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<title>3FS - High-Performance Distributed File System | DeepSeek OpenInfra</title>
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<span class="block">3FS Distributed File System</span>
<span class="block text-blue-200">For AI Workloads</span>
</h1>
<p class="mt-6 max-w-2xl mx-auto text-xl text-blue-100">
A high-performance distributed file system designed to address the challenges of AI training and inference workloads.
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Get Started
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Trusted by leading AI organizations
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<h2 class="text-3xl font-extrabold text-gray-900 sm:text-4xl">
Optimized for AI Workloads
</h2>
<p class="mt-4 max-w-2xl text-xl text-gray-500 mx-auto">
3FS is specifically designed to handle the unique demands of AI training and inference at scale.
</p>
</div>
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<h3 class="text-lg font-medium text-gray-900">High Performance</h3>
<p class="mt-2 text-gray-500">
Achieve unprecedented throughput and low latency for large-scale model training with our optimized data access patterns.
</p>
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<h3 class="text-lg font-medium text-gray-900">Scalability</h3>
<p class="mt-2 text-gray-500">
Seamlessly scale from single-node deployments to thousands of nodes without performance degradation.
</p>
</div>
<!-- Feature 3 -->
<div class="card-hover bg-white p-8 rounded-xl shadow-sm">
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<i class="fas fa-shield-alt text-2xl"></i>
</div>
<h3 class="text-lg font-medium text-gray-900">Fault Tolerance</h3>
<p class="mt-2 text-gray-500">
Automatic data replication and recovery ensure training jobs continue even with node failures.
</p>
</div>
<!-- Feature 4 -->
<div class="card-hover bg-white p-8 rounded-xl shadow-sm">
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<i class="fas fa-memory text-2xl"></i>
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<h3 class="text-lg font-medium text-gray-900">Memory-Centric</h3>
<p class="mt-2 text-gray-500">
Intelligent caching and prefetching minimize disk I/O bottlenecks during training iterations.
</p>
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<!-- Feature 5 -->
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<h3 class="text-lg font-medium text-gray-900">Random Access</h3>
<p class="mt-2 text-gray-500">
Efficient random access patterns optimized for shuffling large datasets during training.
</p>
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<!-- Feature 6 -->
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<i class="fas fa-plug text-2xl"></i>
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<h3 class="text-lg font-medium text-gray-900">Framework Integration</h3>
<p class="mt-2 text-gray-500">
Native support for TensorFlow, PyTorch, and other popular ML frameworks with minimal configuration.
</p>
</div>
</div>
</div>
</div>
<!-- Architecture Section -->
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Architecture Overview
</h2>
<p class="mt-4 max-w-2xl text-xl text-gray-500 lg:mx-auto">
3FS is built with a modular architecture that separates control and data planes for optimal performance.
</p>
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<div class="mt-16">
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<!-- Metadata Service -->
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<h3 class="text-lg font-medium text-gray-900">Metadata Service</h3>
<p class="mt-2 text-gray-500">
Manages file system namespace, access control, and coordinates data placement across the cluster.
</p>
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<div>
<h3 class="text-lg font-medium text-gray-900">Data Nodes</h3>
<p class="mt-2 text-gray-500">
Store actual file data with intelligent block placement and replication for fault tolerance.
</p>
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<div>
<h3 class="text-lg font-medium text-gray-900">Client Library</h3>
<p class="mt-2 text-gray-500">
Provides POSIX-like interface with optimizations for AI workloads, including prefetching and caching.
</p>
</div>
</div>
</div>
</div>
</div>
<!-- Performance Section -->
<div id="performance" class="py-16 bg-gray-900 text-white">
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<div class="lg:text-center">
<h2 class="text-3xl font-extrabold sm:text-4xl">
Benchmark Results
</h2>
<p class="mt-4 max-w-2xl text-xl text-blue-200 lg:mx-auto">
3FS outperforms traditional distributed file systems for AI workloads.
</p>
</div>
<div class="mt-16">
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<!-- Title -->
<text x="400" y="30" text-anchor="middle" font-family="Arial" font-size="16" font-weight="bold" fill="white">Random Read Performance (4K blocks)</text>
<!-- Comparison text -->
<text x="400" y="400" text-anchor="middle" font-family="Arial" font-size="14" fill="#a5b4fc">3FS provides 2.5x better performance than Lustre for AI workloads</text>
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<div class="mt-12 grid grid-cols-1 gap-8 md:grid-cols-3">
<div>
<h3 class="text-lg font-medium text-blue-400">2.5x Faster</h3>
<p class="mt-2 text-blue-100">
Than NFS for small random reads common in training data loading.
</p>
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<div>
<h3 class="text-lg font-medium text-blue-400">40% Lower Latency</h3>
<p class="mt-2 text-blue-100">
Than CephFS for metadata operations critical to AI pipelines.
</p>
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<div>
<h3 class="text-lg font-medium text-blue-400">3x More Scalable</h3>
<p class="mt-2 text-blue-100">
Than Lustre when handling thousands of concurrent clients.
</p>
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</div>
</div>
</div>
</div>
<!-- Get Started Section -->
<div id="get-started" class="py-16 bg-white">
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Deploy 3FS in minutes with our simple installation process.
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<div class="mb-2"># Install 3FS client and server packages</div>
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<div class="mb-2"># Configure metadata server</div>
<div class="mb-2">sudo 3fs-mds init --cluster-name mycluster</div>
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<div class="mb-2"># Add storage nodes</div>
<div class="mb-2">sudo 3fs-dn join --mds-host mds.mycluster.internal</div>
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<div class="mb-2"># Mount 3FS volume</div>
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