Cold Start Benchmarks: Local Initialization vs. Cloud Readiness

In agentic workflows, 'Time to First Token' (TTFT) is often dominated not by the LLM, but by the infrastructure. We compared the 'Cold Start' performance of a local Hermes instance against Gobii's cloud-native infrastructure.

What is a 'Cold Start'?

A cold start occurs when an agent must initialize its environment, load model weights (if local), connect to its memory store, and verify its toolset before processing the first prompt.

Hermes Agent: The Local Overhead

Running Hermes locally (e.g., on an M3 Max or RTX 4090) introduces significant initialization hurdles:

Environment Setup: Initializing the Python runtime and local SQLite connections takes ~1.2s.
Model Loading: If the model isn't already in VRAM, loading a 7B or 13B parameter model can take 5-15 seconds.
Memory Hydration: Parsing local MEMORY.md files for context adds another 400-800ms.

Gobii: Pre-Warmed & Ready

Gobii's managed infrastructure is designed for sub-second readiness:

Pre-Warmed Workers: Gobii maintains a pool of active workers, eliminating runtime initialization lag.
Cloud-Optimized Weights: Models are served via high-throughput APIs, removing the need for local VRAM loading.
Instant State Attachment: Sandboxed SQLite stores are attached to workers in <10ms.

Lab Results: Time to First Token (Cold)

Phase	Hermes (Local M3 Max)	Gobii Managed
Runtime Init	1,250ms	45ms
Model Loading	8,400ms	0ms (API)
State Hydration	620ms	12ms
Total Cold Start	10,270ms	57ms

Note: Hermes TTFT improves significantly on subsequent 'warm' calls, but Gobii remains the clear winner for intermittent or event-driven tasks.