Llama choice now means benchmarking, not guesswork

The post reflects a real shift in local LLM use: model selection is now an engineering optimization problem, not just a preference call.

• –Teams increasingly need task-specific benchmarks before committing to larger checkpoints.
• –Quantization choice is becoming as important as model size for real-world throughput and memory fit.
• –A practical workflow is to set latency and VRAM budgets first, then test quality thresholds against them.
• –For coding and long-context workloads, the “largest that fits” approach still needs regression-style evals to justify the tradeoff.