# 🎮 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 pricing

Detailed pricing comparisons (purchase price, cloud on-demand, $/M token inference cost, $/GB HBM, price trends 2024→2026) see:

- [AI-INFRASTRUCTURE.en.md — GPU pricing and price/performance](AI-INFRASTRUCTURE.en.md#gpu-pricing-and-priceperformance)

## 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.en.md](sources/infrastructure/sources.en.md)

*Last revision: 2026-06-03*