Building the AI Infra Foundation: L0 Resource Layer for GPU Scheduling and Cloud‑Native Architecture

1. Real‑world problem that triggers the discussion

A company bought eight H100 GPUs for a few million dollars, but after launching inference services the GPU utilization stayed at only 30% and the monthly bill hit $120k. The CTO demanded a raise to 70% within two weeks or the project would be cancelled. Investigation revealed that training and inference jobs competed for the same GPUs, scheduling was done manually with kubectl, idle GPUs burned money, model weights were scattered across node disks causing version drift, and ops staff were constantly woken up by alerts.

Industry research for 2026 shows average GPU cluster utilization of 30%–50% and on‑demand prices of $2–$4 per hour for H100 and $6+ per hour for B200. The root cause is not the hardware but the three foundational concerns of the L0 layer: scheduling, storage tiering, and low‑latency networking.

2. What the L0 layer aims to solve

L0 is the foundation of the entire AI Infra stack. It addresses four core problems:

Compute supply : GPUs are scarce and expensive. The 2026 NVIDIA B200 Blackwell offers 192 GB HBM3e, 8 TB/s bandwidth and 9,000 TFLOPS FP4, costing $6/hr on‑demand. Extracting the full compute from each card is the core value of L0.

Scheduling efficiency : Training jobs require gang scheduling, inference jobs need latency‑SLA pools. The default Kubernetes scheduler is unsuitable for AI workloads. Kubernetes Dynamic Resource Allocation (DRA) is GA in 2026, but additional batch schedulers such as Volcano and Kueue are needed for fairness and preemption.

Storage tiering : Model weights can be tens of GB and training data starts at TB scale. A three‑tier system (NVMe local cache → distributed FS like JuiceFS → object storage) balances cost and performance.

Low‑latency networking : Gradient synchronization generates massive traffic. Standard Ethernet becomes a bottleneck; RDMA/InfiniBand and NVLink 5.0 (1.8 TB/s) reduce inter‑GPU latency to <1 ms.

3. Core architecture diagram

The L0 stack is organized into five layers, each solving a dimension of resource provisioning:

Compute layer (GPU/CPU) : Includes NVIDIA H100, H200, B200, GB200 super‑chip, and domestic alternatives such as Ascend 910C and Moore Threads S5000. B200 delivers roughly double the training throughput of H100 and 2.4× memory bandwidth.

Orchestration layer (K8s/Ray/Slurm) : Kubernetes + Volcano is the preferred stack for inference; DRA is GA; Ray 2.55 under KubeRay raises GPU utilization by 50%–70%; Slurm remains the strongest multi‑tenant scheduler for pure HPC clusters.

Storage layer (object/block/file) : Object storage (S3/OSS) for cold data, JuiceFS as a POSIX‑compatible distributed FS (chosen by NAVER over Alluxio, reducing storage cost by 90% in BioMap), and NVMe local cache for hot data.

Network layer (RDMA/IB/VPC) : NVLink 5.0 inside nodes (1.8 TB/s), InfiniBand HDR (200 Gb/s) or RoCE v2 between nodes, NVIDIA Spectrum‑X Ethernet approximates IB performance at lower cost, NCCL automatically selects the optimal path.

Security layer (keys/isolation) : MIG physical isolation, network policies, and secret management (Vault/KMS) ensure multi‑tenant safety.

4. Key technology breakdown

The L0 stack consists of four major modules:

4.1 GPU virtualization: MIG, time‑slicing, MPS

Three mainstream approaches:

MIG (Multi‑Instance GPU) : Hardware‑level partitioning into up to seven isolated instances per GPU. Example: on a GB200 you can create 2 × 93 GB, 4 × 46 GB or 7 × 23 GB instances. Managed via NVIDIA GPU Operator (v1.9.0+) with single or mixed strategies. Ideal for multi‑team sharing.

Time‑slicing : Software‑level sharing where pods rotate on the same GPU. Simple (built‑in Device Plugin) but provides no QoS guarantees.

MPS (Multi‑Process Service) : Multiple processes share the GPU while keeping separate address spaces. Better performance than time‑slicing but weaker isolation.

Recommendation: use MIG for production multi‑tenant workloads, time‑slicing for development, and MPS for performance‑critical but isolation‑tolerant scenarios.

