# 🧠 AI/ML Infrastructure

## Component overview

```mermaid
flowchart TD
    subgraph Compute
        GPU["GPU (H100/B200/Instinct)"]
        CPU["CPU (AMD EPYC / Intel Xeon)"]
        ASIC["ASIC (TPU, Trainium, Inferentia)"]
    end
    subgraph Network
        IB["InfiniBand NDR/XDR"]
        ROCE["RoCEv2"]
        NVL["NVLink / NVSwitch"]
    end
    subgraph Storage
        FS["Parallel FS (Lustre, GPFS, Weka)"]
        OBJ["Object Store (S3, MinIO)"]
        NVME["Local NVMe cache"]
    end
    subgraph Orchestration
        S["Slurm"]
        K["Kubernetes + Volcano/Kueue"]
    end
    subgraph Cooling
        DLC["Direct-to-chip liquid"]
        IMM["Immersion"]
        AIR["Air (high-density)"]
    end

    Compute --> Network --> Storage
    Orchestration --> Compute
    Cooling --> Compute
```

---

## GPU compute

### NVIDIA

| GPU | Architecture | FP8 | FP16/BF16 | FP64 | HBM | NVLink | TDP | Rack config |
|-----|-------------|-----|-----------|------|-----|--------|-----|------|
| **H100 SXM** | Hopper | 3,958 TFLOPS | 1,979 TFLOPS | 67 TFLOPS | 80 GB HBM3 | 900 GB/s | 700 W | 6–8× in DGX H100 |
| **H200 SXM** | Hopper (HBM3e) | 3,958 TFLOPS | 1,979 TFLOPS | 67 TFLOPS | 141 GB HBM3e | 900 GB/s | 700 W | 6–8× in DGX H200 |
| **B200** | Blackwell | ~9,000 TFLOPS | ~4,500 TFLOPS | ~40 TFLOPS | 192 GB HBM3e | 1,800 GB/s | 1,000 W | 6–8× in DGX B200 |
| **GB200 Grace Hopper** | Blackwell | ~18,000 TFLOPS | ~9,000 TFLOPS | — | 192 GB + 480 GB (Grace) | NVLink-C2C | 1,000 W (GPU) + 500 W (CPU) | DGX GB200 (36× GPU) |
| **L40S** | Ada Lovelace | 733 TFLOPS | 367 TFLOPS | — | 48 GB GDDR6 | N/A | 350 W | Inference, enterprise |
| **A100 SXM** | Ampere | 1,248 TFLOPS | 624 TFLOPS | 19.5 TFLOPS | 80 GB HBM2e | 600 GB/s | 400 W | DGX A100 |

### AMD

| GPU | Architecture | FP8 | FP16/BF16 | FP64 | HBM | Infinity Fabric | TDP |
|-----|-------------|-----|-----------|------|-----|----------------|-----|
| **MI300X** | CDNA 3 | 2,615 TFLOPS | 1,307 TFLOPS | 81 TFLOPS | 192 GB HBM3 | 896 GB/s | 750 W |
| **MI250** | CDNA 2 | — | 383 TFLOPS | 95.7 TFLOPS | 128 GB HBM2e | 400 GB/s | 500 W |

### Intel

| GPU | Architecture | FP16/BF16 | FP32 | HBM | TDP |
|-----|-------------|-----------|------|-----|-----|
| **Gaudi 3** | Custom | 1,835 TFLOPS | — | 144 GB HBM2e | 600 W |
| **Max 1550** | Xe HPC | 600+ TFLOPS | 200 TFLOPS | 128 GB HBM2e | 600 W |

### Cloud ASIC

| ASIC | Provider | Use case | Performance |
|------|----------|----------|-------|
| **TPU v5p** | Google | Training | ~4,600 TFLOPS (BF16) per pod |
| **Trainium 2** | AWS | Training | ~1,000 TFLOPS (BF16) per chip |
| **Inferentia 2** | AWS | Inference | ~400 TOPS (INT8) per chip |
| **Maia 100** | Microsoft | Training + inference | Custom, 800 W TDP |

---

## AI networking

### Technology comparison

| Technology | Bandwidth per link | Latency | Topology | Use case |
|-------------|-------------------|---------|-----------|----------|
| **InfiniBand NDR200** | 200 Gb/s | < 1 µs | Fat-tree, Dragonfly+ | Training (NVIDIA) |
| **InfiniBand NDR400** | 400 Gb/s | < 1 µs | Fat-tree, Dragonfly+ | Training (NVIDIA) |
| **InfiniBand XDR** | 800 Gb/s (planned) | < 1 µs | Dragonfly+ | Next-gen training |
| **RoCEv2** (CX-7/8) | 200–400 Gb/s | 1–2 µs | Fat-tree, Spine-leaf | Training (AMD, Intel, open) |
| **NVLink 4.0** | 900 GB/s per GPU | < 0.5 µs | NVSwitch full-mesh | Intra-node GPU comm |
| **NVLink 5.0** | 1,800 GB/s per GPU | < 0.5 µs | NVSwitch full-mesh | Intra-node (Blackwell) |
| **Ethernet (400 GbE)** | 400 Gb/s | 2–5 µs | Spine-leaf | Inference, data pipeline |

