Post by Xinference

Choosing your LLM inference stack shouldn't take 2 weeks of research. But most teams spend weeks comparing frameworks, reading benchmarks, and setting up POCs, only to realize 3 months later they picked the wrong tool for the job. Here's the thing: The right choice depends on 5 questions, not 50 benchmarks. 📌 A 5-question decision tree for your inference stack: Q1: Do you need data privacy or compliance control? └ If no → a managed API (OpenAI, Anthropic) may be enough. └ If yes → you need self-hosted or private-cloud inference. Keep going. Q2: Are you serving multiple model types? (LLM + embedding + reranking + speech + vision) └ If just one model type → a single-framework setup works. └ If multiple → you need a unified serving layer. Keep going. Q3: Do you need sub-200ms latency for production? └ If not latency-critical → a lightweight wrapper + queue may work. └ If yes → you need optimized inference (continuous batching, KV cache reuse, speculative decoding). Keep going. Q4: Will you scale beyond a single GPU? └ If no → single-node deployment is fine. └ If yes → you need multi-node orchestration, load balancing, and fault recovery. Keep going. Q5: Does your team need one unified API across all models? └ If no → you can stitch together multiple tools. └ If yes → you need a unified inference platform. If you answered "yes" to 3+ of these: You don't need another framework comparison. You need an inference platform that handles all of this in one layer. That's exactly what we built Xinference to do: → 300+ models (LLM, embedding, rerank, vision, speech) → One OpenAI-compatible API → Multi-GPU / multi-node with auto load balancing → vLLM, SGLang, Transformers, MLX — switch per workload → Self-hosted, private cloud, or managed — same engine Key takeaways: – Most inference decisions can be resolved with 5 questions, not 50 benchmarks – If you only need one model type + managed API, keep it simple – If you're running production multi-model workloads with privacy needs — a unified platform saves months of integration The goal isn't to use the most advanced stack. It's to use the simplest stack that won't break when you scale. Save this. Repost 🔁 if it helps someone stuck in framework comparison paralysis. Check the link in comments 👇 #OpenSource #LLM #AIInfrastructure #ModelDeployment #Xinference #MLOps #GPUOptimization