4.2 Elastic scaling driven by queue depth and GPU utilization

Inference traffic shows clear peaks. Automatic scaling is essential.

def auto_scale_gpu(queue_depth, gpu_util):
    if queue_depth > 100 and gpu_util > 0.85:
        replicas = scale_up(min=2, max=16, step=2)
        notify("GPU 扩容 → %d 副本" % replicas)
    elif queue_depth < 10 and gpu_util < 0.3:
        scale_down(step=1, min=2)
    if idle_minutes > 5:  # 空跑检测
        scale_to(min_replicas)
    record_metric("queue_depth", queue_depth)
    record_metric("gpu_util", gpu_util)

KEDA consumes custom metrics (e.g., Prometheus queue depth) to trigger HPA. Cluster Autoscaler handles node‑level scaling; the scale-down-unneeded-time parameter should be set to 10 minutes for inference (30 minutes for training) to avoid thrashing. Studies show a well‑tuned elastic‑scaling policy can cut cloud spend by 20%–40%.

4.3 Multi‑tenant fair scheduling: Volcano & Kueue

FIFO scheduling lets large teams monopolize GPUs. Volcano provides gang scheduling; Kueue adds queue management, priority preemption, and quota enforcement. In 2026 the Volcano + Kueue + DRA combo is the de‑facto standard for AI workloads, raising cluster utilization from the default ~40% to ~75%.

4.4 Storage tiering & RDMA high‑speed interconnect

Three‑tier storage : Hot – NVMe SSD cache with <1 ms read latency; Warm – JuiceFS (POSIX + S3 backend) supporting ReadWriteMany; Cold – object storage for long‑term archival. NAVER and BioMap report 90% cost reduction with JuiceFS in large‑scale AI. RDMA networking : Bypasses the kernel to move data directly between GPU memories. InfiniBand HDR (200 Gb/s) or RoCE v2 with Spectrum‑X achieve near‑IB performance at lower cost. Correct NCCL environment variables ( NCCL_SOCKET_IFNAME , NCCL_IB_DISABLE ) are critical; misconfiguration can degrade performance by 10‑100×. Microsoft’s Azure cluster of 4,608 GPUs delivers 92.1 exaflops FP4, demonstrating the value of high‑speed interconnect.

5. Mainstream open‑source framework comparison (2026)

Kubernetes + Volcano – Container + batch scheduling; primary AI platform for inference; DRA GA, stable gang scheduling; 66% of enterprises run GenAI inference on K8s.

Ray Cluster – Distributed compute; unified training + inference; Ray 2.55 stable, KubeRay + DRA integration; GPU utilization +50%–70%.

Slurm – HPC scheduler; pure training clusters; strongest multi‑tenant fairness, mature checkpointing, runs outside the K8s ecosystem.

Kueue – Native K8s queue manager; enables multi‑team GPU sharing, priority preemption, quota management; works with DRA.

JuiceFS – Distributed FS for model weight sharing; POSIX + S3; CSI driver; proven by NAVER/BioMap to cut storage cost by 90%.

Alluxio – Data orchestration layer; suitable as an optional cache; less S3/FUSE compatibility than JuiceFS.

6. Enterprise production implementation (reference architecture for 8–32 GPUs)

6.1 Inference GPU elastic scaling (SLA pools + auto‑scaling)

Separate high‑priority (P99 < 3 s) and normal‑priority pools. High‑priority pool uses MIG‑isolated instances; normal pool uses time‑slicing. Scaling follows the dual‑metric rule (queue depth + GPU utilization) via KEDA and Cluster Autoscaler. Set scale-down-unneeded-time to 10 minutes; shrink to the minimum replica count when idle for >5 minutes.

6.2 Training queue: Volcano fair scheduling + preemption

Allocate GPU quota per team (e.g., A 40%, B 30%, C 30%). High‑priority jobs preempt lower‑priority ones; preempted jobs checkpoint before yielding resources. Ray Train and PyTorch FSDP support automatic checkpointing; checkpoints are stored on JuiceFS for distributed recovery.

