Deployment Principles for Local Models in Production: Beyond Benchmarks

When evaluating a model for on-premise inference, most academic benchmarks focus on isolated metrics: MMLU, GPQA scores, theoretical throughput. But for systems that truly need to operate in production — not just in demos — other numbers matter: permissive license, memory footprint, serving architecture.

Why License and Architecture Change the Rules

SMEs choosing on-premise AI for privacy or compliance often find themselves in a false dilemma: small models (7B-8B) that struggle with complex tasks, or medium models (20B-30B) that require expensive GPUs and restrictive licenses. A 12B model with an Apache 2.0 license, an encoder-free multimodal architecture (text, documents, audio, vision in a single stream), and a context window ≥ 256K breaks this dilemma: it eliminates the orchestration overhead that currently kills pilot projects.

The Practical Insight: Serving Is the Real Differentiator

End-user perceived latency does not depend on the model's tokens/second, but on the **time-to-first-token (TTFT) of the complete system