Offline batches, local models, and heterogeneous fleets

Picture it

Think of inference as a spectrum of urgency. At one extreme, you have a massive overnight batch job—like a freight train slowly but efficiently moving a mountain of data at peak throughput. At the other extreme, you have a tiny model running entirely on a user's local phone—a nimble bicycle weaving through traffic with zero latency and total privacy. Navigating this heterogeneous landscape means placing the workload exactly where the physical constraints of power, time, and hardware align with the task's true purpose.

What to understand

• Offline queues, checkpointed work units, spot/preemptible capacity
• CPU/unified memory, GPU/TPU portability, local inference, and privacy

Follow the references

1. 1 · Code + guide

   ggml · llama.cpp ↗

   Inspect supported backends and quantized local inference; measure on hardware you have.

2. 2 · Book chapter

   How To Scale Your Model · Transformer inference ↗

   Compare bulk, interactive, and edge objectives before choosing a serving configuration.

Make it concrete

Compare a local small model or CPU run with an available accelerator baseline. Include transfer, startup, completion deadline, quality, and energy/cost assumptions.