### AI fabric principles

- **Rail-optimized topology** — each GPU communicates on dedicated "rails" (same GPU indices across nodes connect to the same switch)
- **Fat-tree (Clos)** — standard for InfiniBand and RoCE, non-blocking bisection bandwidth
- **Dragonfly+** — reduces hop count while maintaining bandwidth (used in largest clusters)
- **GPU Direct RDMA** — direct GPU ↔ GPU communication without CPU involvement, supports InfiniBand and RoCE
- **SHARP (Scalable Hierarchical Aggregation and Reduction Protocol)** — in-network reduction for AllReduce (InfiniBand only)

### Bandwidth sizing

```text
Rule of thumb: InfiniBand bandwidth ≥ 50 % GPU HBM bandwidth for scalable training

Example: H100 has 3.35 TB/s HBM
  → Needs min. 1.6 TB/s bisection bandwidth per GPU
  → 8× H100 in DGX: 4× NDR400 IB per GPU = 4 × 50 GB/s = 200 GB/s
  → Reality: 8× 200 Gb/s (25 GB/s) per GPU in typical config = ~6 % HBM → bottleneck
```

---

## AI storage

### Requirements

| Dataset size | IO pattern | Recommended storage | Bandwidth |
|-------------|-----------|-------------------|-----------|
| < 10 TB | Sequential read (data loading) | Local NVMe | > 10 GB/s per node |
| 10–100 TB | Random read (checkpointing) | Parallel FS (Lustre, Weka) | > 100 GB/s cluster-wide |
| 100 TB–10 PB | Mixed (training + checkpoint) | Parallel FS + object store | > 500 GB/s |
| 10 PB+ | Multi-modal, video, LLM | Tiered (NVMe cache + parallel FS + object) | > 1 TB/s |

### Storage solution comparison

| Solution | Type | Bandwidth per node | Max capacity | Scaling | Use case |
|--------|-----|-------------------|-------------|-----------|----------|
| **Lustre** | Parallel FS (POSIX) | > 100 GB/s (cluster) | 100s PB | OST + MDS | HPC, LLM training (standard) |
| **GPFS / StorageScale** | Parallel FS (POSIX) | > 100 GB/s | 100s PB | NSD servers | HPC, AI (IBM) |
| **WekaFS** | Parallel FS (POSIX + NFS/SMB) | ~80 GB/s per 10 nodes | 10s PB | Container-native | AI/ML, NVIDIA DGX preferred |
| **VAST Data** | Universal storage (NVMe + QLC) | ~100 GB/s per cluster | 10s PB | Scale-out | AI, checkpoint, data lake |
| **Pure Storage//E** | All-flash (NVMe) | ~50 GB/s | ~30 PB | Scale-out | Enterprise AI, database |
| **MinIO / S3** | Object store | ~20 GB/s per gateway | EB | Erasure coding | Dataset repository, checkpoint |
| **NetApp AFF** | NAS + S3 | ~10 GB/s per controller | ~50 PB | HA pair | Enterprise, NFS baseline |

### Checkpointing strategies

| Strategy | RPO | Storage impact | Description |
|-----------|-----|---------------|-------|
| **Full checkpoint** | every N steps | High (stops training) | Full model + optimizer state |
| **Async checkpoint** | every N steps | Medium (non-blocking) | Copy to staging buffer, async write |
| **Distributed checkpoint** (NVIDIA NeMo) | every N steps | Low | Each rank writes its own shard |
| **In-memory checkpoint** (IBM) | on failover | Minimal (DRAM) | Replication to another node's DRAM |
| **Continuous checkpoint** (Microsoft) | every 1–5 min | Low (delta) | Changed shards only |

---

## AI cluster architecture

### Physical topology — DGX H100 example

```
┌──────── DGX H100 (8× GPU) ────────┐
│  ┌─────┐ ┌─────┐ ┌─────┐ ┌─────┐ │
│  │GPU 0│ │GPU 1│ │GPU 2│ │GPU 3│ │
│  └──┬──┘ └──┬──┘ └──┬──┘ └──┬──┘ │
│  ┌──┴──┐ ┌──┴──┐ ┌──┴──┐ ┌──┴──┐ │
│  │GPU 4│ │GPU 5│ │GPU 6│ │GPU 7│ │
│  └─────┘ └─────┘ └─────┘ └─────┘ │
│  NVSwitch (NVLink 4.0, 900 GB/s)  │
│  InfiniBand CX-7: 8× NDR400       │
└────────────────────────────────────┘
         │ 8× IB rails
    ┌────┴──────────────┐
    │  IB NDR400 Switches │  (rail-optimized)
    └────────────────────┘
```

