Keep GPU jobs running

8 October 2026

Provisioning GPUs with Terraform: Provider & IaC Guide

Declare the GPU, driver and runtime separately. Prefer a native Terraform provider for VM lifecycle management. For an API-only service, keep an explicit operational boundary instead of treating a shell provisioner as a full provider.

23 February 2026

Maximizing VRAM: Gradient Checkpointing Memory Savings Guide

26 August 2026

Weight Sync Between Trainer and vLLM: The Hidden Cost in RL Loops

Moving updated policy weights from your trainer to vLLM for rollouts incurs a recurring time penalty that bleeds capital. This guide models the true cost of weight synchronization over disk, NCCL, and delta transfers, explaining how your cluster fabric sets the limit.

24 September 2026

Training MoE Models: Preventing Expert Collapse & Balancing Load

Training a Mixture-of-Experts model introduces a critical bottleneck: if the router favors a small subset of experts, the starved parameters waste memory while overloaded ones halt the distributed run. Here is how to prevent routing collapse and balance MoE loads effectively.

22 September 2026

Gradient Accumulation Loss Bug: Why Effective Batch Is Wrong

Gradient accumulation is assumed to be mathematically identical to full-batch training, but the standard implementation normalizes over the wrong denominator. Here is why the mean-of-means error skews weights, and how to verify if your fine-tuning setup is affected.

23 September 2026

Training Long-Context Models Without OOM: Ring Attention

When a training run fails on long sequences, the culprit is unsharded activation memory, not model parameters. Standard tensor and pipeline parallelism will not fix it. Context parallelism splits the sequence itself across GPUs, enabling massive contexts without OOMs.

21 September 2026

Sequence Packing: Reclaiming GPU Hours in Long-Context SFT

Padding waste silently inflates long-context SFT costs by processing empty tokens. This guide explains how to calculate token occupancy, migrate from length-grouped batching to true sequence packing, and prevent the three silent correctness bugs that destroy model quality.

16 September 2026

Checkpoint Resume Loss Spikes: Fixing Optimizer & LR State

When a long training run is interrupted, resuming from a checkpoint often triggers a massive loss spike. The most common culprits are missing optimizer moment buffers, reset learning rate schedulers, or mismapped FSDP shards - here is the triage order and recovery checklist.

17 September 2026

Multi-Node GRPO Orchestration: Ray, Slurm or Kubernetes

When scaling GRPO to multi-node clusters, Ray is not an alternative to Slurm or Kubernetes; it is the runtime that sits inside them. Discover why RL's co-dependent architecture makes gang scheduling non-negotiable and how to orchestrate your training jobs on Lyceum.

15 September 2026

What It Costs to Train a Text-to-Video Model: Real GPU Budgets

Calculate the real cost of a text-to-video training run using active parameters, latent tokens, and dense MFU. Build a realistic campaign budget in GPU-hours to multiply by your own quoted hardware rates.

15 September 2026

How Many GPUs for Trainer vs Rollout in RL Post-Training

In a disaggregated reinforcement learning pipeline, colocation is obsolete. Here is how to instrument your RL post-training loop, measure phase-level timings, and properly split your GPU fleet between the compute-bound trainer and the memory-bound rollout engine.

14 September 2026

GRPO vs DPO vs RLHF: Compute Cost and GPU Footprint Compared

Direct Preference Optimization (DPO), RLHF, and GRPO scale their compute footprints differently based on how many models they keep resident. Here is the definitive comparison of resident model headcounts, generation wall-clock time, and memory bandwidth constraints.

10 September 2026

GRPO VRAM & GPU Sizing: Policy, Reference and Reward

If a GRPO run is running out of memory, this guide accounts for every model copy the algorithm keeps resident. Sizing GPUs correctly requires accounting for the policy's optimiser state, inference copies, and the rollout cache before you start.

9 September 2026

Agentic RL GPU Cost: Multi-Turn Rollouts and KV Reuse

Agentic reinforcement learning repeats identical prefill calculations across turns and rollouts, compounding GPU hours. Moving a serving engine like vLLM into the training loop stops this waste by reusing the KV cache across the entire group.

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.

22 May 2026

Multi-GPU Tensor Parallelism Setup: Configuration and Optimization Guide

A 70B model needs about 140GB in FP16 and does not fit on one 80GB GPU. Tensor parallelism splits weight matrices across devices, at the cost of four all-reduce collectives per transformer layer in a training step.

20 May 2026

LoRA vs Full Fine-Tuning Memory Cost: VRAM Math

You have a 24GB GPU and an 8B model. The math says it should fit, but your training script crashes with an OOM error before the first epoch. We break down the exact VRAM requirements for full fine-tuning versus LoRA.

18 May 2026

FP8 Training on H100: Benchmarks and Memory Savings

Training a 70-billion parameter model in BF16 requires hundreds of gigabytes of GPU memory. Shifting to FP8 precision on NVIDIA H100s halves the bytes per element for the tensors actually held in FP8, master weights and optimizer states stay in higher precision, and NVIDIA's NeMo measurements show 1.30x throughput on Llama 3 8B and 1.43x on Llama 3 70B versus BF16.

16 May 2026

Multi GPU Distributed Training Setup Guide: Frameworks & Infrastructure

Scaling from a single GPU to a multi-node cluster introduces complex communication bottlenecks and fatal memory errors. Learn how to configure DDP, FSDP, and DeepSpeed while optimizing your infrastructure for maximum throughput.

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.

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.

23 February 2026

ZeRO-3 vs FSDP: A Deep Dive into Memory Efficiency for LLMs

Scaling large language models requires moving beyond standard data parallelism to overcome the memory wall. This technical guide compares DeepSpeed ZeRO-3 and PyTorch FSDP to help engineers optimize GPU utilization and eliminate out-of-memory errors.

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.