A whole field of tools has emerged to route local AI requests, round-robin dispatchers, model-aware proxies, per-user fair-share queues. Most solve one slice. Exactly one, Olla, competes on the whole problem, and it's built for interchangeable servers, not workstations. Herd is the only integrated solution with device-condition scoring, multimodal routing, mDNS auto-discovery, capacity learning, and a real dashboard.
A growing ecosystem of open-source tools addresses pieces of the local-AI routing problem. Some are lean round-robin dispatchers; some add model-aware routing or per-user fair-share queues; some manage model loading on a single machine. Each solves one slice of fleet coordination. Very few attempt the whole problem, full routing intelligence, multimodal support, capacity learning, and observability, and of those that do, only one is a serious peer to Herd.
Olla (built by TensorFoundry) is the standout of this field and the closest thing to a direct competitor Herd has. It's a high-performance Go proxy supporting more backend engines than Herd, vLLM, SGLang, LMDeploy, llama.cpp, Docker Model Runner, Ollama, and more, with production-proxy machinery like circuit breakers and KV-cache sticky sessions. If your fleet is a rack of dedicated, interchangeable inference servers across many engines, Olla is excellent.
The difference is philosophical. Olla treats machines as endpoints with a health status, listed in a config file. Herd treats them as workstations with conditions, is this Mac in a meeting, thermally throttling, being actively used, does it already have the model warm, discovered automatically via mDNS. Olla is the better generic proxy; Herd is the better scheduler for a fleet of real Macs that people also work on. See the full Ollama Herd vs Olla comparison →
Ollama Herd is an open-source smart multimodal AI router that turns multiple Ollama instances across Apple Silicon devices into one intelligent endpoint. It routes LLMs, embeddings, image generation, speech-to-text, and vision with a 8-signal scoring engine, mDNS auto-discovery, and an 8-tab real-time dashboard. Two commands to set up, zero config files. pip install ollama-herd or brew install ollama-herd.
ollama_proxy_server (~200 stars), Python proxy that sits in front of multiple Ollama instances. Provides API key authentication and model-aware routing. Routes requests to whichever backend has the requested model loaded. Simple, functional, limited.
ollama_load_balancer (~50 stars), Rust-based parallel request dispatcher. Round-robins requests across Ollama instances. Fast, minimal, no intelligence. Think nginx for Ollama.
llama-swap (~2.7K stars), Model loading/unloading orchestrator for a single Ollama instance. Automatically swaps models in and out of GPU memory based on incoming requests. Solves the "I have 10 models but only enough VRAM for 2" problem on one machine.
SOLLOL (~4 stars), The most ambitious of the small tools. Context-aware scoring, priority queues, auto-discovery, and a dashboard. Closest to Herd's vision but far less mature, with minimal community validation.
OLOL (~23 stars), Ollama inference cluster tool. Groups Ollama instances and distributes requests. Basic clustering without scoring intelligence.
Hive (~17 stars), Task queue for Ollama. Queues inference requests and dispatches them to available backends. More of a job scheduler than a router.
OllamaFlow, Label-based routing layer, now archived (its successor is a heavier multi-tenant control plane, Conductor). Included here for lineage.
The current-generation slice tools, a newer wave has more traction: ollamaMQ (per-user fair-share queues for shared labs), ollamafarm (multi-Ollama management with offline detection), and LoLLMs Hub (a broader secure orchestration platform). Each is strong at its slice. None combine device-condition scoring, learned availability, multimodal routing, and mDNS discovery the way Herd does, and for the whole-problem comparison, Olla remains the one serious peer.
| Feature | ollama_proxy_server | ollama_load_balancer | SOLLOL | llama-swap | Ollama Herd |
|---|---|---|---|---|---|
| Multi-instance routing | Yes | Yes | Yes | No (single node) | Yes |
| Load balancing | Model-aware | Round-robin | Scored | N/A | 8-signal scoring |
| Auto-discovery (mDNS) | No | No | Yes | No | Yes |
| API key auth | Yes | No | No | No | No |
| Priority queues | No | No | Yes | No | Yes |
| Real-time dashboard | No | No | Basic | No | 8-tab dashboard |
| Capacity learning | No | No | No | No | Yes |
| Thermal awareness | No | No | No | No | Yes |
| GPU memory tracking | No | No | Partial | Yes (single) | Yes (fleet-wide) |
| Dynamic context optimization | No | No | No | No | Yes |
| Smart benchmark | No | No | No | No | Yes |
| Model swapping | No | No | No | Yes | No (routes instead) |
| Multimodal support | LLM only | LLM only | LLM only | LLM only | LLM, embed, img, STT |
| OpenAI API compat | Yes | Yes | Partial | No | Yes |
| Ollama API compat | Yes | Partial | Partial | Yes | Yes |
| Language | Python | Rust | Python | Go | Python |
| Test coverage | Minimal | Minimal | Minimal | Moderate | 1200+ tests |
| Health checks | Basic | None | Basic | None | 16 checks |
| Active maintenance | Sporadic | Low | Low | Active | Active |
In theory, you could wire together ollama_proxy_server for auth + model-aware routing, ollama_load_balancer for throughput distribution, and llama-swap on each node for model memory management. This gives you auth, distribution, and model swapping. But you still lack:
And you're maintaining three tools from three different authors with three different update cycles and no guarantee they play well together. Herd is the integrated answer. One install, zero config, all the intelligence.
The existence of 7+ Ollama routing tools validates that the problem is real. People with multiple Ollama instances need a way to coordinate them. But the fragmented, single-feature nature of these tools shows that nobody has built the complete solution, except Herd.
None of these tools are competitive threats individually. They're validation signals. The fragmented landscape confirms the need; Herd is the complete answer.
pip install ollama-herd # or: brew install ollama-herd
herd # start the router
herd-node # on each deviceYes. They solve different problems. llama-swap manages model loading and unloading on a single machine with limited VRAM. Herd routes requests across multiple machines. You can run llama-swap on individual nodes for model memory management while Herd handles fleet-level routing.
Not yet. This is a genuine gap compared to ollama_proxy_server. If API key gating is your primary requirement for shared access, ollama_proxy_server handles that today. Herd is designed for trusted local networks.
You could combine ollama_proxy_server for auth, ollama_load_balancer for dispatch, and llama-swap for model management. But you still lack intelligent scoring, auto-discovery, multimodal routing, capacity learning, and a unified dashboard. You are also maintaining three tools from three authors with no integration guarantees.
SOLLOL is conceptually the closest to Herd, attempting scored routing, priority queues, and auto-discovery. However, it has minimal community validation (~4 stars), limited test coverage, and no multimodal support. Herd has 1200+ tests, 30+ health checks, and routes 5 model types.
The proxy layer overhead is minimal in both. ollama_load_balancer adds the least possible latency for pure round-robin dispatch, but the routing decision itself (1–2ms) is negligible compared to actual inference time (seconds). Herd's scoring intelligence more than compensates by sending requests to the node that will complete fastest.