To compare the performance of 3FS (Fire-Flyer File System) with other filesystem products for AI training use cases, we’ll evaluate it against some prominent filesystems commonly used in high-performance computing (HPC) and AI workloads: Lustre, CephFS, GPFS (IBM Spectrum Scale), and JuiceFS. Since 3FS is a relatively new distributed filesystem from DeepSeek, designed specifically for AI training and inference workloads, its performance metrics will be contextualized using available data and typical characteristics of these competitors. Here's the breakdown:
3FS Overview
3FS is a high-performance, parallel, distributed filesystem built by DeepSeek to leverage modern SSDs and RDMA (Remote Direct Memory Access) networks. It’s optimized for AI workloads, offering features like strong consistency via Chain Replication with Apportioned Queries (CRAQ), a disaggregated architecture combining thousands of SSDs, and stateless metadata services backed by a transactional key-value store (e.g., FoundationDB).
Reported Performance:
Aggregate read throughput of 6.6 TiB/s (approximately 6,758 GB/s) on a 180-node cluster, each with 2×200Gbps InfiniBand NICs and sixteen 14TiB NVMe SSDs, alongside 500+ client nodes.
Throughput of 3.66 TiB/min (about 62.5 GB/s) in a specific test scenario (possibly GraySort benchmark).
Use Case Fit: Tailored for AI training, emphasizing high throughput for large datasets and low-latency access to massive numbers of files.
Comparison with Other Filesystems
1. Lustre
Overview: A widely-used parallel filesystem in HPC and supercomputing, known for scalability and high throughput. It separates metadata and data services, using object storage targets (OSTs) and metadata servers (MDS).
Performance:
Example: A large Lustre deployment (e.g., at ORNL’s Summit supercomputer) achieves read/write throughput of 2.5–5 TB/s (2,560–5,120 GB/s) with thousands of nodes and disks, though this varies by configuration.
Per-disk throughput is often cited around 50–100 MB/s/HDD in HDD-based setups, but SSD-based Lustre can hit 1–2 GB/s per OST with NVMe.
AI Training Fit:
Strengths: Excellent for sequential, large-file I/O common in HPC and some AI datasets (e.g., large video or image files).
Weaknesses: Metadata performance can bottleneck with millions of small files (common in AI preprocessing), and setup complexity is high.
Comparison to 3FS:
3FS’s 6.6 TiB/s exceeds typical Lustre deployments, likely due to its SSD+RDMA optimization. Lustre can scale similarly with enough nodes, but 3FS seems to outperform per-node due to its disaggregated design and modern hardware focus.
2. CephFS
Overview: Part of the Ceph ecosystem, a distributed filesystem with a unified object, block, and file interface. It uses a dynamic metadata server (MDS) cluster and relies on object storage daemons (OSDs).
Performance:
Throughput varies widely: A well-tuned CephFS cluster with SSDs might achieve 1–3 TB/s (1,024–3,072 GB/s) in large setups, but smaller clusters often see 100–500 GB/s.
Metadata operations lag with small files; benchmarks show 10–50K IOPS per MDS, scaling with more MDS nodes.
AI Training Fit:
Strengths: Unified storage (object+file) is versatile for AI pipelines; good for mixed workloads.
Weaknesses: Slower metadata handling and lower throughput compared to 3FS or Lustre for massive parallel reads.
Comparison to 3FS:
3FS’s 6.6 TiB/s dwarfs CephFS’s typical throughput, especially in read-heavy AI training scenarios. CephFS struggles with the extreme parallelism and small-file intensity that 3FS targets.
3. GPFS (IBM Spectrum Scale)
Overview: A high-performance, parallel filesystem from IBM, used in enterprise and HPC environments. It supports distributed metadata and scales across thousands of nodes.
Performance:
Example: IBM Elastic Storage Server (ESS) GL4S model (HDD-based) delivers 24 GB/s with 334 drives (~72 MB/s/drive), while SSD configs can hit 50–100 GB/s in smaller clusters. Large-scale setups reach 1–3 TB/s.
