129 lines
5.9 KiB
Markdown
129 lines
5.9 KiB
Markdown
# 🎮 GPU — architektura, modely, virtualizace
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## GPU modely
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### NVIDIA
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| GPU | Architektura | VRAM | HBM | FP16 (TFLOPS) | FP8 (TFLOPS) | Interconnect | TDP |
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|-----|-------------|------|-----|--------------|-------------|-------------|-----|
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| **A100** | Ampere (2020) | 40/80 GB | HBM2e | 312 | — | NVLink 3 (600 GB/s) | 400 W |
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| **H100** | Hopper (2022) | 80 GB | HBM3 | 1000 | 2000 (sparse) | NVLink 4 (900 GB/s) | 700 W |
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| **H200** | Hopper (2023) | 141 GB | HBM3e | 1650 | ~3300 | NVLink 4 (900 GB/s) | 700 W |
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| **B200** | Blackwell (2024) | 192 GB | HBM3e | 2250 | ~4500 | NVLink 5 (1800 GB/s) | 700 W |
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| **B100** | Blackwell (2024) | 192 GB | HBM3e | ~1800 | ~3600 | NVLink 5 | 700 W |
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| **GB200** | Blackwell (2024) | — | HBM3e | 4500 (dual) | 9000 (dual) | NVLink 5 | 2700 W |
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### AMD
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| GPU | Architektura | VRAM | HBM | FP16 (TFLOPS) | Interconnect | TDP |
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|-----|-------------|------|-----|--------------|-------------|-----|
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| **MI250X** | CDNA 2 (2021) | 128 GB | HBM2e | 383 | Infinity Fabric | 500 W |
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| **MI300X** | CDNA 3 (2023) | 192 GB | HBM3 | ~2600 | Infinity Fabric (896 GB/s) | 750 W |
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| **MI350** | CDNA 4 (2025) | 288 GB | HBM3e | ~3500 | Infinity Fabric | 750 W |
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## GPU interconnects
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| Technologie | Poskytovatel | Bandwidth | Topologie | Use case |
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|------------|-------------|-----------|-----------|----------|
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| **NVLink 4** | NVIDIA | 900 GB/s (18× 50 GB/s) | GPU-GPU direct | AI training (H100, H200) |
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| **NVLink 5** | NVIDIA | 1800 GB/s (18× 100 GB/s) | GPU-GPU direct | AI training (B200, GB200) |
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| **Infinity Fabric** | AMD | 896 GB/s | GPU-GPU + CPU-GPU | AI training (MI300X, MI350) |
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| **NVSwitch** | NVIDIA | 900 GB/s per GPU (NVLink) | Full-mesh (256 GPU) | DGX SuperPOD, HGX |
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| **InfiniBand (NDR)** | NVIDIA/Mellanox | 400 Gbps per port | GPU-NIC direct, RDMA | Distributed training, HPC |
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| **PCIe 5.0** | Standard | 63 GB/s per x16 | CPU-GPU | Inference, rendering |
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| **Ethernet (RoCE v2)** | Standard | 100/200/400 GbE | GPU-NIC, RDMA over converged ethernet | AI inference, storage |
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### GPU direct communication
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```
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GPU 0 ──NVLink── GPU 1 GPU 0 ───PCIe─── CPU ───PCIe─── GPU 1
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│ │
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│ │
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NVSwitch InfiniBand
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│ │
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│ │
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GPU 2 ──NVLink── GPU 3 GPU 2 ───PCIe─── CPU ───PCIe─── GPU 3
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NVLink topologie (GPU direct) PCIe topologie (CPU mediated)
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```
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- **GPU Direct RDMA** — GPU ↔ NIC bez CPU (InfiniBand, RoCE)
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- **GPU Direct Storage** — GPU ↔ NVMe bez CPU (NVIDIA Magnum IO)
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- **NVSwitch** — full bisection bandwidth mezi všemi GPU v node
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## Virtualizace GPU
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| Technologie | Popis | GPU support | Use case |
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|------------|-------|-------------|----------|
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| **NVIDIA vGPU (Grid)** | Časové slicing + dedikované profily | A-series (VDI), Q-series (pro viz), B-series (AI) | VDI, virtualizované AI |
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| **NVIDIA MIG** | Hardwarové partition GPU | A100 (7 inst.), H100/H200/B200 | AI inference, multi-tenant GPU |
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| **AMD MxGPU** | SR-IOV, hardwarové partition | AMD MI (pro), Radeon Pro | VDI, cloud gaming |
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| **Intel SG (SG1)** | SR-IOV, hardwarové partition | Intel SG1, Flex, Arc | VDI, media transcoding |
