AliCloud’s Agentic Cloud Stack Enables Autonomous Agents with Qwen3.8 Max and KIMI K3
At the 2026 Apsara Conference, AliCloud unveiled its "Agentic Cloud" infrastructure, designed to empower agents capable of autonomous planning beyond traditional Q&A functions. The initiative introduces AgentCore, Agent Sandbox, and an upgraded CPFS storage layer to meet the computational and contextual demands of models leveraging recursive self-improvement (RSI) and long-term memory—key features of Qwen3.8 Max and KIMI K3, both exceeding 2T parameters.
The transition to models with trillions to ten-trillion parameters demands greater stability. AliCloud’s Lingjun Zhenwu M890 super-node, its first domestically developed 100k-card cluster, now supports these high-capacity models. The forthcoming V900 super-node further extends capabilities with:
- Threefold performance gains over predecessor versions.
- 200Pbps communication bandwidth for seamless connectivity.
- 6µs cluster latency, enabling near-instantaneous data exchange.
- Scalable interconnects: Single clusters now support up to 1,000-card Scale-Up configurations, with 500,000-card expansions via SNPO all-optical interconnects.
To mitigate "GPU waiting for data" delays, the updated CPFS storage layer delivers 100PiB per filesystem—a 5x scale increase—while achieving 100TB/s throughput. In training scenarios, it cuts model startup time by 50%, boosts peak compute utilization by 30%, and reduces storage costs by 69%. For inference, Tair KVCM manages KVCache across Mempool and remote storage, achieving a 99% effective hit rate and lowering token costs by 50%. Built atop the G3.5 storage layer, KV CacheStore processes long-context agent workloads, expanding cache coverage by 900% and reducing first-token latency by 54%.
Deploying agents at scale relies on AgentCore, which eliminates probabilistic failures by providing a governance platform for standardized infrastructure. It enables:
- Long-running tasks with automated retries and checkpoint recovery.
- Asynchronous execution and resource isolation, with full-link auditing.
This approach raises task completion rates to 99% while cutting TCO by 70%. Agent Sandbox supports 100,000 deployments per minute, with <600ms wake-up times, and integrates with E2B and K8s. The Agentic OS further optimizes token usage by >30% and triples sandbox deployment density.
AliCloud’s "Context Engine" transforms fragmented enterprise data into actionable agent inputs through a three-tier architecture:
- Storage: Handles video frames, point clouds, and trajectories.
- Processing: Integrates and models data quality.
- Context Engineering: The Agent Context tool enhances retrieval accuracy by 50 percentage points and improves token efficiency by over 3x.
Ecosystem updates include OpenLake, which reduces TCO by 38% via a unified multi-modal lakehouse, and Apsara Lakebase, enabling sub-second zero-copy data branching. MC-MaxFrame elevates multi-modal compute performance by 12%, while the Flink Streaming Agent now offers 60+ multi-modal operators for real-time triggers. For post-training, PAI supports asynchronous AgenticRL, completing a SOTA model cycle in 5 days when paired with Qwen models.
For developers, running Qwen1—including the 5-72B-Chat variant—on local devices is now documented. The Qwen team released six model sizes simultaneously: 0.8B, 4B, 7B, 14B, 72B, and 360B, with Qwen3.8 Max and KIMI K3 among the latest additions. To replicate this workflow, users can adjust the model name on the command line for other Qwen variants. Additionally, LlamaEdge (a Rust + Wasm stack) enables deploying Qwen1 applications, offering OpenAI-compatible API services for seamless integration.
In benchmark comparisons, Qwen3.8 Max outperforms GPT-4 in 4 out of 10 tests, as illustrated on Qwen’s GitHub page. The framework’s modular design ensures compatibility with B-Chat and other OpenAI-compatible workflows.
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While it's true that CPFS storage is a critical component, the bottleneck is often in the planning stage rather than hardware limits. For instance, AliCloud's updated CPFS high-performance storage layer supports 100PiB for single file system scale, which is a significant improvement, but it's the planning and orchestration that truly hold agents back before they even hit hardware constraints.
AgentCore is useless to me if I cannot swap Qwen3.8 Max for a smaller model mid-run. For instance, the upcoming V900 super-node, which supports 1,000-card Scale-Up interconnects, could enable seamless model swapping during runtime, ensuring optimal performance without unnecessary resource consumption.
Recursive self-improvement still dies in my planner. But AliCloud’s upgraded CPFS layer, now supporting 100PiB per file system with 100TB/s throughput, directly cuts model startup time by 50% and raises peak compute utilization by 30%—that’s the concrete lever to keep recursive loops alive, not a rescue for a bad plan.