Start large ML containers with lazy image pulling
Use eStargz and containerd for lazy image pulls, then measure a Polyaxon GPU job through its first completed CUDA operation.
KubernetesContainersPerformance
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The latest guides, tutorials, and product updates.
Measure MIG and GPU time-slicing under co-load
Run synchronized training and inference workers on Polyaxon, compare MIG with time-slicing, and measure the shared interval rather than misleading solo utilization.
KubernetesGPUPerformance
Profile a slow PyTorch training run on Polyaxon
Capture a short CPU/CUDA trace, identify whether input loading or GPU work is delaying training, and measure a targeted change without profiler overhead.
MLOpsPyTorchGPU
Benchmark vLLM speculative decoding on Polyaxon
Compare ordinary decoding with prompt-lookup proposals under the same model, workload, and GPU allocation, including latency, failures, and output checks.
LLMOpsvLLMInference
Align CPU and GPU resources with Kubernetes NUMA policies
Combine CPU Manager eligibility, full-core allocation, and Topology Manager policy, then compare CPU/GPU workloads with Polyaxon.
KubernetesGPUPerformance
Measure AI agent workflow bottlenecks
Measure Polyaxon agent workflow bottlenecks across planning, queueing, execution, transfers, and evaluation using critical-path timing and quality-aware comparisons.
AI AgentsPerformanceObservability
Keep Polyaxon sandboxes ready with bounded lifetimes
Keep Polyaxon sandbox services ready for active sessions while controlling idle cost, absolute lifetime, state persistence, and cleanup.
SandboxesPerformanceOperations
Reduce AI agent startup latency with Polyaxon
Reduce Polyaxon agent startup latency by measuring queueing, image preparation, initialization, and readiness before tuning capacity or workspace reuse.
AI AgentsPerformanceScheduling
Tune checkpoint storage with Kubernetes VolumeAttributesClass
Change persistent-volume performance profiles, inspect the completed change, and compare checkpoint behavior in Polyaxon GPU jobs.
KubernetesStoragePerformance
When CPU-accelerated AI inference makes sense
Evaluate CPU inference using model fit, precision, latency, throughput, memory, Kubernetes placement, and end-to-end cost instead of assuming every model needs a GPU.
InferenceKubernetesPerformance