deepseek-ai/3FS resource

Health v2 · maintenance only

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10166 stars · 1085 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

Fire-Flyer File System (3FS) is a high-performance distributed file system from DeepSeek designed for AI training and inference workloads. It leverages NVMe SSDs and RDMA networks with a disaggregated architecture and CRAQ-based strong consistency to provide a shared POSIX-like storage layer.

Use cases

• high-throughput shared storage for AI training clusters
• fast parallel checkpointing for large-scale model training
• random-access dataloaders without prefetching or shuffling
• KVCache backend for LLM inference
• organizing large volumes of data pipeline intermediate outputs
• running sort and analytics workloads on massive datasets

When to choose

• you need to aggregate throughput of thousands of NVMe SSDs over RDMA/InfiniBand
• you want strong consistency with a familiar file interface for distributed AI workloads
• you need cost-effective, high-capacity KVCache for inference instead of DRAM
• you run large-scale training that requires high-throughput checkpointing

When to avoid

• you need a general-purpose POSIX file system for small clusters or commodity networks without RDMA
• you lack dedicated NVMe storage nodes and high-speed networking hardware
• you need a mature turnkey solution rather than a system requiring careful cluster setup
• your workload is small enough for a single-node or cloud object storage solution

Facets

infra-config · maturity active

file-system caching microservices machine-learning big-data infrastructure-as-code cpp self-hosted distributed-file-system rdma nvme ai-training checkpointing kvcache craq foundationdb high-performance-storage storage linux

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markdown · JSON · MCP: product_card(name="deepseek-ai/3FS")