13 posts tagged mlops from Omnissiah Systems.
Activation recomputation trades GPU memory for extra compute during training. Here's when that tradeoff works, when it breaks, and how to tune it.
A practical breakdown of where time disappears during model weight loading at startup, and how to profile and reduce it in production inference systems.
How request routing silently degrades across multi-instance inference deployments, the failure modes to recognize, and how to diagnose and correct them.
Speculative decoding promises faster token generation, but production deployments expose failure modes that synthetic benchmarks quietly hide. Here's what to watch.
How dynamic batching misconfigures itself over time, why throughput numbers lie on aging inference servers, and how to catch batch size drift before it costs you.
A practical breakdown of how FlashAttention allocates GPU memory during prefill, where the pressure points are, and how to diagnose OOM failures before they surprise you.
Long prompts silently inflate time-to-first-token in LLM serving. Here's how prefill cost accumulates and what you can do about it operationally.
How pipeline bubbles form in multi-stage model inference, how to measure them precisely, and what scheduling changes actually reduce their cost.
A field guide to the failure modes that emerge when you scale tensor parallelism across GPU ranks: communication bottlenecks, numerical drift, and load imbalance.
A practical guide to KV cache eviction policies in distributed LLM inference: what triggers eviction, how it degrades latency, and how to tune against it.
How misconfigured gradient accumulation silently corrupts large model training runs, and the specific checks you need to catch it before loss curves lie to you.
Checkpoints feel like safety nets, but saved model state degrades in subtle ways. Here's how to detect checkpoint rot before it costs you a training run.