What are key bottlenecks in deploying LLMs at scale in production systems?

Major bottlenecks include latency, memory bandwidth, GPU cost, and token throughput limits.

Production LLM deployment faces constraints in KV cache memory, batch inference efficiency, cold start latency, and GPU utilization. Optimization strategies include speculative decoding, quantization, caching, and model sharding. Serving architectures often use async pipelines and request batching systems.