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1. Inside Deepseek 3FS: A
Deep Dive into
AI-Optimized Distributed
Storage
Stephen Pu
[email protected]

2. Agenda
■Parallel file system landscape for AI
■3FS deep dive
●System Architecture
●Software Components
●Read / Write Flows
●FUSE (hf3fs_fuse) & USRBIO
■Which AI storage stack solution is right for your
needs?

3. Parallel file system landscape for AI
Fire-Flyer File System (3FS)
Infinia

4. Introducing 3FS
DeepSeek 3FS (Fire-Flyer File System) is a high-performance parallel
file system designed to address the challenges of AI training and
inference workloads.
●RDMA and SSD Flash Utilization
●Decentralized Design
●FUSE Optimization (Async Zero-copy API)
●Strong Consistency (Eventual Consistency)

5. System
Architecture
●FUSE
●Native C++ API
（USRBIO）
ETCD /
ZooKeeper
Metadata
Service
Storage
Service
SSD SSD SSD
Foundation
DB (K/V)
Chunk
Store
RocksDBChunk AllocatorCache
CRAQ
Primary Node
Metadata
Service
Storage
Service
Foundation
DB (K/V)
Chunk
Store
RocksDBChunk AllocatorCache
CRAQ
Node
Client
Verbs SSD SSD SSD Verbs
RDMA
Infiniband
gRPC

6. System Architecture

7. Software Components
•ETCD / ZooKeeper
•Metadata Service
•FoundationDB
•RocksDB
•Rendezvous Hashing
•Replication Chain
•Storage Service
•CRAQ
•Chunk Store
•Chunk Allocator
•Chunk Metadata Cache

8. Code Structure Overview
GitHub Source Code Main Directory Structure

3FS
├── cmake/ # CMake build-related files
├── docs/ # Design documents and user guides
├── examples/ # Example code
├── scripts/ # Auxiliary scripts (deployment, testing, etc.)
├── src/ # Main source code directory
│ ├── client/ # Client implementation
│ ├── common/ # Common components (network, storage, protocols, etc.)
│ ├── metadata/ # Metadata management service
│ ├── storage/ # Storage service
│ ├── cluster/ # Cluster manager
│ ├── transport/ # Network communication layer (including RDMA support)
├── tests/ # Test cases
└── CMakeLists.txt # CMake configuration file

9. 3FS/
├── cmake/ # CMake build-related files
├── docs/ # Design documents and user guides
├── examples/ # Example code
├── scripts/ # Auxiliary scripts (deployment, testing, etc.)
├── src/ # Main source code directory
│ ├── client/ # Client implementation
│ │ ├── api/ # Client API definitions
│ │ ├── cache/ # Data caching mechanisms
│ │ ├── transport/ # Client-side network communication
│ ├── common/ # Common components (network, storage, protocols, etc.)
│ │ ├── net/ # Network abstraction layer
│ │ ├── data/ # Data structures for storage and metadata
│ │ ├── proto/ # Protocol definitions for inter-component communication
│ ├── metadata/ # Metadata management service
│ │ ├── server/ # Metadata server implementation
│ │ ├── storage/ # Metadata storage backend
│ │ ├── consistency/ # CRAQ and consistency management
│ ├── storage/ # Storage service
│ │ ├── engine/ # Data storage engine
│ │ ├── replication/ # Replication and high availability
│ │ ├── rdma/ # RDMA-based storage optimizations
│ ├── cluster/ # Cluster manager
│ │ ├── discovery/ # Node discovery and membership management
│ │ ├── load_balance/ # Load balancing mechanisms
│ │ ├── failover/ # Failure detection and recovery
│ ├── transport/ # Network communication layer (including RDMA support)
│ │ ├── rdma/ # RDMA transport layer
│ │ ├── tcp/ # TCP transport layer
│ │ ├── messaging/ # Message serialization and dispatch
├── tests/ # Test cases
│ ├── integration/ # Integration tests
│ ├── unit/ # Unit tests
└── CMakeLists.txt # CMake configuration file
Directory Structure

10. Data File Store
Each SSD deploys a single Chunk Store by default.
And A RocksDB instance, Chunk Allocator, Cache Service (Chunk Metadata)
SSD
Chunk
Store
RocksDB
Chunk
Allocator
Cache
Service
Data File
Chunk 1 Chunk 2 Chunk N
…
•RocksDB Instance: Maintains data block metadata
and other system information.
•Cache (In-Memory): Stores data block metadata in
memory to improve query performance.
•Chunk Allocator: Facilitates fast allocation of new
data blocks.

11. File Write Flow Client request
Write()
RDMA Network
src/client/
Parameters:
•path
•offset
•data content
src/common/net
gRPC
RocksDB
Foundation
DB
Metadata
Service
src/mds/
•FUSE
•API
•SDK
Copy-on-Write, COW
Allocates a new block before modifying data.
The old block remains readable until all
handles are released.