NVMe-optimized GPFS can exceed 5 TB/s with enough nodes.
AI Training Fit:
Strengths: Strong consistency and scalability; good for large-scale, enterprise-grade AI training.
Weaknesses: Centralized metadata can bottleneck with small files; less optimized for RDMA compared to 3FS.
Comparison to 3FS:
3FS’s 6.6 TiB/s outpaces most GPFS deployments, especially in SSD+RDMA contexts. GPFS can match or approach this with massive infrastructure, but 3FS’s design gives it an edge in AI-specific throughput.
4. JuiceFS
Overview: An open-source distributed filesystem optimized for cloud and AI workloads, using a metadata engine (e.g., Redis) and object storage (e.g., S3) backend with local caching.
Performance:
Throughput depends on the backend: With SSD caching and high-bandwidth networks, it can achieve 10–50 GB/s in small clusters. Large-scale tests are less documented but suggest 100–500 GB/s with optimization.
Excels with small files due to metadata engine; read performance scales with cache hit rates.
AI Training Fit:
Strengths: Handles massive small-file datasets (e.g., millions of images) well; cost-effective with cloud storage.
Weaknesses: Throughput caps out lower than 3FS due to reliance on object storage and network latency.
Comparison to 3FS:
3FS’s 6.6 TiB/s far exceeds JuiceFS’s practical limits, as JuiceFS prioritizes flexibility over raw throughput. For AI training requiring terabytes-per-second reads, 3FS is superior.
Performance Metrics Summary
Filesystem
Peak Throughput (Example)
AI Training Strengths
AI Training Weaknesses
3FS
6.6 TiB/s (6,758 GB/s)
Extreme throughput, RDMA+SSD optimized
Limited real-world deployment data
Lustre
2.5–5 TB/s (2,560–5,120 GB/s)
Scalable, large-file I/O
Metadata bottlenecks with small files
CephFS
1–3 TB/s (1,024–3,072 GB/s)
Unified storage, versatile
Lower throughput, metadata scaling
GPFS
1–5 TB/s (1,024–5,120 GB/s)
Enterprise-grade, consistent
Less RDMA focus, metadata limits
JuiceFS
100–500 GB/s (estimated)
Small-file handling, cloud-friendly
Throughput limited by backend
Analysis for AI Training Use Case
Throughput: 3FS’s 6.6 TiB/s is a standout, likely due to its focus on NVMe SSDs and RDMA, making it ideal for feeding massive datasets to GPU clusters in AI training. Lustre and GPFS can approach this in large-scale HPC setups, but 3FS seems to achieve it with fewer nodes.
Small Files: AI training often involves millions of small files (e.g., images, text). 3FS’s stateless metadata and key-value store likely give it an edge over Lustre and GPFS, which struggle with metadata-heavy workloads. JuiceFS competes here but lacks 3FS’s throughput.
Scalability: All systems scale well, but 3FS’s disaggregated architecture and CRAQ consistency could simplify scaling for AI compared to Lustre’s complex OST/MDS setup or CephFS’s MDS limits.
Ease of Use: 3FS’s appliance-like design (per DeepSeek’s claims) may reduce complexity compared to Lustre or GPFS, though JuiceFS wins for cloud integration.
Conclusion
For AI training use cases requiring extreme read throughput and parallelism (e.g., large-scale deep learning with terabyte-sized datasets), 3FS appears to outperform Lustre, CephFS, GPFS, and JuiceFS based on its reported 6.6 TiB/s metric. Its design leverages modern hardware (NVMe SSDs, RDMA) more aggressively than competitors, giving it a theoretical edge in feeding data-hungry GPU clusters. However, its real-world adoption is less documented compared to established players like Lustre or GPFS, so practical performance may depend on specific configurations and workloads. If your AI training prioritizes small-file access over raw throughput, JuiceFS could be a contender, but for peak performance, 3FS looks like the leader among these options.
If you’d like me to search for more specific benchmarks or X posts about 3FS in AI contexts, let me know!
性能与易用性
多样化工作负载支持
主要特点
性能评估