6.3 Model weight versioning & P2P distribution

def deploy_model_weights(model_id, version, target_nodes):
    weights = registry.fetch(model_id, version, verify_hash=True)
    for node in target_nodes:
        distribute(weights, node, method="p2p")
    wait_until(health_check(target_nodes), timeout=120)
    notify("模型 %s v%s 已就绪" % (model_id, version))
    update_serving_routes(model_id, version, target_nodes)

Weights are stored in an Artifact Registry (e.g., self‑hosted HuggingFace Hub). Hash verification guarantees integrity. P2P protocols (Dragonfly, Kraken) pull in parallel, avoiding single‑point bandwidth limits. Pre‑loading to NVMe reduces model start‑up time to <60 s for a 140 GB 70B FP16 model on a B200.

6.4 Network & monitoring configuration

Use InfiniBand or RoCE v2 between nodes; set NCCL environment variables correctly. DCGM Exporter feeds GPU utilization, memory, temperature, and power metrics to Prometheus; Grafana visualizes them. Critical alerts: GPU utilization <30% for >10 min (idle), temperature >85 °C, NVLink bandwidth anomalies.

7. Metrics and acceptance criteria (2026 enterprise AI platform)

GPU utilization ≥ 70% (measured by Prometheus + DCGM Exporter)

GPU idle‑run rate < 5% (time GPU is occupied without serving requests)

Model weight distribution latency < 60 s (from registry to ready state)

Storage IOPS for inference > 10 K (fio benchmark)

RDMA network latency < 1 ms (GPU‑to‑GPU ping)

Resource reclamation time < 3 min (training finish → GPU release)

Overall cluster utilization ≥ 75% (post‑optimisation vs default 40%)

Elastic scaling response < 2 min (metric trigger → pod ready)

Achieving ≥ 70% GPU utilization effectively doubles compute ROI compared with the industry average of 30%–50%.

8. Common pitfalls and best practices

Pitfall 1 – GPU idle burn : Pods keep GPUs overnight with no traffic. Best practice : Auto‑scale down after 5 min idle using KEDA + Cluster Autoscaler; set scale-down-unneeded-time to 10 min for inference. Expected cost reduction 20%–40%.

Pitfall 2 – Storage I/O bottleneck : Model weights stored only in object storage cause cold‑start >5 min. Best practice : Pre‑load weights to NVMe, use JuiceFS for shared hot layer, accelerate with P2P distribution; keep start‑up <60 s.

Pitfall 3 – Network latency : Standard Ethernet leads to gradient sync delays, reducing 8‑GPU training to ~3× the speed of 2‑GPU. Best practice : Deploy InfiniBand or RoCE v2, set NCCL_SOCKET_IFNAME and NCCL_IB_DISABLE correctly; otherwise performance may drop 10–100×.

Pitfall 4 – Training queue starvation : FIFO lets large teams dominate. Best practice : Volcano fair scheduling with priority preemption and team‑weight quotas; checkpoint before preemption.

Pitfall 5 – Scattered model weights : Inconsistent versions cause unreliable inference. Best practice : Central Artifact Registry with versioning and hash verification; distribute via P2P; treat weights as code.

9. Relationship with upstream and downstream layers

L1 Model & inference layer : Engines (vLLM, TGI, SGLang) run on L0 GPUs; L0 scheduling directly impacts L1 latency and cost. Raising GPU utilization from 30% to 70% halves per‑inference cost.

L2 Data & knowledge layer : Vector databases rely on L0 storage performance; JuiceFS IOPS and latency affect retrieval P99.

L5 Agent & tool layer : Agent sandboxes need compute resources; L0 CPU/GPU scheduling provides the foundation.

L6 Memory & storage layer : Long‑term memory storage depends on L0 object storage and distributed FS.

The efficiency of L0 propagates upward, shaping the experience of every higher layer. The next article will explore L1 model & inference details.

10. One‑page summary

Problem solved : Compute scheduling, storage tiering, low‑latency networking.

Core technologies : MIG GPU virtualization, elastic scaling, Volcano fair scheduling, RDMA/NVLink interconnect, JuiceFS tiered storage.

Preferred frameworks : K8s + Volcano + Kueue (inference), Ray 2.55 (training), JuiceFS + S3 (storage).

2026 hardware : NVIDIA B200 192 GB HBM3e / 8 TB/s / 9,000 TFLOPS FP4, GB200 super‑node (72 × B200).

Key metrics : GPU utilization ≥ 70%, idle‑run < 5%, weight distribution < 60 s, cluster utilization ≥ 75%.

Biggest pitfalls : GPU idle burn, storage I/O bottleneck, network latency, scattered weights.

Takeaway : L0 is the foundation; higher utilization equals lower cost.