mesh-llm alternatives
Curated alternatives to mesh-llm — and why you'd switch.
colibri
Pure-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 attack 'model bigger than your box': mesh-llm scales OUT by pooling GPUs across machines into one endpoint, colibrì scales DOWN by deepening one machine's memory hierarchy to disk.
Full comparison →Ollama
Run Llama, Mistral and other open models locally with a single command and a clean API.
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 →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 answers to 'the model doesn't fit': AirLLM streams layers from disk on one small GPU (slow, solo), mesh-llm splits stages across peers' GPUs (faster, needs friends).
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 switchDistributed inference at opposite trust levels: llm-d orchestrates vLLM across a Kubernetes cluster you own; mesh-llm federates volunteer boxes over the open internet.
Full comparison →