llm-d vs Ollama
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. — versus — Run Llama, Mistral and other open models locally with a single command and a clean API.
Same 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.
| llm-d | Ollama | |
|---|---|---|
| Stars | 3.8k | 176k |
| Forks | 613 | 17k |
| Language | Shell | Go |
| License | Apache-2.0 | MIT |
| Last activity | yesterday | yesterday |
| Topics | local | local |
| Curated connections | 2 | 16 |
llm-d — the curator's take
The Kubernetes answer when one vLLM box stops scaling: prefix-cache-aware routing, disaggregated prefill/decode and tiered KV offloading deliver real, benchmarked wins (3x throughput, 2x TTFT in partner numbers) at fleet scale, with Red Hat/Google/IBM/NVIDIA behind it. NOT for a single GPU or a laptop — that's Ollama or plain vLLM territory — and not turnkey: you are operating Kubernetes, Helm charts and gateways. If you don't already run K8s, don't start here.
Ollama — the curator's take
Run Llama, Mistral and other open models locally with a single command and a clean API.