local companion

Local model ran out of memory

The model, context, or training process requested more VRAM, unified memory, or system RAM than the device could safely provide.

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Curious Obmil examines three clearly separated illuminated diagnostic paths inside a brass-and-crystal crossroads — Decode the cause before choosing the response.

MATCH THESE VISIBLE STRINGS

CUDA out of memory
out of memory
failed to allocate memory
metal out of memory

LIKELY CAUSES

Model or quantization is too large
Context length or batch size is too high
Other applications consume memory
Training needs more memory than inference

NEVER DO THIS

Do not use page-file or swap success as proof of acceptable speed
Do not start training merely because inference barely fits

SMALLEST DIAGNOSTIC FIRST

Repair sequence

01

Record model size, quantization, context, RAM, and VRAM

02

Run the hardware checker with the exact values

03

Close unrelated memory-heavy applications

04

Reduce context or batch size

05

Choose a smaller or more heavily quantized model

06

Treat inference fit and training fit as separate decisions