vLLM alternatives
Curated alternatives to vLLM — and why you'd switch.
Ollama
Run Llama, Mistral and other open models locally with a single command and a clean API.
Why switchBoth serve open models locally; vLLM optimizes for throughput, Ollama for one-command simplicity.
Full comparison →sie
Self-hosted inference cluster for everything agents call besides the big LLM: embeddings, rerankers, OCR, NER, guardrails and small LLMs — 100+ models, one OpenAI-compatible API, K8s stack included.
Why switchBoth self-hosted OpenAI-compatible inference servers, opposite shapes: vLLM maximizes throughput for one big LLM; SIE serves breadth — 100+ heterogeneous task models (embedders, rerankers, OCR, NER, guards) loaded on demand across a cluster.
Full comparison →airllm
Layer-by-layer inference that runs 70B models on a 4GB GPU — no quantization required; 405B on 8GB, DeepSeek-V3 671B on ~12GB. One AutoModel line for most open model families.
Why switchOpposite ends of the local-inference spectrum: vLLM maximizes throughput given abundant VRAM (production serving); AirLLM minimizes VRAM given abundant patience (frontier-size models on consumer cards).
Full comparison →