OOM

OOM (out of memory) is one of the most common runtime failures when deploying ML services; in containerized or PaaS free-tier environments, the memory available to a process is usually hard-capped (for example, 512MB or 1GB).

On a machine with 2 GB of memory, what actually causes OOM is often not average memory usage, but the sudden spike in peak RSS during a particular rendering or decoding step.

On a server with 1 vCPU / 2 GB of memory, knowing whether peak memory is approaching the OOM line is more useful than knowing average usage; ru_maxrss records the historical high-water mark and can catch short memory bursts.

On a machine with 2GB of memory, running multiple parallel processes for image tasks reduces efficiency due to resource contention; calling gc.collect() after processing each asset helps free memory between assets.

OOM (Out of Memory) is the most commonly encountered failure in local inference: when the VRAM jointly occupied by model weights, KV cache, and concurrent requests exceeds GPU capacity, the CUDA runtime directly terminates the process.

A severely overspecified model parameter count will cause an OOM (out-of-memory) error and prevent startup, while a slightly overspecified count will borrow regular memory and cause a significant drop in speed

For devices with tight memory, blindly using a larger quantization model may result in frequent OOM errors.

OOM, or Out of Memory, refers to a situation where the memory requested by a program exceeds the available system limit, causing the operating system to forcibly terminate the process.

OOM (out-of-memory / GPU memory overflow) is a stability threat unique to large model inference services, because GPU VRAM is a physically isolated fixed resource that cannot be temporarily expanded using virtual memory or swap the way CPU memory can.

After loading a 70B-parameter model onto an 80GB A100, the space left for the KV cache is extremely limited, and when concurrency increases or context length grows, the KV cache can rapidly exhaust VRAM, triggering OOM and crashing the inference process.

VRAM fragmentation can cause large tensor allocation to fail with an OOM error even when the total remaining VRAM is sufficient, because no contiguous block of adequate size can be found.

One of the core bottlenecks for running large language models locally is memory capacity, as model parameters need to be loaded into RAM or VRAM.

When memory is pushed to the limit, Linux's OOM mechanism may kill the service process due to excessive RAM usage; this can be avoided by setting oom_score_adj to -1000 to prevent the system from mistakenly killing it.

Long-context reasoning accumulates large attention matrices in GPU VRAM, and when the context window approaches the model's maximum length limit, VRAM consumption rises sharply, leading to out-of-memory (OOM) errors.

If the gateway implementation relies on unbounded in-memory queues to buffer chunks from upstream, under high-concurrency scenarios of 5,000 to 10,000 active streams, memory usage will grow linearly with connection duration, directly causing OOM crashes.