### Kubernetes for AI

| Component | Role |
|-----------|------|
| **Volcano** | Batch scheduling, gang scheduling, queue management |
| **Kueue** | Multi-tenant admission, resource quotas, fair sharing |
| **NVIDIA GPU Operator** | Driver, container toolkit, MIG, DCGM, monitoring |
| **HAMi** (ex k8s-vGPU-scheduler) | GPU sharing, MIG partitioning, fractional GPU |
| **Node Feature Discovery** | GPU type detection, NUMA topology |
| **Topology Manager** | NUMA-aware pod placement |
| **DPDK / SR-IOV** | High-performance networking for GPU Direct RDMA |

### Slurm for AI

| Component | Role |
|-----------|------|
| **slurm.conf** | Partition for GPU nodes, GRES (Generic Resource) |
| **gres.conf** | GPU type, GPU count per node |
| **srun --gres=gpu:8** | Allocate 8 GPUs per job |
| **sbatch --nodes=64 --ntasks=512** | 64 nodes, 512 ranks (8 GPU/node) |
| **Pixis** | NVIDIA orchestration plugin for Slurm |

---

## AI cluster cooling

### Power density comparison

| Configuration | TDP per node | Racks | kW/rack | Note |
|-------------|-------------|-------|---------|----------|
| Standard server (2U) | 1 kW | 20 | 5–10 | Typical DC |
| GPU server (DGX H100, 6×) | 42 kW | 6 | 45–50 | Air cooling limit |
| GPU server (DGX B200, 6×) | 72 kW | 6 | 90–100 | Liquid cooling required |
| GPU server (GB200 NVL72) | 120 kW | — | ~120 | Liquid cooling mandatory |
| NVIDIA NVL72 rack | 120 kW | 1 | 120 | Fully liquid cooled |

### Cooling technologies

| Method | Max kW/rack | CAPEX | OPEX | Complexity |
|--------|-------------|-------|------|-----------|
| **Air cooling (CRAC/CRAH)** | < 15 | Low | Medium | Low |
| **Air cooling (in-row)** | 15–30 | Medium | Medium | Low |
| **Rear-door heat exchanger** | 30–50 | Medium | Low | Medium |
| **Direct-to-chip liquid (cold plate)** | 50–150 | High | Low | High |
| **Immersion (single-phase)** | 100–200 | High | Low | High |
| **Immersion (two-phase)** | 200+ | Very high | Low | Very high |

---

## Inference infrastructure

### Inference server comparison

| Tool | Frameworks | Optimization | Use case |
|---------|-----------|-------------|----------|
| **vLLM** | Megatron, HF, AWQ, GPTQ | PagedAttention, KV cache, continuous batching | LLM inference (open source) |
| **TensorRT-LLM** | TensorRT | INT4/INT8/FP8, inflight batching, attention optimizations | Production (NVIDIA) |
| **Triton Inference Server** | All (TensorRT, vLLM, PyTorch) | Model ensemble, model caching, concurrent execution | Enterprise, multi-model |
| **SageMaker** | Managed | Auto-scaling, model parallelism | AWS managed |
| **OpenAI API / TGI** | HF Transformers | Continuous batching, flash attention | Hosting |

### Inference optimization

| Technique | Latency improvement | Throughput improvement | Memory reduction |
|----------|-----------------|---------------------|------------------|
| **FP8/INT8 quantization** | — | 2× | 2× |
| **INT4 quantization** | — | 4× | 4× |
| **Flash Attention 2/3** | 2–4× | — | 50 % (KV cache) |
| **PagedAttention** | — | 2–5× | 95 % (KV cache fragmentation) |
| **Continuous batching** | — | 10–20× | — |
| **Speculative decoding** | 2–3× | — | — |
| **Multi-LoRA / S-LoRA** | — | 8–16× | — |

---

## Distributed training techniques

| Technique | Description | Frameworks |
|----------|-------|------------|
| **Data Parallelism (DDP/FSDP)** | Each GPU has model copy, different batch | PyTorch DDP, FSDP |
| **Tensor Parallelism (TP)** | Model split across layers (intra-node) | Megatron-LM, DeepSpeed |
| **Pipeline Parallelism (PP)** | Layers split across nodes | Megatron-LM, DeepSpeed |
| **Sequence Parallelism (SP)** | Sequence split across GPUs | Megatron-LM |
| **Expert Parallelism (EP)** | Different expert subnets on different GPUs | Mixture-of-Experts (MoE) |
| **3D Parallelism** | TP + PP + DP combination | Megatron-LM, NeMo |
| **ZeRO (1/2/3)** | Optimizer/gradient/parameter sharding | DeepSpeed |
| **NCCL / RCCL** | GPU collective communication library | NVIDIA/AMD |

