Best GPU Orchestration Tools: SLURM vs Ray vs Kubernetes

Contents

• GPU Orchestration Tools for ML Workloads
• FAQ
• Related Resources
• Sources

GPU Orchestration Tools for ML Workloads

Distributed GPU clusters need orchestration. SLURM for HPC. Ray for ML. Kubernetes for containers. Pick right = no bottlenecks, lower costs.

SLURM for High-Performance Computing

SLURM (Simple Linux Utility for Resource Management) schedules jobs across compute clusters. Originally developed for supercomputers, now widely used in academic HPC facilities.

Architecture: Central controller manages job queue. Compute nodes execute jobs. Database tracks job state, accounting, and metrics.

SLURM excels at:

• Static workloads with predictable resource needs
• Long-running batch jobs (multi-day training runs)
• Preemption handling when higher-priority jobs arrive
• Guaranteed resource allocation with no noisy neighbors

Job submission:

#!/bin/bash
#SBATCH --job-name=llm-training
#SBATCH --gpus=4
#SBATCH --mem=128G
#SBATCH --time=48:00:00
#SBATCH --partition=gpu

python train_llm.py \
    --batch-size 32 \
    --num-epochs 10 \
    --distributed True

Submit with: sbatch train_job.sh

SLURM handles GPU allocation, restarts on failure, and reports completion. Simple interface for single-node training.

Multi-node distributed training requires MPI (Message Passing Interface):

srun --gpus-per-node=4 python -m torch.distributed.launch train.py

SLURM launches training script on all allocated nodes. MPI handles inter-node communication.

Limitations: SLURM requires manual setup, tuning, and cluster management. Suitable for dedicated clusters operated by experienced teams. Not practical for small teams or heterogeneous hardware.

Pricing model: No software licensing cost. Only hardware and power. Cluster ownership requires capital expenditure. Small clusters (8 GPUs) cost $50k-100k upfront.

Ray for Distributed ML

Ray simplifies distributed ML with Python-native API. Handles job scheduling, fault tolerance, and resource management. Designed for modern ML workflows, not legacy HPC patterns.

Ray Cluster architecture: Head node coordinates scheduling. Worker nodes execute tasks. Ray handles all communication transparently.

Basic distributed training:

import ray
from ray import air, tune
from ray.air import session

ray.init(address="auto")

def train_fn(config):
    # Training code here
    for epoch in range(config["num_epochs"]):
        loss = train_one_epoch()
        session.report({"loss": loss})

tuner = tune.Tuner(
    train_fn,
    param_space={"num_epochs": 10, "lr": 0.001},
    run_config=air.RunConfig(
        stop={"loss": 0.01},
        checkpoint_config=air.CheckpointConfig(num_to_keep=3)
    )
)

results = tuner.fit()

Ray handles:

• Distributing training across GPUs
• Checkpointing and fault recovery
• Hyperparameter sweep orchestration
• Early stopping based on metrics

Ray excels at:

• Dynamic workloads (neural architecture search, hyperparameter tuning)
• Interactive development (Jupyter notebooks)
• Flexible resource requirements
• Multi-stage pipelines (data preparation, training, evaluation)

Ray on Cloud supports quick cluster creation:

ray up cluster.yaml

YAML configuration specifies GPU count, instance type, and autoscaling:

cluster_name: llm-training
max_workers: 10
docker:
  image: rayproject/ray-ml:latest

worker_nodes:
  - aws_instance_type: g4dn.2xlarge
    count: 4

Ray autoscales based on pending tasks. Unused workers terminate automatically. Pay only for active resources.

Limitations: Ray adds overhead compared to bare-metal SLURM. Suitable for workloads tolerating 5-20% overhead. Not optimal for throughput-critical batch processing.

Pricing model: No licensing cost. Pay for cloud instances. Autoscaling reduces waste. Typical 3-cluster costs $2-5k/month for active training.

Kubernetes for Container Orchestration

Kubernetes manages containerized applications at scale. Provides abstraction over hardware. NVIDIA GPU Device Plugin enables GPU scheduling.

Kubernetes GPU orchestration:

apiVersion: v1
kind: Pod
metadata:
  name: llm-training-pod
spec:
  containers:
  - name: training
    image: llm-training:latest
    resources:
      limits:
        nvidia.com/gpu: 4
      requests:
        memory: "128Gi"
        cpu: "32"
    env:
    - name: CUDA_VISIBLE_DEVICES
      value: "0,1,2,3"

Kubernetes handles:

• GPU scheduling across nodes
• Pod placement optimization
• Network connectivity
• Persistent storage mounting
• Service discovery

Distributed training with StatefulSet:

apiVersion: apps/v1
kind: StatefulSet
metadata:
  name: distributed-training
spec:
  replicas: 4
  template:
    spec:
      containers:
  - name: trainer
        image: llm-training:latest
        resources:
          limits:
            nvidia.com/gpu: 1

Kubernetes automatically launches training pods on separate nodes with GPU.

