Ollama alternatives
Curated alternatives to Ollama — and why you'd switch.
vLLM
High-throughput, memory-efficient inference and serving engine for LLMs.
Why switchBoth serve open models locally; vLLM optimizes for throughput, Ollama for one-command simplicity.
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 switchBoth run open models on your own hardware, on opposite sides of one constraint: Ollama gives fast, polished local inference for models that fit your VRAM; AirLLM runs models that don't fit at all — 70B on 4GB — by streaming one layer at a time, at heavy latency cost.
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 switchSame 'serve open models behind one local API' job at different scales: Ollama is the single-machine developer runner; SIE is the multi-model production cluster with autoscaling, gateway and Terraform.
Full comparison →llm-d
Distributed inference stack for Kubernetes from Red Hat, Google and IBM (CNCF) — prefix-cache-aware routing, tiered KV-cache, prefill/decode disaggregation and SLO autoscaling above vLLM/SGLang.
Why switchSame job — serve open models on your own hardware — at opposite scales: Ollama is one command on one machine; llm-d is a CNCF stack for multi-node GPU fleets. Outgrow one, reach for the other.
Full comparison →