---

## Operating systems for AI

### Distribution comparison

| OS | GPU driver | CUDA | Container toolkit | IB/RoCE | Lustre client | Production support |
|----|-----------|------|-------------------|---------|--------------|-------------------|
| **Ubuntu 22.04 LTS** | NVIDIA 525+ | 12.x | nvidia-container-toolkit | MLNX_OFED, rdma-core | Yes (lustre-client) | NVIDIA DGX standard |
| **Ubuntu 24.04 LTS** | NVIDIA 550+ | 12.5+ | nvidia-container-toolkit | MLNX_OFED, rdma-core | Yes | Latest GPU support |
| **RHEL 9 / Rocky 9** | NVIDIA 525+ | 12.x | nvidia-container-toolkit | MLNX_OFED | Yes (EL repo) | Red Hat, enterprise |
| **DGX OS** (Ubuntu-based) | NVIDIA custom | 12.x | Pre-installed | Pre-configured | Yes | NVIDIA DGX only supported |
| **SLES 15 SP5** | NVIDIA 525+ | 12.x | nvidia-container-toolkit | MLNX_OFED | Yes | HPC, some Lustre clusters |
| **Debian 12** | NVIDIA 525+ | 12.x | nvidia-container-toolkit | rdma-core | Yes (backports) | Community, research |
| **Flatcar / Bottlerocket** | Container-host | — | nvidia-container-toolkit | Limited | No | K8s-only, minimal footprint |

### Limitations and constraints

#### GPU drivers and CUDA

| Constraint | Detail |
|----------|--------|
| **Driver-CUDA compatibility** | NVIDIA driver major version must match CUDA toolkit (driver ≥ CUDA req). E.g., CUDA 12.5 requires driver ≥ 550 |
| **Kernel version** | NVIDIA driver not compatible with all kernels. New kernel (6.8+) may require DKMS build or delayed support |
| **Secure Boot** | NVIDIA driver requires signed module (MOK, shim) or disabled Secure Boot — common enterprise issue |
| **Open vs Proprietary driver** | NVIDIA `nvidia-open` (since R515) — open source kernel module. GPU support: DC (H100+) → OK, older GPUs → proprietary required |
| **nvidia-persistenced** | Required to maintain GPU initialization; without it GPUs may sleep after idle timeout (`nvidia-smi -pm 1`) |
| **GPU reset** | After crashed training job, GPU may hang. `nvidia-smi --gpu-reset` or reboot node, sometimes power cycle |
| **Multi-instance GPU (MIG)** | Requires specific driver, MIG mode on GPU, GPU restart. Cannot be changed at runtime. A100, H100, B200 only |

#### Network (InfiniBand / RoCE)

| Constraint | Detail |
|----------|--------|
| **MLNX_OFED vs rdma-core** | MLNX_OFED (NVIDIA) — full support, but own kernel modules, kernel version compatibility needed. `rdma-core` (open) — limited support, no custom modules |
| **Kernel compatibility** | MLNX_OFED supports only specific kernel versions (major.minor). Kernel upgrade → MLNX_OFED rebuild required |
| **NCCL** | NCCL version must be compatible with CUDA and IB firmware. `nccl-tests` for validation |
| **SHARP** | In-network reduction requires specific MLNX_OFED + IB switch firmware combination |
| **GPU Direct RDMA** | Requires `nvidia-peermem` module + MLNX_OFED. Does not work with all GPU and IB card combinations |
| **RoCE PFC/ECN** | RoCE requires lossless fabric (PFC, ECN, DCQCN). Switch and host configuration — complex tuning |

#### Storage

| Constraint | Detail |
|----------|--------|
| **Lustre client** | Client version must match server. Server upgrade → upgrade all clients. Compatible with RHEL/Debian derivatives only |
| **POSIX locking** | NFS and Lustre have different POSIX locking behavior. Distributed training relies on flock → problematic with mixed FS |
| **Filesystem cache** | Page cache can mask IO bottlenecks. Training jobs often require `O_DIRECT` or sync IO |
| **Local NVMe vs parallel FS** | Dataset staging on local NVMe eliminates network dependency but requires space and pre-fetch pipeline |

