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+# 🎮 GPU — architecture, models, virtualization
+
+## GPU models
+
+### NVIDIA
+
+| GPU | Architecture | VRAM | HBM | FP16 (TFLOPS) | FP8 (TFLOPS) | Interconnect | TDP |
+|-----|-------------|------|-----|--------------|-------------|-------------|-----|
+| **A100** | Ampere (2020) | 40/80 GB | HBM2e | 312 | — | NVLink 3 (600 GB/s) | 400 W |
+| **H100** | Hopper (2022) | 80 GB | HBM3 | 1000 | 2000 (sparse) | NVLink 4 (900 GB/s) | 700 W |
+| **H200** | Hopper (2023) | 141 GB | HBM3e | 1650 | ~3300 | NVLink 4 (900 GB/s) | 700 W |
+| **B200** | Blackwell (2024) | 192 GB | HBM3e | 2250 | ~4500 | NVLink 5 (1800 GB/s) | 700 W |
+| **B100** | Blackwell (2024) | 192 GB | HBM3e | ~1800 | ~3600 | NVLink 5 | 700 W |
+| **GB200** | Blackwell (2024) | — | HBM3e | 4500 (dual) | 9000 (dual) | NVLink 5 | 2700 W |
+
+### AMD
+
+| GPU | Architecture | VRAM | HBM | FP16 (TFLOPS) | Interconnect | TDP |
+|-----|-------------|------|-----|--------------|-------------|-----|
+| **MI250X** | CDNA 2 (2021) | 128 GB | HBM2e | 383 | Infinity Fabric | 500 W |
+| **MI300X** | CDNA 3 (2023) | 192 GB | HBM3 | ~2600 | Infinity Fabric (896 GB/s) | 750 W |
+| **MI350** | CDNA 4 (2025) | 288 GB | HBM3e | ~3500 | Infinity Fabric | 750 W |
+
+## GPU interconnects
+
+| Technology | Provider | Bandwidth | Topology | Use case |
+|------------|-------------|-----------|-----------|----------|
+| **NVLink 4** | NVIDIA | 900 GB/s (18× 50 GB/s) | GPU-GPU direct | AI training (H100, H200) |
+| **NVLink 5** | NVIDIA | 1800 GB/s (18× 100 GB/s) | GPU-GPU direct | AI training (B200, GB200) |
+| **Infinity Fabric** | AMD | 896 GB/s | GPU-GPU + CPU-GPU | AI training (MI300X, MI350) |
+| **NVSwitch** | NVIDIA | 900 GB/s per GPU (NVLink) | Full-mesh (256 GPU) | DGX SuperPOD, HGX |
+| **InfiniBand (NDR)** | NVIDIA/Mellanox | 400 Gbps per port | GPU-NIC direct, RDMA | Distributed training, HPC |
+| **PCIe 5.0** | Standard | 63 GB/s per x16 | CPU-GPU | Inference, rendering |
+| **Ethernet (RoCE v2)** | Standard | 100/200/400 GbE | GPU-NIC, RDMA over converged ethernet | AI inference, storage |
+
+### GPU direct communication
+
+``` 
+GPU 0 ──NVLink── GPU 1        GPU 0 ───PCIe─── CPU ───PCIe─── GPU 1
+  │                            │
+  │                            │
+NVSwitch                     InfiniBand
+  │                            │
+  │                            │
+GPU 2 ──NVLink── GPU 3        GPU 2 ───PCIe─── CPU ───PCIe─── GPU 3
+
+NVLink topologie (GPU direct)   PCIe topologie (CPU mediated)
+```
+
+- **GPU Direct RDMA** — GPU ↔ NIC without CPU (InfiniBand, RoCE)
+- **GPU Direct Storage** — GPU ↔ NVMe without CPU (NVIDIA Magnum IO)
+- **NVSwitch** — full bisection bandwidth between all GPUs in a node
+
+## GPU virtualization
+
+| Technology | Description | GPU support | Use case |
+|------------|-------|-------------|----------|
+| **NVIDIA vGPU (Grid)** | Time slicing + dedicated profiles | A-series (VDI), Q-series (pro viz), B-series (AI) | VDI, virtualized AI |
+| **NVIDIA MIG** | Hardware GPU partitioning | A100 (7 inst.), H100/H200/B200 | AI inference, multi-tenant GPU |
+| **AMD MxGPU** | SR-IOV, hardware partitioning | AMD MI (pro), Radeon Pro | VDI, cloud gaming |
+| **Intel SG (SG1)** | SR-IOV, hardware partitioning | Intel SG1, Flex, Arc | VDI, media transcoding |
+| **GPU passthrough** | Dedicated GPU to whole VM (VFIO-pci) | All GPUs | AI training, HPC, highest performance |
+
+### MIG partition table (A100 / H100)
