Quick Comparison
| Feature | Single Ollama | DIY Scripts | exo | LiteLLM | GPUStack | Ollama Herd |
|---|---|---|---|---|---|---|
| Multi-device routing | No | Manual | No (splits models) | Cloud providers | Yes | Yes |
| Zero-config setup | Yes | No | Yes | Config file | Install + config | 2 commands |
| mDNS auto-discovery | No | No | Yes | No | Yes | Yes |
| Thermal-aware routing | No | No | No | No | No | Yes |
| Memory pressure detection | No | No | No | No | No | Yes |
| Meeting detection | No | No | No | No | No | Yes (macOS) |
| Capacity learning | No | No | No | No | No | 168-slot model |
| Per node:model queues | No | No | No | Rate limiting | Yes | Yes |
| Multi-signal scoring | No | No | No | Provider-level | Engine selection | 7 signals |
| Model fallbacks | No | No | No | Yes | No | Yes |
| Auto-retry on failure | No | No | No | Yes | No | Yes |
| Auto-pull missing models | No | No | No | No | Yes | Yes |
| Real-time dashboard | No | No | Limited | Admin panel | Web UI | SSE + 8 tabs |
| Request tagging/analytics | No | No | No | Yes | No | Yes |
| OpenAI API compatible | No | Fragile | No | Yes | Yes | Yes |
| Ollama API compatible | Yes | Partial | No | Via config | No | Yes |
| Multimodal (images + STT) | No | No | No | No | No | Yes |
| Target user | Single machine | Tinkerers | Model sharding | Cloud gateway | GPU clusters | Personal fleet |
| Best for | One Mac, one user, simple setup | Learning, prototyping with 2–3 machines | Running one huge model across multiple GPUs | Routing between cloud API providers | Enterprise GPU cluster management | 2–5 Macs running mixed workloads (LLM + image + STT) |
Detailed Comparisons
vs. Single Ollama
Running one Ollama instance is the starting point. It works great — until you have more than one machine or more than one concurrent user.
When single Ollama is enough:
- You have one machine
- You run one model at a time
- You don't mind waiting in a queue
When you need Herd:
- You have 2+ machines and want them to work together
- Multiple tools hit Ollama simultaneously (agents, coding assistant, chat)
- You're tired of model thrashing (loading/unloading models to free memory)
- Your MacBook fans spin up during inference and you want requests routed elsewhere
vs. exo
exo splits a single large model across multiple devices using tensor parallelism. If one machine can't fit a 405B model, exo distributes the layers so they collectively run it.
exo and Herd solve different problems. exo answers "how do I run a model too big for one machine?" Herd answers "how do I route many requests to many models across many machines?" They're complementary — an exo cluster can register as a single Herd node.
Choose exo when: You need to run one model that's too large for any single device.
Choose Herd when: You have multiple devices that can each run their own models and you want intelligent routing across all of them.
vs. LiteLLM
LiteLLM is a cloud API gateway that provides a unified OpenAI-compatible interface to 100+ LLM providers (OpenAI, Anthropic, Bedrock, Azure, etc.).
Different layer entirely. LiteLLM routes between cloud providers. Herd routes between local devices. LiteLLM has no concept of thermal state, memory pressure, device health, or mDNS discovery. They work together naturally — Herd sits between LiteLLM and your local Ollama instances, giving LiteLLM a single "local" endpoint backed by an intelligent fleet.
Choose LiteLLM when: You need to route between cloud providers or want a unified API across OpenAI/Anthropic/etc.
Choose Herd when: You want your local devices to work together. Use both if you want local + cloud with intelligent routing at each layer.
vs. GPUStack
GPUStack is a GPU cluster manager for AI model deployment. It manages GPU resources across environments (on-prem, Kubernetes, cloud), auto-configures inference engines (vLLM, SGLang, TensorRT-LLM), and supports all GPU vendors.
GPUStack is more polished but more complex. It targets GPU cluster operators who want multi-engine support and enterprise features. Herd targets individuals and small teams who want zero-config fleet management with the Ollama they already use.
Choose GPUStack when: You're managing a GPU cluster with mixed vendors and need multi-engine support.
Choose Herd when: You have a few personal devices running Ollama and want them to work together in 60 seconds.
vs. DIY Scripts
Many people write their own routing scripts — round-robin across Ollama instances, manually checking which node has capacity, or just SSH-ing into whichever machine seems free.
DIY works until it doesn't. You'll spend more time maintaining the scripts than using them. No thermal awareness, no capacity learning, no auto-retry, no dashboard, no meeting detection. Every edge case becomes your problem.
Choose DIY when: You have very specific routing logic that no tool supports.
Choose Herd when: You want routing that handles the edge cases you haven't thought of yet.
What Makes Herd Unique
No other project combines all of these:
- 7-signal intelligent scoring with learned latency data
- Per node:model queue management with dynamic concurrency
- mDNS zero-config discovery — truly two commands
- Adaptive capacity learning — learns your weekly usage patterns
- Meeting detection + app fingerprinting — respects that laptops aren't servers
- Multimodal routing — LLM, embeddings, image gen, and speech-to-text
- Both OpenAI and Ollama API formats — drop-in for any client
- Real-time dashboard with fleet overview, trends, health, and analytics
The market is fragmenting into three niches: model splitting (exo), cloud API gateways (LiteLLM), and local fleet routing. Herd owns the local fleet routing niche — purpose-built for people with multiple devices who want one smart endpoint.