#### Container runtime

| Constraint | Detail |
|----------|--------|
| **Docker + GPU** | `nvidia-container-toolkit` (formerly nvidia-docker2). Requires runtime installation and config in `/etc/docker/daemon.json` |
| **Podman + GPU** | Requires `nvidia-container-toolkit` + podman hook. Less tested than Docker |
| **containerd + GPU** | Standard for K8s. Requires `cdi` (Container Device Interface) or `nvidia-container-runtime` |
| **Enroot + Pyxis** | NVIDIA container stack for Slurm (Enroot = daemonless container runtime, Pyxis = Slurm plugin) |
| **User namespace mapping** | Container GPU access requires device cgroup; rootless may fail (exception for /dev/dri and /dev/nvidia*) |

#### Kernel parameters

```text
# AI workload recommended sysctl
net.core.rmem_max = 134217728       # sufficient for NCCL
net.core.wmem_max = 134217728
net.ipv4.tcp_rmem = 4096 87380 134217728
net.ipv4.tcp_wmem = 4096 65536 134217728
net.core.netdev_budget = 600        # for high packet rate
vm.max_map_count = 1048576          # PyTorch DataLoader workers
kernel.numa_balancing = 0           # disable NUMA balancing (breaks locality)
kernel.sched_min_granularity_ns = 10000000

# Disable security mitigations for perf (dedicated AI clusters only)
mitigations=off
transparent_hugepages=never         # or madvise — THP may cause latency spikes
intel_idle.max_cstate=1             # reduce C-state transition latency
```

#### Firmware and HW

| Constraint | Detail |
|----------|--------|
| **GPU firmware (VBIOS)** | NVIDIA datacenter GPUs (H100, B200) have VBIOS updates via NVFlash. Without update → missing partitioning support or newer CUDA features |
| **InfiniBand firmware** | IB switch and HCA firmware must be compatible. Mix old switch + new HCA → degraded perf |
| **NVSwitch firmware** | DGX systems have NVSwitch firmware updatable only via NVIDIA DGX tools |
| **Power capping (nvidia-smi)** | `nvidia-smi -pl <power>` — limit TDP for power budget management. Test impact on training throughput |
| **GPU clock locking** | `nvidia-smi -ac <clock,mem>` — locked clock frequency for stable benchmarks. Apply after `nvidia-persistenced` |
| **PCIe Gen** | GPU in PCIe Gen4 slot (instead of Gen5) → bottleneck for CPU↔GPU data transfer. Important for FSDP sharding |

### Recommended OS per use case

| Use case | OS | Rationale |
|----------|-----|-------|
| **DGX cluster (production)** | DGX OS / Ubuntu 22.04 LTS | NVIDIA standard, best driver support |
| **Enterprise K8s (OpenShift)** | RHEL 9 / RHCOS | Red Hat support, GPU Operator compatible |
| **Vanilla K8s (on-prem)** | Ubuntu 22.04 LTS + Flatcar (workers) | Widest community support, Flatcar for minimal footprint |
| **Slurm cluster (HPC/AI)** | Rocky Linux 9 / Ubuntu 22.04 LTS | EL ecosystem (Lustre, OFED) or Ubuntu (community) |
| **Research / rapid prototyping** | Ubuntu 24.04 LTS | Latest CUDA, PyTorch, driver support |
| **Edge inference** | NVIDIA JetPack / Ubuntu (ARM) | Embedded GPU (Jetson Orin, AGX) |

---

## AI-ready data center — check-list

| Area | Requirement |
|--------|-----------|
| **Power** | 30–120 kW/rack, HVDC (400 V DC), UPS supporting GPU spikes |
| **Cooling** | Liquid cooling ready (direct-to-chip), rear-door for 30+ kW |
| **Network** | InfiniBand (NDR/XDR) or RoCEv2, rail-optimized fat-tree |
| **Storage** | Parallel FS (Lustre/Weka), checkpoint bandwidth > 100 GB/s |
| **GPU density** | Max GPU/rack, minimize NVSwitch hops |
| **Physical** | Floor load 1,500+ kg/m², rack 52U–60U |
| **Security** | Tenant isolation, network segmentation, data encryption |
| **Monitoring** | DCGM, NCCL health checks, thermals, power capping |

---

## Model and throughput limitations

### Model size per GPU

Maximum model size fitting on a single GPU depends on HBM capacity and precision:

| GPU | HBM | FP32 | FP16/BF16 | INT8 | INT4 |
|-----|-----|------|-----------|------|------|
| **H100 80GB** | 80 GB | ~10B | ~40B | ~80B | ~160B |
| **H200 141GB** | 141 GB | ~18B | ~70B | ~140B | ~280B |
| **B200 192GB** | 192 GB | ~24B | ~96B | ~192B | ~384B |
| **MI300X 192GB** | 192 GB | ~24B | ~96B | ~192B | ~384B |
| **A100 80GB** | 80 GB | ~10B | ~40B | ~80B | ~160B |
| **GB200 (192+480)** | 192 GB GPU + 480 GB Grace | — | ~96B + CPU offload | — | — |

