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+# Architecture Overview
+
+RustyTorch++ is a modular ML framework with 56 specialized crates organized for separation of concerns.
+
+## Design Philosophy
+
+### 1. GPU-Native First
+
+Unlike frameworks that wrap CPU-first libraries, RustyTorch++ is designed from the ground up for GPU computation:
+
+- Memory allocated directly on GPU
+- Operations dispatch to GPU kernels by default
+- Zero-copy data transfers where possible
+- Automatic kernel fusion for common patterns
+
+### 2. Memory Safety
+
+Rust's ownership model eliminates common GPU programming errors:
+
+- No double-free of GPU memory
+- No use-after-free tensor operations
+- Compile-time shape validation where possible
+- Thread-safe concurrent GPU access
+
+### 3. Production Ready
+
+Built-in infrastructure for deployment:
+
+- HTTP/gRPC serving APIs
+- Monitoring and telemetry
+- Distributed training support
+- Model versioning and A/B testing
+
+## Crate Architecture
+
+```
+┌─────────────────────────────────────────────────────────────────┐
+│                        Meta Crates                               │
+│  rtx  │  rtx-core  │  rtx-training  │  rtx-inference-stack      │
+└───────────────────────────────────────────────────────────────────┘
+         │                    │                     │
+         ▼                    ▼                     ▼
+┌─────────────────┐  ┌─────────────────┐  ┌─────────────────────────┐
+│   Core (11)     │  │  Training (11)  │  │    Production (6)       │
+│ ─────────────── │  │ ─────────────── │  │ ───────────────────────│
+│ rtx-tensor      │  │ rtx-transformers│  │ rtx-serving-api        │
+│ rtx-runtime     │  │ rtx-distributed │  │ rtx-inference          │
+│ rtx-autograd    │  │ rtx-flash-attn  │  │ rtx-streaming          │
+│ rtx-memory      │  │ rtx-rl          │  │ rtx-hub                │
+│ rtx-kernel      │  │ rtx-compress    │  │ rtx-config             │
+│ rtx-nn          │  │ rtx-federated   │  │ rtx-monitoring         │
+│ rtx-graph       │  │ ...             │  │                        │
+│ ...             │  │                 │  │                        │
+└─────────────────┘  └─────────────────┘  └─────────────────────────┘
+         │                    │                     │
+         └────────────────────┼─────────────────────┘
+                              ▼
+┌─────────────────────────────────────────────────────────────────┐
+│                     Models (7)                                   │
+│  rtx-vision  │  rtx-nlg  │  rtx-multimodal  │  rtx-diffuse      │
+└─────────────────────────────────────────────────────────────────┘
+         │
+         ▼
+┌─────────────────────────────────────────────────────────────────┐
+│              Specialized & Data (14)                             │
+│  rtx-science │ rtx-geom │ rtx-cfd │ rtx-etl │ rtx-feature-store │
+└─────────────────────────────────────────────────────────────────┘
+```
+
+## Core Infrastructure
+
+### Tensor System
+
+The `rtx-tensor` crate provides the foundational tensor type:
+
+```rust
+// GPU-backed tensor with automatic memory management
+let tensor = Tensor::randn([1024, 1024], &Device::cuda(0)?)?;
+
+// Zero-copy views
+let slice = tensor.narrow(0, 0, 512)?;  // No memory copy
+
+// Automatic differentiation ready
+let tracked = tensor.requires_grad(true)?;
+```
+
+### Runtime System
+
+The `rtx-runtime` crate manages GPU resources:
+
+- Device discovery and selection
+- Memory allocation with pooling
+- Stream scheduling for concurrency
+- Kernel compilation and caching
+
+### Autograd Engine
+
+The `rtx-autograd` crate implements automatic differentiation:
+
+- Tape-based gradient recording
+- Topological backward pass
+- Gradient checkpointing for memory efficiency
+- Higher-order derivatives (Hessian, Jacobian)
+
+## Data Flow
+
+```
+Input Data
+    │
+    ▼
+┌─────────────────┐
+│  Preprocessing  │  ← rtx-preprocessing
+│  (Transforms)   │
+└────────┬────────┘
+         │
+         ▼
+┌─────────────────┐
+│    Tensor       │  ← rtx-tensor
+│  (GPU Memory)   │
+└────────┬────────┘
+         │
+         ▼
+┌─────────────────┐
+│     Model       │  ← rtx-nn, rtx-transformers
+│   (Forward)     │
+└────────┬────────┘
+         │
+         ▼
+┌─────────────────┐
+│     Loss        │  ← rtx-losses
+│  (Objective)    │
+└────────┬────────┘
+         │
+         ▼
+┌─────────────────┐
+│   Backward      │  ← rtx-autograd
+│  (Gradients)    │
+└────────┬────────┘
+         │
+         ▼
+┌─────────────────┐
+│   Optimizer     │  ← rtx-transformers (optimizers)
+│   (Update)      │
+└─────────────────┘
+```
+
+## Memory Model
+
+### GPU Memory Hierarchy
+
+```
+┌─────────────────────────────────────┐
+│         Global Memory (HBM)         │  48GB+ on high-end GPUs
+│  ┌─────────────────────────────┐    │
+│  │     Tensor Pool (Cached)    │    │  Pre-allocated blocks
+│  │  ┌───────────────────────┐  │    │
+│  │  │   Active Tensors      │  │    │  Currently in use
+│  │  └───────────────────────┘  │    │
+│  │  ┌───────────────────────┐  │    │
+│  │  │   Gradient Storage    │  │    │  Accumulated gradients
+│  │  └───────────────────────┘  │    │
+│  └─────────────────────────────┘    │
+└─────────────────────────────────────┘
+         │
+         ▼
+┌─────────────────────────────────────┐
+│         Shared Memory (SRAM)        │  Per-block cache
+│         L2 Cache                    │  GPU-wide cache
+└─────────────────────────────────────┘
+```
+
+### Memory Management Strategy
+
+1. **Tensor Pool**: Pre-allocate and reuse GPU memory
+2. **Lazy Allocation**: Allocate on first use
+3. **Memory Mapping**: Use pinned host memory for efficient transfers
+4. **Gradient Checkpointing**: Trade compute for memory in training
+
+## Thread Safety
+
+All public types are `Send + Sync` where appropriate:
+
+```rust
+// Share device handle across threads
+let device = Arc::new(Device::cuda(0)?);
+
+// Parallel data loading
+let handles: Vec<_> = (0..4)
+    .map(|i| {
+        let device = Arc::clone(&device);
+        std::thread::spawn(move || {
+            Tensor::randn([1000, 1000], &device)
+        })
+    })
+    .collect();
+```
+
+## Next Steps
+
+- [Crate Organization](./crate-organization.md) - Detailed crate breakdown
+- [Tensor System](./tensor-system.md) - Deep dive into tensors
+- [Autograd Engine](./autograd-engine.md) - How gradients work