vLLMHigh-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 → colibriPure-C, zero-dep MoE runtime that runs GLM-5.2 (744B) on a 25GB-RAM consumer box by streaming experts from disk — VRAM/RAM/NVMe as one tiered hierarchy, never touching precision.
Why switchBoth run open models locally. Ollama is the multi-model daily driver for models that fit; colibrì is a single-model specialist that makes a 744B MoE fit where nothing else will.
Full comparison → airllmLayer-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 → FreeTokenEdge-native MoE serving engine: bandwidth-adaptive CPU-GPU co-execution, global LRU expert caching and elastic VRAM run 290B+ frontier MoE models on a gaming PC at interactive speed.
Why switchollama is the one-command runner for local models that fit and the friendlier daily driver; FreeToken exists precisely for the frontier MoE weights that don't fit.
Full comparison → mesh-llmDistributed LLM inference in Rust: pool GPUs across machines into one OpenAI-compatible endpoint — local fit first, mesh routing, and stage splits for models too large for any single box.
Why switchBoth give you a local OpenAI-compatible endpoint for GGUF-family models; Ollama serves from one machine, mesh-llm pools many and routes to whoever can serve.
Full comparison → sieSelf-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-dDistributed 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 →