentCore, Agent Sandbox, and a new CPFS storage layer—specifically designed to handle the massive compute and context requirements of agents that use recursive self-improvement RSI and long-term memory. How they are handling the RSI compute bottleneck Scaling models from trillions to ten-trillion parameters requires a different kind of stability. AliCloud is using the Lingjun Zhenwu M890 super-node their first domestic 100k-card cluster to support models like Qwen3.8 Max and KIMI K3, both of which exceed 2T parameters. The upcoming V900 super-node looks even more aggressive: - Performance: 3x increase over previous versions. - Connectivity: 200Pbps communication bandwidth. - Latency: 6us cluster architecture. - Scale: Supports 1,000-card Scale-Up interconnects, with single clusters expanding up to 500,000 cards using SNPO all-optical interconnects. Solving the "GPU waiting for data" problem with CPFS They've updated their high-performance storage CPFS , which is critical for multi-modal data processing. The new version hits 100PiB for single file system scale a 5x increase and delivers 100TB/s throughput. In real-world training, this reportedly cuts model startup time by 50% and boosts peak compute utilization by 30%, while slashing storage costs by 69%. On the inference side, they've launched Tair KVCM to manage KVCache across Mempool and remote storage. This achieves a 99% effective hit rate and drops the cost per token by 50%. Their KV CacheStore on the G3.5 storage layer is handling the massive cache loads from long-context agent tasks, expanding the cache coverage window by 900% and reducing first-token latency by 54%. Scaling Agent deployment with AgentCore and Sandbox To move agents into production without them being "probabilistic" and unreliable, AliCloud introduced AgentCore. It's a governance platform that standardizes agent infrastructure, specifically handling: - Long-running tasks with failure retries and breakpoint recovery. - Asynchronous execution. - Resource isolation and full-link auditing. They claim this setup increases task completion rates to 99% while cutting TCO by 70%. For the runtime environment, Agent Sandbox allows for 100,000 creations per minute with wake-up times under 600ms, compatible with both E2B and K8s. They also have Agentic OS, which reportedly saves over 30% in token consumption and triples sandbox deployment density. Turning enterprise data into real-time context The "Context Engine" is their attempt to turn fragmented data into a usable state for agents. This involves a three-tier stack: 1. Storage: Handling video frames, point clouds, and trajectories. 2. Processing: Data integration and quality modeling. 3. Context Engineering: Using the Agent Context tool to increase retrieval accuracy by 50 percentage points and improve token efficiency by over 3x. They've also updated the surrounding ecosystem: OpenLake reduces TCO by 38% through a unified multi-modal lakehouse, Apsara Lakebase allows sub-second zero-copy data branching, and MC-MaxFrame has improved multi-modal compute performance by 12%. For real-time triggers, Flink Streaming Agent now provides over 60 multi-modal operators. Finally, for those doing post-training, the PAI platform now supports asynchronous AgenticRL. In practice with Qwen models, they managed to complete a SOTA model post-training cycle in just 5 days. Next Google AI Studio Build Agent keeps hitting a quota error despite zero usage → https://promptcube3.com/en/threads/9562/ All Replies （3） Want a live back-and-forth? Join the global AI chat room https://promptcube3.com/en/chat/ — login to talk. I want to try this tonight. My current setup chokes on recursive self-improvement, so a dedicated CPFS layer might finally fix my latency. Skeptical that CPFS storage actually solves the bottleneck. Most local agents fail at planning long before the hardware hits a limit. I'm curious if this AgentCore suite handles multi-model orchestration. It's useless without a way to swap between Qwen3.8 Max and smaller distilled models.