Kubernetes excels at:

• Mixed workloads (batch + interactive + services)
• Multi-team environments with resource isolation
• Complex networking requirements
• Production inference serving
• Infrastructure as code

Limitations: Steep learning curve. Requires expertise in containers, networking, and storage. YAML configuration verbose and error-prone. Overkill for single-team pure ML workloads.

Pricing model: Kubernetes itself free (open-source). Hosted services (EKS, GKE, AKS) charge management fee plus compute. Comparable to self-managed SLURM for mature deployments.

Comparison Matrix

Cost Analysis

SLURM on dedicated hardware: $500-2000/month for 4-GPU cluster. Ownership cost amortized over 5 years. Best for sustained large-scale workloads.

Ray on cloud instances: $2-5k/month for active training. Autoscaling reduces idle costs to near-zero. Best for bursty, dynamic workloads.

Kubernetes on cloud: $3-8k/month for mixed workloads including serving. Higher management overhead. Best for mature production systems.

Break-even analysis:

Small workloads (<100 GPU-hours/month): Use RunPod on-demand at $0.34/hour per RTX 4090.

Medium workloads (100-500 GPU-hours/month): Ray on cloud instances or SLURM spot instances.

Large workloads (>500 GPU-hours/month): Dedicated SLURM cluster or negotiated cloud contract.

Integration Patterns

Hybrid approach: SLURM for batch training, Ray for hyperparameter search, Kubernetes for serving. Specialization matches strengths.

Data flow:

1. Ray conducts distributed hyperparameter search
2. Best hyperparameters go to SLURM for final large-scale training
3. Trained model deployed to Kubernetes for inference serving

Staging environments: Develop and test on Ray locally. Scale production training on SLURM. Serve predictions through Kubernetes.

Migration Strategies

Moving from SLURM to Ray: Export training scripts, wrap in Ray train functions. Most existing code works unchanged. Gradual migration possible.

Moving from Ray to Kubernetes: Containerize Ray application, create Kubernetes manifests, deploy. Ray provides Kubernetes integration for automated manifest generation.

Moving from Kubernetes to SLURM: Extract training logic from container, add SLURM job directives, submit. Requires more refactoring.

Practical Recommendations

Use SLURM if:

• Operating dedicated on-premise cluster
• Training runs measure in days/weeks
• Static resource needs
• Team has HPC experience

Use Ray if:

• Conducting research or development
• Hyperparameter tuning important
• Using cloud resources
• Team prefers Python-first interfaces

Use Kubernetes if:

• Managing mixed workloads (ML training + serving + analytics)
• Multiple teams sharing infrastructure
• Production stability critical
• Existing Kubernetes expertise

FAQ

Can SLURM and Ray work together? Yes. Ray jobs can submit to SLURM. Ray-on-SLURM mode provides best of both. Contact SLURM for resource reservation, Ray handles job scheduling.

Which tool offers best fault tolerance? Ray and Kubernetes handle faults automatically with minimal config. SLURM requires manual checkpointing. Ray and Kubernetes recommended for reliability.

Can I run SLURM in Kubernetes? Technically yes, but defeats purpose. Run Kubernetes-native workloads on Kubernetes, SLURM on bare metal.

What's the learning curve for each? SLURM: 2-4 weeks for basics, months for advanced cluster tuning. Ray: 1-2 weeks to basic proficiency. Kubernetes: 4-8 weeks minimum, often months for production expertise.

Which supports multi-GPU training best? All three handle multi-GPU training. SLURM and Ray more straightforward. Kubernetes requires explicit resource specification and network setup.

Can I use spot instances with these tools? SLURM: Limited spot support, requires manual interruption handling. Ray: Excellent spot integration with automatic fallback. Kubernetes: Good spot support with pod disruption budgets.

Which scales easiest from 1 GPU to 100 GPUs? Ray designed for horizontal scaling. Ray clusters autoscale smoothly. SLURM requires cluster reconfiguration. Kubernetes scales well but requires infrastructure planning.

Related Resources

GPU Cloud Pricing Trends:Are GPUs Getting Cheaper? Best GPU Cloud for LLM Training:Provider and Pricing Best GPU Cloud for AI Startup:Provider and Pricing

Sources

SLURM documentation and user guide Ray distributed computing framework documentation Kubernetes GPU scheduling documentation NVIDIA GPU Device Plugin for Kubernetes Ray on Kubernetes integration guide