[Verse 1] Your AI model's running slow, users tapping fingers cold Million requests per minute, servers buckling from the load Time to shrink those hefty weights, quantization holds the key Thirty-two bits down to eight, speed without complexity [Chorus] Cache it, batch it, quantize the math API or self-host, choose your path Milliseconds matter when the traffic grows Optimize the pipeline, watch performance glow Speed and cost together, find the balance sweet Make your AI lightning fast and lean [Verse 2] Batching groups your queries tight, parallel processing might Handle twenty in one shot instead of twenty separate fights Caching stores repeated calls, Redis keeping answers near Skip the computation walls when the same request appears [Chorus] Cache it, batch it, quantize the math API or self-host, choose your path Milliseconds matter when the traffic grows Optimize the pipeline, watch performance glow Speed and cost together, find the balance sweet Make your AI lightning fast and lean [Bridge] OpenAI or run your own, trade-offs weigh like stone APIs scale without the pain but costs can break your zone Self-hosting gives control but hardware bills unfold GPU clusters burning gold, infrastructure bold [Verse 3] Quantization takes your weights from floating point precise Down to integers that slice through calculations twice as nice Eight-bit math runs blazing fast, accuracy stays intact Inference optimized at last, performance is a fact [Chorus] Cache it, batch it, quantize the math API or self-host, choose your path Milliseconds matter when the traffic grows Optimize the pipeline, watch performance glow Speed and cost together, find the balance sweet Make your AI lightning fast and lean [Outro] Measure twice and optimize once Balance speed with what it costs Every millisecond counts When your AI system mounts
← MLOps: Managing AI Models in Production | AI Quality Control: Evaluation and Guardrails →