+
+| GPU | Partition profile | GPU Memory | Compute units |
+|-----|------------------|-----------|--------------|
+| **A100 80 GB** | 1g.5gb | 5 GB | 1 |
+| A100 80 GB | 2g.10gb | 10 GB | 2 |
+| A100 80 GB | 3g.20gb | 20 GB | 3 |
+| A100 80 GB | 7g.40gb | 40 GB | 7 |
+| A100 80 GB | Full (7× 1g) | 7 × 5 GB | 7 instances |
+| **H100 80 GB** | 1g.6gb+me | 6 GB | 1 |
+| H100 80 GB | 2g.12gb+me | 12 GB | 2 |
+| H100 80 GB | 3g.24gb+me | 24 GB | 3 |
+| H100 80 GB | 7g.80gb | 80 GB | 7 |
+
+## GPU use cases
+
+### AI Training
+
+- **Models**: LLM (70B-405B+), vision, multimodal
+- **GPU**: H100, B200, GB200, MI300X
+- **Interconnect**: NVLink 5 / Infinity Fabric (within node), InfiniBand NDR (between nodes)
+- **Parallelism**: Data Parallel (DDP), Tensor Parallel (TP), Pipeline Parallel (PP), Fully Sharded (FSDP)
+- **Framework**: PyTorch (NCCL), JAX (XLA), DeepSpeed, Megatron-LM
+- **Tips**:
+  - GB200: 2× B200 connected via NVLink, 8 GPU → 4 GB200
+  - DGX B200 / HGX B200: standard building block
+  - InfiniBand: fat tree topology for all-reduce optimization
+
+### AI Inference
+
+- **Models**: LLM serving, embedding, image gen
+- **GPU**: A100, H200, B200 (larger VRAM for larger models)
+- **Techniques**: MIG partition, TensorRT-LLM, vLLM, Triton Inference Server
+- **Quantization**: FP8, INT8, INT4 → lower VRAM, higher throughput
+- **Latency**: batch size optimization, dynamic batching, continuous batching
+- **Scale**: on-prem (2-32 GPU) / cloud (elastic)
+
+### VDI (Virtual Desktop Infrastructure)
+
+- **GPU**: NVIDIA A16 (1 GPU = 16 users), A10 (1 GPU = 4 users)
+- **Technology**: vGPU (Grid), AMD MxGPU
+- **Protocols**: VMware Blast, Citrix HDX, Microsoft RDP, PC-over-IP (HP Teradici)
+- **Use case**: CAD (CATIA, SolidWorks), Office, engineering, healthcare (PACS)
+
+### Rendering and VFX
+
+- **GPU**: NVIDIA RTX 6000 Ada, RTX A6000, AMD Radeon Pro W7900
+- **Rendering**: Blender (Cycles/OptiX), V-Ray, Octane Render, Redshift
+- **Denoising**: AI-accelerated denoising on GPU
+- **Farm rendering**: Deadline, Qube! (job scheduler)
+
+## GPU server form factors
+
+| Form factor | GPU count | Power | Cooling | Example |
+|------------|-----------|-------|---------|---------|
+| **1U** | 1-2 | 700-1400 W | Air (high-RPM) | Dell XR4510c |
+| **2U** | 4-8 | 3-6 kW | Air / Liquid | Dell R760xa, HPE DL380a |
+| **4U** | 8-10 | 5-8 kW | Liquid | NVIDIA DGX H100, Dell R760xa |
+| **8U / Chassis** | 8-16 | 10-20 kW | Liquid (CDU) | NVIDIA HGX, Supermicro SYS-821GE |
+
+## OpenStack Cyborg (GPU lifecycle management)
+
+Cyborg is an OpenStack service for managing accelerators (GPU, FPGA, DPU, NPU).
+
+### Key capabilities
+
+- **Discovery** — automatic GPU detection on compute nodes (NVIDIA, AMD, Intel)
+- **Inventory** — tracking available accelerators in the cluster
+- **Lifecycle** — attach/detach GPU to VM, firmware update, reset
+- **Scheduling** — Placement API for GPU-aware scheduling (Nova)
+- **Cyborg API** — REST API for accelerator management
+
+### Integration
+
+| Component | Role |
+|------------|------|
+| **Nova** | VM scheduling with GPU requirements (extra_specs: `accel:device_profile`) |
+| **Placement** | Resource provider for GPU (inventory, traits) |
+| **Neutron** | SR-IOV VF passthrough for GPU networking |
+| **Ironic** | Bare metal + GPU provisioning |
+
+## Sources
+
+Links, books and standards: [sources/infrastructure/sources.md](sources/infrastructure/sources.md)
+
+*Last revision: 2026-06-03*