*Approximate: 1B params ≈ 2 GB FP16 ≈ 4 GB FP32 ≈ 1 GB INT8 ≈ 0.5 GB INT4. Subtract ~10–15 % HBM for activations, KV cache, optimizer states.*

### Memory breakdown inference

| Component | Llama 3 70B (FP16) | Llama 3 8B (FP16) |
|------------|-------------------|-------------------|
| Model weights | 140 GB | 16 GB |
| KV cache (4K context, batch 1) | ~2 GB | ~0.2 GB |
| KV cache (128K context, batch 1) | ~60 GB | ~6.5 GB |
| Activations (peak) | ~5 GB | ~1 GB |
| **Total 4K ctx** | ~147 GB | ~17 GB |
| **Total 128K ctx** | ~205 GB | ~23 GB |

**Conclusion:** Llama 3 70B FP16 does not fit on a single H100 (80 GB). Required: INT8 (170 GB → 2× H100), INT4 (85 GB → 1× H200), or tensor parallelism.

### Context length vs memory

| Context | KV cache 70B (FP16) | KV cache 8B (FP16) | Note |
|---------|-------------------|-------------------|------|
| 4K | ~2.2 GB | ~0.25 GB | Typical chat |
| 32K | ~18 GB | ~2 GB | Documents |
| 128K | ~72 GB | ~8 GB | Long-context (Claude, Gemini) |
| 1M | ~560 GB | ~64 GB | Experimental (Gemini 1.5 Pro) |

KV cache is **linear with context length** and quadratic with attention head count. Critical for long-context inference.

### Throughput inference

| Model | GPU | Precision | Batch size | Tokens/s | QPS (1K output) |
|-------|-----|-----------|-----------|----------|-----------------|
| Llama 3 8B | H100 | FP16 | 1 | ~800 | ~0.8 |
| Llama 3 8B | H100 | FP16 | 128 | ~4 500 | ~35 |
| Llama 3 8B | H100 | INT4 | 128 | ~8 000 | ~62 |
| Llama 3 70B | 4× H100 | FP16 | 1 | ~180 | ~0.18 |
| Llama 3 70B | 4× H100 | INT4 | 64 | ~1 200 | ~19 |
| Llama 3 70B | 8× H100 | FP16 (TP=8) | 128 | ~2 500 | ~20 |
| DeepSeek-R1 671B | 8× H200 | FP8 (MoE) | 64 | ~500 | ~8 |
| GPT-4 class (est.) | — | — | — | ~100–300 | ~1–3 |

**Notes:**
- QPS (queries per second) depends on output length (1K tokens ≈ ~1 query)
- Larger batch increases throughput but increases TTFB (time to first token)
- Tensor Parallelism (TP) scales, but communication overhead grows linearly

### Training limits

#### Scaling efficiency

| GPU count | Model | Efficiency | Reason |
|-----------|-------|-----------|-------|
| 8 (1 node) | Llama 3 8B | ~95 % | NVLink intra-node |
| 64 (8 nodes) | Llama 3 8B | ~85 % | IB inter-node |
| 512 (64 nodes) | Llama 3 70B | ~75 % | Communication overhead |
| 4 096 (512 nodes) | Llama 3 70B | ~60 % | Pipeline bubble, network |
| 16 384 (2 048 nodes) | Llama 3 405B | ~45 % | Synchronous SGD overhead |

**Note:** Efficiency = (actual throughput) / (ideal linear speedup). Decreases logarithmically with GPU count.

#### Memory breakdown training

| Component | Llama 3 70B (BF16) | Llama 3 8B (BF16) |
|------------|-------------------|-------------------|
| Model weights | 140 GB | 16 GB |
| Optimizer states (Adam) | 280 GB | 32 GB |
| Gradients | 140 GB | 16 GB |
| Activations (peak) | ~30 GB | ~4 GB |
| **Total (DDP)** | ~590 GB | ~68 GB |
| **Total (FSDP shard=8)** | ~74 GB | ~8.5 GB |

**Conclusion:** FSDP (Fully Sharded Data Parallelism) is required for training models > 10B. Adam optimizer doubles memory vs inference (weights + optimizer + gradients).

