DeepSeek Elastic Compute Presenta Infraestructura de Sandbox Escalable para Modelos de Lenguaje Grande

What is DeepSeek Elastic Compute's scalable sandbox infrastructure?

Large language models (LLMs) require isolated, stateful execution environments to inspect repositories, invoke tools, and interact with task-specific services. DeepSeek Elastic Compute (DSec) addresses this need with a production sandbox platform that exposes multiple sandbox backends through a unified SDK.

How does DeepSeek Elastic Compute support large language models and LLM optimization?

DSec coordinates placement and lifecycle management across the cluster, allowing for high-density execution and efficient resource utilization. The platform composes environments from independently versioned layers and combines memory sharing, reclamation, and CPU scheduling to minimize overhead. Additionally, DSec loads image data on demand from the Fire-Flyer File System (3FS), a cluster-wide distributed filesystem.

Co-designed with the reinforcement learning (RL) framework, DSec decouples stateful rollout execution from preemptible GPU training, preserving rollout state while reclaiming idle resources. This design mitigates agent misbehavior, such as reward hacking, and supports over 380,000 concurrent sandboxes, sustaining over 5,000 sandbox creations per second.

A single production-scale unit of DSec spans around 160 nodes, serving approximately 3 million sandboxes per day. Evaluation and deployment experience demonstrate that DSec's mechanisms reduce environment setup and image-distribution overhead, improve memory efficiency, and preserve latency-sensitive performance under high-density overcommit.

By providing a scalable and efficient sandbox infrastructure, DeepSeek Elastic Compute enables the development and training of large language models and LLM optimization, supporting advancements in areas like answer engine optimization, including zero-click search and agentic SEO and generative engine optimization (AEO) and LLM visibility and AI search optimization.

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