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| **GPU passthrough** | Dedikovaný GPU celé VM (VFIO-pci) | Všechny GPU | AI training, HPC, nejvyšší výkon |
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### MIG partition table (A100 / H100)
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| GPU | Partition profile | GPU Memory | Compute units |
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|-----|------------------|-----------|--------------|
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| **A100 80 GB** | 1g.5gb | 5 GB | 1 |
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| A100 80 GB | 2g.10gb | 10 GB | 2 |
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| A100 80 GB | 3g.20gb | 20 GB | 3 |
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| A100 80 GB | 7g.40gb | 40 GB | 7 |
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| A100 80 GB | Full (7× 1g) | 7 × 5 GB | 7 instances |
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| **H100 80 GB** | 1g.6gb+me | 6 GB | 1 |
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| H100 80 GB | 2g.12gb+me | 12 GB | 2 |
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| H100 80 GB | 3g.24gb+me | 24 GB | 3 |
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| H100 80 GB | 7g.80gb | 80 GB | 7 |
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## GPU use cases
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### AI Training
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- **Modely**: LLM (70B-405B+), vision, multimodal
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- **GPU**: H100, B200, GB200, MI300X
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- **Interconnect**: NVLink 5 / Infinity Fabric (v rámci node), InfiniBand NDR (mezi nody)
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- **Parallelism**: Data Parallel (DDP), Tensor Parallel (TP), Pipeline Parallel (PP), Fully Sharded (FSDP)
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- **Framework**: PyTorch (NCCL), JAX (XLA), DeepSpeed, Megatron-LM
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- **Tipy**:
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- GB200: 2× B200 propojené NVLink, 8 GPU → 4 GB200
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- DGX B200 / HGX B200: standardní building block
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- InfiniBand: fat tree topology pro all-reduce optimalizaci
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### AI Inference
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- **Modely**: LLM serving, embedding, image gen
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- **GPU**: A100, H200, B200 (larger VRAM pro větší modely)
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- **Techniky**: MIG partition, TensorRT-LLM, vLLM, Triton Inference Server
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- **Kvantizace**: FP8, INT8, INT4 → nižší VRAM, vyšší throughput
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- **Latency**: batch size optimalizace, dynamic batching, continuous batching
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- **Scale**: on-prem (2-32 GPU) / cloud (elastic)
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### VDI (Virtual Desktop Infrastructure)
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- **GPU**: NVIDIA A16 (1 GPU = 16 users), A10 (1 GPU = 4 users)
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- **Technologie**: vGPU (Grid), AMD MxGPU
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- **Protokoly**: VMware Blast, Citrix HDX, Microsoft RDP, PC-over-IP (HP Teradici)
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- **Use case**: CAD (CATIA, SolidWorks), Office, engineering, healthcare (PACS)
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### Rendering a VFX
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- **GPU**: NVIDIA RTX 6000 Ada, RTX A6000, AMD Radeon Pro W7900
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- **Rendering**: Blender (Cycles/OptiX), V-Ray, Octane Render, Redshift
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- **Denoising**: AI-accelerated denoising na GPU
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- **Farm rendering**: Deadline, Qube! (job scheduler)
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## GPU server form factors
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| Form factor | GPU count | Power | Cooling | Příklad |
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|------------|-----------|-------|---------|---------|
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| **1U** | 1-2 | 700-1400 W | Air (high-RPM) | Dell XR4510c |
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| **2U** | 4-8 | 3-6 kW | Air / Liquid | Dell R760xa, HPE DL380a |
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| **4U** | 8-10 | 5-8 kW | Liquid | NVIDIA DGX H100, Dell R760xa |
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| **8U / Chassis** | 8-16 | 10-20 kW | Liquid (CDU) | NVIDIA HGX, Supermicro SYS-821GE |
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## Zdroje
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Odkazy, knihy a standardy: [sources/infrastructure/sources.md](sources/infrastructure/sources.md)
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*Poslední revize: 2026-06-03*
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