•mds_lookup()
•mds_allocate_chunk
•mds_commit()
Block
Engine
src/block/
Storage
•chunk_alloc()
•get_block_metadata()
•update_block_metadata()
Chunk
Allocator
src/storage/
•storage_write()
•submit_io_request()
•commit()
•sync_metadata_cache()
•send_response()
inode
Storage Service

12. File Read Flow Client request
read()
RDMA Network
Libfabric
src/client/
Parameters:
•path
•offset
•size
src/common/net
gRPC
RocksDB
Foundation
DB
Metadata
Service
src/mds/
•FUSE
•API
•SDK
•mds_lookup()
•get_block_location()
•chunk_cache_hit()
Block
Engine
src/block/
Storage
•chunk_alloc()
•get_block_metadata()
•update_block_metadata()
Chunk
Allocator
src/storage/
•decode_block_data()
•apply_read_offset()
•return_read_data()
inode
Storage Service
•net_recv_request()
•parse_read_request()
•dispatch_read_operation()
•get_block_data()
•read_from_cache()
•read_from_ssd()
•rdma_transfer_data()

13. Chunk Store – Physical Data Block
•Data blocks are ultimately stored in physical blocks
1.Physical Block Size: Ranges from 64KiB to 64MiB, increasing in powers of two, with 11
different size classes.
2.Allocation Strategy: The allocator selects the physical block size closest to the actual block
size.
•Resource Pool Management
1.Each physical block size corresponds to a resource pool, with 256 physical files per pool.
2.The usage state of physical blocks is tracked using an in-memory bitmap.
•Recycling and Allocation
1.When a physical block is reclaimed, its bitmap flag is set to 0, its storage space is preserved,
and it is prioritized for future allocations.
2.If available physical blocks are exhausted, the system calls fallocate() to allocate a large
contiguous space within a physical file, generating 256 new physical blocks to minimize disk
fragmentation.

14. FUSE & USRBIO

15. FUSE
•Based on the libfuse low-level API and requires libfuse version 3.16.1 or higher.
•4 kernel-user context switches and one to two data copies, leading to performance
bottlenecks.
•POSIX: Not support file lock and xattr
•Directory Traversal: readdirplus API
•readahead: 16 MB by default
•Write Buffer：ʼDIOʼ and ʻBuffered IOʼ
•Delayed File Size Update： 30s, close, fsync
•Async Close
•Deleting Files in Write Mode is delayed: write mode, read mode
•Recursive Directory Deletion: rm -rf

16. USRBIO
•A user-space, asynchronous, zero-copy API.
•Requires modifications to the application source code for adaptation, making the
adoption threshold higher.
•Eliminating context switches and data copies, thereby achieving optimal
performance.

17. USRBIO

18. 3FS Design Tradeoffs Highlights

Strengths Costs
FUSE and Client Access
3FSʼs custom API USRBIO delivers good
performance
Low usability as users need to modify each
application source code to utilize the 3FS
custom API;
FUSE performance is very low as 3FS is not
designed to optimize for FUSE
Read vs Write Optimized for read-heavy scenarios
Sacrificed write performance, so users with
write needs will not fully appreciate the benefits
of HPC
File Size Optimizations Optimized for large data files
Small file workloads are a second class citizen
with lower performance despite small files
expedition design

19. Positioning of Alluxio and 3FS
Alluxio
Alluxio is a data abstraction and distributed caching
layer between compute and storage layers. Alluxio is
NOT a PFS (Parallel File System).
Key capabilities that a typical PFS does not provide
include:
✔Deep integration with compute frameworks and
cloud storage ecosystems.
✔Providing high-throughput, low-latency hot data
caching using commodity hardware on top of data
lakes.
✔Frequently utilized for supporting multi-cloud,
hybrid cloud, and cross-data-center data access.
Multi-cloud/hybrid
cloud/cross-data-center
Low-latency
Massive small data files
3FS
3FS is a parallel file system designed to leverage
high-end hardware.
✔3FS abandons the “general-purpose file system”
approach of being comprehensive and instead
focuses on large data files and high-throughput
scenarios in subset of AI workloads.
✔For the target workloads, it makes the trades-off in
optimization by leveraging high-end hardware like
RDMA and NVMe.
✔At the end of the day, 3FS is a new member of the
HPC storage family, competing with existing PFSes
such as GPFS and Lustre

Large data files
High bandwidth
High-end hardware
Alluxio unifies data in
local high-speed storage
(including 3FS and other
PFS) and data lake via
Caching, Data Lifecycle
Management, Data
Migration
Complimentary

20. 27
Which AI storage stack is right for you?
Low cost + massive
scale
✅ Low cost, high reliability due to global distribution
❌ Low performance
Low cost + massive
scale + low latency
On top of object storage, Alluxio:
✅ enables low-latency and high throughput with
commodity storage such as S3
✅ manages data loading transparently
✅ provides hybrid and multi-cloud support
Leverage high end
hardware with custom
solution
✅ High performance from leveraging RDMA
❌ Need to manually copy data into 3FS
❌ High cost of specialized hardware
Primary Need
Leverage high end
hardware with
global/remote data
lakes
❌✅ Fully leverage your existing high end hardware
✅ Alluxio takes care of global data transfer and removes
need to manually copy data into 3FS
✅ Alluxio provides hybrid and multi-cloud support
Best Fit Trade Offs
S3 like object
storage alone
S3 +
3FS
3FS +

21. Alluxio AI Overview

Alluxio Accelerates AI
by solving speed, scale, & scarcity challenges
through high-performance, distributed caching
and unified access to heterogeneous data sources.

22. Large-scale distributed caching (petabytes of data; billions of objects)
-Eliminates I/O bottlenecks
-Increases GPU utilization
-Improves performance across AI lifecycle
Alluxio Accelerates AI Workloads
MODEL TRAINING
& FINE TUNING
MODEL
DISTRIBUTION
INFERENCE
SERVING
AI LIFECYCLE
DATA COLLECTION
& PREPROCESSING

23. Future
Stay tuned for Part 2 of this webinar series:
●RDMA Network
●CRAQ
●Cluster / Node Management
●Disaster Recovery Algorithm

24. Q&A
twitter.com/alluxio

slackin.alluxio.io/linkedin.com/alluxiowww.alluxio.io