#### Time to train

| Model | GPU count | GPU type | Precision | Time | Cost (on-prem estimate) |
|-------|-----------|---------|-----------|------|---------------------|
| Llama 3 8B | 64 | H100 | BF16 | ~3 days | ~$5 000 |
| Llama 3 70B | 512 | H100 | BF16 | ~14 days | ~$100 000 |
| Llama 3 405B | 16 384 | H100 | BF16 | ~60 days | ~$14 M |
| DeepSeek-R1 671B (MoE) | 2 048 | H800 | BF16 | ~30 days | ~$6 M |
| GPT-4 (est.) | 25 000 | A100/H100 | Mixed | ~90–100 days | ~$100 M |

### Power and thermal limits

| Configuration | TDP limit | Throughput loss | Reason |
|-------------|-----------|------------------|--------|
| H100 SXM | 700 W (default) | 0 % | Nominal |
| H100 SXM | 600 W (-15 %) | ~5–8 % | Power capping |
| H100 SXM | 500 W (-30 %) | ~15–25 % | Significant throttling |
| H100 SXM | 400 W (-43 %) | ~30–50 % | Emergency only |
| DGX H100 (8×) | 5.6 kW (max) | 0 % | Liquid cooling required |
| DGX H100 (8×) | 4.5 kW (air) | ~10–15 % | Rear-door heat exchanger |

GPU throttles when exceeding TDP or temperature (85°C+). Power capping correlates linearly with frequency but non-linearly with throughput.

### API and operational limits

| Limit | Description | Typical value |
|-------|-------|-----------------|
| **Rate limit** | Max requests per minute/hour | 100–10 000 RPM (per tier) |
| **Tokens per minute (TPM)** | Max tokens per minute | 1M–300M (per model) |
| **Context window** | Max input tokens | 4K–2M (per model) |
| **Max output tokens** | Max generated tokens | 4K–32K (per model) |
| **Concurrent requests** | Parallel request count | 10–10 000 (per backend) |
| **Batch window** | Time to accumulate batch | 0–20 s (vLLM, TGI) |
| **TTFB timeout** | Max latency to first token | 30–120 s |
| **Idle timeout** | GPU idle → scale to 0 | 5–15 min (cloud) |

### Limits per deployment model

| Dimension | On-prem HW | Managed cloud (SageMaker, Vertex) | API (OpenAI, Anthropic) |
|-----------|--------------|----------------------------------|------------------------|
| **Model size** | Limited by HBM (max 192 GB/GPU) | Unlimited (cluster scaling) | Unlimited |
| **Queries** | Limited by GPU count | Auto-scaling | Rate limit (per tier) |
| **Latency** | < 10 ms (same node) | 10–100 ms (network hop) | 100 ms – 10 s |
| **Customization** | Full (fine-tuning, quantization) | Managed (SageMaker, Bedrock) | Prompt engineering only |
| **Data privacy** | Yes (on-prem) | Contractual (region, encryption) | Limited |
| **Cost per 1M tokens** | ~$0.10–0.50 (FP16 inference) | ~$0.20–1.00 | ~$0.15–15.00 |
| **Max context** | 128K+ (depending on GPU count) | 128K+ | 32K–2M |
| **Cold start** | 0 (always-on) | 30 s – 5 min | 0 (shared infra) |

---

## GPU pricing and price/performance (2026)

> Prices are approximate — NVIDIA does not publish official datacenter GPU price lists. Cloud prices from public providers (Q2 2026). HW purchase prices vary by volume, reseller, and region.

### Purchase price (buy)

| GPU | Price/GPU | Price 8× GPU baseboard | $/PFLOPS (FP16) | Note |
|-----|---------|----------------------|----------------|------|
| **H100 SXM** | $27,000–40,000 | ~$200,000 | $25,000 | Scarcity 2023–2024, now stabilized |
| **H200 SXM** | $35,000–50,000 | ~$280,000 | ~$35,000 | H100 upgrade, HBM3e |
| **B200** | ~$60,000–70,000 | ~$500,000+ | ~$31,000 | Blackwell, FP4 support |
| **B100** | ~$30,000 | ~$240,000 | ~$20,000 | Lower price than B200, similar FP8 perf |
| **GB200** (Grace+Blackwell) | ~$70,000–100,000 | ~$2,000,000 (rack) | — | CPU+GPU unified, high-density |
| **A100 80GB** | ~$10,000–15,000 | ~$120,000 | ~$19,200 | Previous gen, still relevant |
| **MI300X** | ~$12,000–18,000 | ~$100,000 | ~$9,600 | AMD, 192 GB HBM3 |
| **Gaudi 3** | ~$15,625 | ~$125,000 | **$8,515** | Intel, best $/PFLOPS |
| **L40S** | ~$8,000–10,000 | — | — | Inference, enterprise |

