Operations
Running GPU workloads day to day: utilization, monitoring, failure handling, scheduling and workflow integration, SLAs and support.
15 articles
Articles
2 January 2026
GPU Utilization Too Low: How to Fix Compute Bottlenecks
When you monitor your training jobs and see GPU utilization sitting far below what the hardware can deliver, you are paying for capacity you never use. In high-performance environments, especially those utilizing NVIDIA H100 or A100 GPUs, the hardware is often faster than the software feeding it. This mismatch creates a 'starvation' effect where the GPU completes its work and waits for the next batch. It is common in teams whose data pipelines were built for smaller models and never updated for modern compute scales. Fixing this requires a systematic approach to profiling, data orchestration, and memory management to ensure your compute investment is fully leveraged.
24 May 2026
GPU Cloud API CI/CD Automation: Scaling ML Pipelines
Managing GPU infrastructure manually slows down model deployment and inflates costs. Integrating GPU cloud APIs directly into your CI/CD pipeline enables automated testing, faster iteration, and scale-to-zero efficiency.
27 May 2026
Migrating GPU Workloads from Slurm to Kubernetes: A Practical Guide
Moving from Slurm to Kubernetes often means trading predictable batch scheduling for YAML complexity and silent hangs. Navigate the transition, maintain high GPU utilization, and build a unified AI infrastructure stack.
26 May 2026
Kubernetes GPU Node Setup for ML: Fixing Idle Allocation and OOM Crashes
Kubernetes GPU utilization across the industry is persistently low. Here is how to configure your nodes, schedule workloads efficiently, and stop burning budget on idle infrastructure.
26 May 2026
How to Run a Production ML Pipeline Without a DevOps Team
Managing your own GPU infrastructure is a massive engineering bottleneck. Learn how to decouple compute from operations and run end-to-end ML pipelines without hiring a dedicated DevOps team.
25 May 2026
GPU Fault Tolerance in Distributed Training: A Technical Guide
Hardware failures are inevitable when scaling AI workloads across hundreds of GPUs. Learn how to implement robust fault tolerance in distributed training to prevent catastrophic job restarts and wasted compute.
14 May 2026
The ML Engineer Guide to GPU VM SSH Access and Scaling
Managing local hardware creates bottlenecks, but legacy cloud pricing destroys budgets. You need raw, reliable GPU access that scales without locking you into proprietary ecosystems.
12 May 2026
GPU Cloud SLA Uptime Comparison 2026: The True Cost of Downtime
Two hours of downtime on a 128-GPU H100 cluster wastes about 700 USD of compute at Lyceum's listed on-demand rate, before idle engineering time. Evaluate GPU cloud SLAs on exclusions, capacity and data residency, not on the headline number.
4 May 2026
First GPU Cloud Setup: The ML Startup Guide to Infrastructure
Transitioning from local hardware or expiring cloud credits to production infrastructure is a critical inflection point for ML startups. This guide breaks down how to architect your first scalable, EU-sovereign GPU cloud environment without falling into vendor lock-in.
16 January 2026
Optimize Slurm GPU Allocation for High Performance AI Workloads
GPU scarcity and high operational costs make inefficient scheduling a terminal risk for AI startups. We break down how to tune Slurm for maximum throughput while maintaining the data sovereignty your enterprise clients demand.
31 December 2025
PyTorch Memory Profiling in Production: A Guide to Efficiency
Out-of-memory errors in production are more than a technical hurdle; they represent a direct failure in system reliability and cost efficiency. Effective memory profiling requires a shift from local debugging to continuous, low-overhead monitoring that identifies leaks and fragmentation before they crash your sovereign GPU cluster.
29 December 2025
Eliminating CUDA OOM: Expert Memory Management for LLMs
The dreaded RuntimeError: CUDA out of memory is the primary bottleneck for scaling large language models in production. This guide provides the technical framework to optimize VRAM utilization through quantization, attention mechanisms, and distributed orchestration.
22 December 2025
Solving OOM Errors in 70B Model Fine-Tuning
You hit the wall. Your terminal is flooded with CUDA Out of Memory errors while trying to fine-tune a 70B parameter model. This is not a hardware shortage; it is a memory orchestration challenge that requires a precise technical response.
19 December 2025
Solving CUDA Out of Memory Errors in Llama Fine-Tuning
The torch.cuda.OutOfMemoryError is the most common roadblock for engineers fine-tuning Llama models. This guide breaks down the technical strategies to bypass VRAM limits and scale your training on sovereign infrastructure.
17 December 2025
How to Prevent OOM Errors in PyTorch Training
Nothing halts a training run faster than the dreaded CUDA Out of Memory error. As models grow and datasets expand, managing VRAM becomes a critical engineering discipline rather than a trial and error exercise.