Measuring Gemma & On-Device Latency on 16GB Apple Silicon for Agent Workflows

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Critical latency benchmarks for local models on Apple Silicon, directly actionable for multi-agent workflow design.

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Why It Matters

For engineers building multi-agent systems on local hardware, this data shows that TTFT and E2E measure different bottlenecks, and that prompt engineering—not model swapping—is the highest-leverage optimization for reducing per-call latency in agent chains.

Key Takeaways

Measure your rendered prompt token count before blaming model latency, and choose between TTFT-optimized (Gemma E2B) or E2E-optimized (Apple fm) models based on whether your agent makes streaming or blocking calls.

Measuring Gemma & On-Device Latency on 16GB Apple Silicon for Agent Workflows

Summary

Benchmarking local models on a 16GB M1 MacBook Air reveals that Gemma 4 E2B (4-bit) achieves the fastest time-to-first-token (TTFT) at 152ms, while Apple's on-device fm system delivers the best end-to-end (E2E) latency at 886ms for short outputs. The critical finding is that prompt template bloat—not model choice—dominates latency: Ministral 3 3B's TTFT jumped from 0.56s to 3.23s when a default template added 530 prompt tokens. For agent workflows with routing or classification steps, optimizing prompt length matters more than selecting a faster model.

Author

JimmyLiao