### Cloud pricing (on-demand $/GPU/hr)

| GPU | Cheapest | Mid-range (CoreWeave, Lambda) | Hyperscaler (AWS, GCP, Azure) |
|-----|----------|-----------------------------|-------------------------------|
| **H100 SXM** | $1.38 (Thunder) | $2.89–3.29 | $4.15–6.88 |
| **H100 PCIe** | $2.01 (Spheron) | $2.50 | — |
| **H200 SXM** | $3.89 (Spheron) | $4.54 | $5.00+ |
| **B200** | **$3.39** (Spheron) | $6.02 | $14.24 (AWS) |
| **B200 spot** | **$2.12** (Spheron) | — | — |
| **GB200** | $3.50 (Runcrate) | $5.85 (Oracle) | $6.95 (GCP) |
| **MI300X** | **$1.50** (TensorWave) | $1.85 (Vultr) | $7.86 (Azure) |
| **A100 80GB** | $1.07 (Spheron) | $1.50–2.00 | $3.00+ |
| **Gaudi 3** | ~$1.50–2.50 | — | — |
| **L40S** | $0.91 (Spheron) | $1.50–2.00 | — |

### Inference cost ($/M tokens)

| GPU | Provider | $/hr | Est. tok/s | $/M tok |
|-----|----------|------|-----------|--------|
| **B200** | Spheron | $3.39 | ~4,000 | **$0.42** |
| **B200 spot** | Spheron | $2.12 | ~4,000 | **$0.15** |
| **H100 PCIe** | Spheron | $2.01 | ~1,200 | $0.47 |
| **A100 80GB** | Spheron | $1.07 | ~520 | $0.57 |
| **H100 SXM** | AWS | $6.88 | ~1,200 | $1.59 |
| **H200 SXM** | Spheron | $4.54 | ~1,800 | $0.70 |
| **L40S** | Spheron | $0.91 | ~450 | $0.56 |

*Values for Llama 3 70B (INT8, batch=1, output 1K tok). Actual values vary by batch size, context, and quantization.*

### Cost per GB HBM

| GPU | HBM | Price/hr cloud | $/GB/hr | Best for memory-bound workloads |
|-----|-----|-------------|--------|--------------------------------|
| **MI300X** | 192 GB | $1.50 | **$0.0078** | ✅ Best |
| **B200** | 192 GB | $3.39 | $0.0177 | ✅ Good |
| **H200** | 141 GB | $3.89 | $0.0276 | ⚠️ |
| **H100 SXM** | 80 GB | $1.38 | $0.0173 | ⚠️ Only up to 70B models |
| **GB200** | 384 GB | $3.50 | $0.0091 | ✅✅ (2× MI300X capacity) |

### Price/performance by scenario

| Scenario | Winner | Rationale |
|----------|--------|-----------|
| **Absolute performance** (cost no object) | **GB200 DGX NVL72** | 72× GPU, 18 PFLOPS FP8, 384 GB HBM/GPU |
| **Cloud inference** — best $/token | **B200 spot** | $0.15/M tok; 4× H100 throughput at lower cost |
| **Cloud inference** — on-demand | **B200** | $0.42/M tok |
| **Cloud inference** — budget | **A100 / L40S** | $0.57–0.56/M tok |
| **Training** — price/perf on purchase | **Gaudi 3** | $8,515/PFLOPS, 2.5–3× better than H100 |
| **Training** — cloud | **H100 SXM** | $1.38/hr, CUDA ecosystem, NCCL |
| **Memory-bound** — long context, 70B+ | **MI300X / GB200** | 192–384 GB, $0.0078–0.0091/GB |
| **Ecosystem + safe choice** | **H100/H200** | CUDA, widest SW, NVIDIA tools |
| **Spot / preemptible** — lowest cost | **A100 / H100** | $1.07–1.38/hr, 50–90% off on-demand |

### 2026 Trends

- **H100** — price dropped 64% from peak $8/hr to $1.38–2.89/hr, then 40% rebound from inference demand
- **B200** — new high-end, $3.39/hr cloud → ~$0.15/M tok on spot — new inference benchmark
- **MI300X** — supply growing (TensorWave, Vultr, CoreWeave, Oracle, Azure), from $1.50/hr
- **Gaudi 3** — best $/PFLOPS on purchase, but narrow ecosystem and limited cloud availability
- **Market bifurcation** — prior gen (H100, A100) commoditizing, new gen (B200, GB200) commanding premium

- [GPU.en.md](GPU.en.md) — GPU architecture, NVIDIA/AMD, vGPU, MIG
- [NETWORKING.en.md](NETWORKING.en.md) — InfiniBand, RoCE, network topology
- [STORAGE.en.md](STORAGE.en.md) — parallel filesystem, object store
- [DATACENTERS.en.md](DATACENTERS.en.md) — DC layout, power, cooling
- [CLOUD.en.md](CLOUD.en.md) — cloud AI services (SageMaker, Vertex AI)

## Sources

Links, books, and standards: [sources/infrastructure/sources.en.md](sources/infrastructure/sources.en.md)

*Last revision: 2026-06-18*