AIModel loading reportedly accounts for 55–70% of cold-start latency for small quantized LLMs on serverless CPUs
A post describing a Google paper says increasing Cloud Run memory from 4 GB to 8 GB gives the same model roughly twice the CPU, nearly halving warm inference time.
TLDR
According to a post describing a Google paper, model loading accounts for 55–70% of cold-start latency for small quantized LLMs on serverless CPUs. The post identifies getting model weights into memory—not generating tokens—as the frequent bottleneck. It also says increasing Cloud Run memory from 4 GB to 8 GB for the same model unlocks roughly twice the CPU, cutting warm inference time almost in half.
New Google paper shows for small quantized LLMs on serverless CPUs, 55–70% of cold-start latency is just model loading. i.e. the bottleneck is often moving model weights, not generating tokens. The inference itself is less of the problem than getting the model into memory.…
Model loading reportedly accounts for 55–70% of cold-start latency for small quantized LLMs on serverless CPUs
A post describing a Google paper says increasing Cloud Run memory from 4 GB to 8 GB gives the same model roughly twice the CPU, nearly halving warm inference time.
TLDR
According to a post describing a Google paper, model loading accounts for 55–70% of cold-start latency for small quantized LLMs on serverless CPUs. The post identifies getting model weights into memory—not generating tokens—as the frequent bottleneck. It also says increasing Cloud Run memory from 4 GB to 8 GB for the same model unlocks roughly twice the CPU, cutting warm inference time almost in half.
New Google paper shows for small quantized LLMs on serverless CPUs, 55–70% of cold-start latency is just model loading. i.e. the bottleneck is often moving model weights, not generating tokens. The inference itself is less of the problem than getting the model into memory.…
