COURSE · OPS · A004
Local AI Clusters
Learn local ai clusters through RunLocalAI's practical lens: clusters, distributed, parallelism and kubernetes, hardware fit, runtime settings, verification habits and local-vs-cloud tradeoffs.
PREREQUISITES
- I004
- I016
Why this course matters
Local AI Clusters is for operators making local AI reliable, measurable and cheaper to run. It connects clusters, distributed, parallelism, kubernetes and networking to the questions RunLocalAI wants every reader to answer before they install, upgrade or scale a model: will it run, what will it cost in memory, what setting changes the result, and how do you verify the answer instead of trusting a demo?
What you will be able to do
By the end, you should be able to explain the main tradeoffs in plain language, choose a safe next experiment, and use the chapter exercises as a repeatable operator checklist. The course favors local evidence, hardware fit, context limits, latency and failure modes over generic AI vocabulary.
How to use this course
Start at chapter one if the topic is new. If you already have a working stack, scan for chapters such as Why Cluster?, Cluster Topology, Network Requirements and Shared Storage and use those lessons as a quality-control pass before changing a workstation, team workflow or production-like local deployment.
CHAPTERS
- 01Why Cluster?Single-node GPU limits are memory-bound before computation-bound. Clusters solve memory constraints through distribution, and throughput constraints through parallelism—both necessary for production LLM serving.10 min
- 02Cluster TopologyLLM collective communication patterns require high-bandwidth, low-latency networks. The bandwidth demand of all-reduce operations scales with the cluster size—misjudging network capacity is the most common cluster topology failure.10 min
- 03Network RequirementsCollective operation bandwidth is often the limiting factor in multi-node LLM serving, not raw GPU compute. Network specifications must match the collective communication demands of your parallelism strategy.10 min
- 04Shared StorageShared storage for AI clusters should separate model artifacts from training data. Model weights benefit from local caching with shared network access; training datasets require high-bandwidth parallel reads that distributed filesystems handle better.10 min
- 05Tensor ParallelismTensor parallelism reduces per-GPU memory footprint linearly but incurs collective communication overhead for each partitioned operation. The useful degree of tensor parallelism depends on your network bandwidth—exceeding 8 GPUs typically requires combining with pipeline parallelism.15 min
- 06Pipeline ParallelismPipeline parallelism reduces memory proportionally to pipeline depth but creates bubble overhead that reduces GPU utilization. Effective pipeline parallelism requires careful batch size and scheduling configuration—the naive implementation often performs worse than single-device baseline.15 min
- 07vLLM Distributed ServingvLLM manages distributed serving through torchrun initialization and internal collective operations. Correct hardware placement and proper torchrun configuration are prerequisites for successful distributed serving.15 min
- 08Multi-Node InferenceMulti-node inference introduces coordinator and network latency that can negate parallelism benefits. Profiling end-to-end latency breakdown by component identifies bottlenecks invisible in single-node testing.15 min
- 09Kubernetes Cluster SetupKubernetes networking for distributed training requires careful CNI configuration. Standard CNIs may introduce latency overhead incompatible with frequent collective operations. Host networking or SR-IOV CNI options reduce network overhead for AI workloads.15 min
- 10NVIDIA GPU OperatorThe GPU Operator centralizes GPU lifecycle management but introduces operator-specific failure modes: driver compilation timeouts on busy nodes, container runtime hook misconfigurations, and the temptation to run mixed driver versions. Explicit driver version pinning and pre-installation of system drivers often beats automatic reconciliation.20 min
- 11Slurm for AISlurm provides battle-tested scheduling for long-running GPU workloads with fair share scheduling, gang scheduling, and backfill optimization. The complexity lies in proper database configuration, partition definition, and understanding the distinction between `slurmctld` (controller) and `slurmd` (compute daemon) failure modes.20 min
- 12Model RepositoryA model repository eliminates redundant downloads and provides audit trails for model provenance. Using S3-compatible storage with path-based versioning offers a pragmatic balance between simplicity and functionality for local clusters without adding operational overhead of full MLOps platforms.20 min
- 13Load BalancingLoad balancing for inference workloads requires awareness of request duration, model loading times, and GPU memory constraints. Health check configuration directly impacts failure rates, and connection draining becomes essential when pods require graceful shutdown before termination.20 min
- 14Cluster MonitoringMonitoring GPU utilization patterns reveals both over-provisioned resources and opportunities for workload consolidation. Custom application metrics provide inference-specific observability while DCGM metrics expose hardware-level bottlenecks. Alert thresholds require tuning against actual workload characteristics.20 min
- 15Fault ToleranceFault tolerance in AI clusters combines Kubernetes HA patterns (anti-affinity, rolling updates) with application-level resilience (checkpointing, circuit breakers). GPU hardware failures require automated node eviction and replacement workflows rather than manual intervention.20 min
- 16Cost AnalysisThe real cost of AI inference includes amortized capital, electricity, operations, and opportunity cost from underutilized resources. Batch processing, proper resource requests, and node auto-scaling can reduce effective cost-per-token by 50-80% versus naive single-request serving.20 min
- 17Cluster BenchmarkingBenchmarking reveals that naive single-request serving wastes GPU capacity. The latency-throughput tradeoff is not linear—batching provides diminishing returns while latency grows super-linearly. Optimal configurations target specific SLAs rather than maximizing either metric in isolation.20 min
- 18Local AI Cluster ProjectEvery local AI cluster is a living system requiring ongoing attention to driver updates, model rotations, and monitoring gaps. The patterns established across these chapters—GPU Operator management, Slurm scheduling, repository versioning, load balancing, and fault tolerance—transform individual commands into an integrated, maintainable platform.25 min