Ollama Herd vs OLOL

TL;DR

OLOL and Herd both cluster Ollama, but they optimize for different things. OLOL's headline feature is distributed inference: it can split a large model across several machines so you can run something too big for any one of them, over a gRPC architecture with automatic quantization handling. Herd doesn't shard models; it routes whole requests to whole nodes, scoring each on thermal state, memory fit, and model residency, and it does it for embeddings, image generation, and speech-to-text as well as chat, behind OpenAI and Anthropic APIs. If you need to run a model bigger than one machine, look at OLOL (or exo). If you need intelligent, multimodal routing across a fleet, look at Herd.

What is OLOL?

OLOL (Ollama Load Balancer) is a Python, gRPC-based clustering system for Ollama (39 stars, MIT). It presents a unified, Ollama-compatible API, load balances across servers, keeps session affinity for chats, and can pull models to multiple machines for redundancy. Its most ambitious feature is distributed inference: it automatically partitions the layers of a large model (13B and up) across servers so the cluster can run models too big for a single box, with quantization selected to fit each machine. It also includes subnet auto-discovery. Its last commit was in August 2025.

What is Ollama Herd?

Ollama Herd is an open-source smart multimodal AI router that turns multiple inference nodes across Apple Silicon and mixed hardware into one intelligent endpoint. It routes LLMs, embeddings, image generation, speech-to-text, and vision with a 8-signal scoring engine, mDNS auto-discovery, an 8-tab real-time dashboard, and OpenAI + Ollama + Anthropic Messages API compatibility. Two commands to set up, zero config files. pip install ollama-herd or brew install ollama-herd.

The core difference: shard vs route

This is the same distinction as exo and Apple's distributed MLX, applied to Ollama:

• OLOL shards. For a large model, it splits the layers across machines so several devices collaborate on one response. That lets you run a model no single machine could hold. It's genuinely more ambitious than a plain load balancer.
• Ollama Herd routes. Each request runs entirely on one node; Herd's job is to pick the best node, based on which machine has the model hot, how much memory it has, how busy it is, and whether it's a workstation someone is actively using. It can't run a model too big for one machine, but it makes a fleet of independent machines serve many models and modalities intelligently.

They're not really competitors on their strongest axes. An OLOL (or exo) cluster could even sit behind Herd as one powerful node. Where they overlap is the plain "spread requests across Ollama machines" job, and there the difference is Herd's device-aware scoring, multimodal routing, and API breadth versus OLOL's gRPC clustering.

Feature comparison

Where OLOL wins

1. Distributed inference. The big one. OLOL can run a model too large for any single machine by splitting it across the cluster. Herd cannot; it routes whole requests to whole nodes.
2. Quantization intelligence. It picks compatible quantization levels per machine and can serve a lower-quality request from a higher-quality loaded model.
3. gRPC architecture. If you want a typed, protobuf-defined clustering contract between coordinator and inference servers, OLOL is built that way.
4. Redundancy by design. Pulling models to multiple servers for high availability is a first-class feature.

Where Ollama Herd wins

1. Device-aware scoring. Herd routes on thermal state, memory fit, model residency, queue depth, and whether a Mac is in a meeting. OLOL load balances and tracks model availability, but doesn't model the condition of each machine.
2. Multimodal and multi-API. Herd routes embeddings, image generation, speech-to-text, and vision, and speaks OpenAI and Anthropic Messages so Claude Code and any OpenAI client point straight at it. OLOL is Ollama-focused over gRPC.
3. Appliance simplicity. Two commands and a pip install, no gRPC or protobuf stack to stand up.
4. Operational visibility and active development. A real 8-tab dashboard, 30+ automated health checks, and ongoing releases.
5. Workstation-first design. Herd is built for real Macs that people also use for work, not just dedicated inference servers.

When to choose

Bottom line

OLOL is the most architecturally ambitious of the Ollama load balancers: a gRPC clustering system that genuinely shards models across machines. That's a real capability Herd doesn't have. But for the far more common job of routing a fleet of independent machines intelligently, across every modality and behind the APIs your tools already speak, Herd is the more complete, actively-developed, appliance-simple answer. And if you need both, an OLOL cluster can serve behind Herd as one powerful node.

Getting started

pip install ollama-herd    # or: brew install ollama-herd
herd                       # start the router
herd-node                  # on each device

FAQ

Does Ollama Herd do distributed inference like OLOL?

No. OLOL shards one large model across machines; Herd routes whole requests to whole nodes. For a model too big for one machine, OLOL or exo is the right shape.

Which is simpler to run?

Herd (pip install, two commands, no gRPC stack). OLOL involves a Python gRPC and protobuf setup.

Is Ollama Herd free?

Yes. Both projects are MIT licensed.

See Also

• Ollama Herd vs exo, the leading model-sharding project
• Ollama Herd vs Apple MLX Distributed, Apple's first-party sharding
• How to load balance Ollama, the full guide