Ollama Herd vs Apple's Distributed MLX

What is Apple's Distributed MLX?

At WWDC26, Apple shipped an official distributed inference stack for MLX, available in macOS 26.2+. It has three components:

• RDMA over Thunderbolt 5, direct memory access between Macs at 50-60 Gbps with microsecond-scale latency. A first-party OS-level primitive.
• JACCL, Apple's open-source collective communication library (all-reduce, all-gather, broadcast) that automatically selects mesh vs ring topology based on message size.
• MLX Distributed, the distributed backend for MLX itself, supporting tensor and pipeline parallelism with sharded weights across a multi-Mac cluster.

Demonstrated at WWDC26: Qwen 27B running ~3x faster on four M3 Ultras than on one, and a trillion-parameter model running across four 512GB Mac Studios. For a single huge model on a pure-Apple cluster with Thunderbolt 5, nothing else is faster.

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.

How Apple's Stack Works

MLX Distributed splits a single large model across multiple Macs so it can run models that would not fit on any one machine, the same problem category as exo, but first-party:

• Tensor parallelism: individual layers are sharded across devices, computed in parallel. Needs the high-bandwidth Thunderbolt 5 RDMA interconnect.
• Pipeline parallelism: layers split sequentially across devices. Lower bandwidth requirement, but sequential.
• MoE expert distribution: Mixture-of-Experts models can spread experts across devices with auto-configured placement.

JACCL handles the collective operations underneath, auto-selecting network topology at runtime. The whole cluster serves one model, that's the design point, and it's also the key difference from a router.

Feature Comparison

Where Apple's Stack Wins

1. Raw model-size ceiling on pure-Apple clusters. Trillion-parameter models across four Mac Studios is a category nothing else matches today. If your workload is "run the biggest possible model on a Mac cluster," MLX Distributed is state-of-the-art.
2. Single-request latency on huge models. Tensor parallelism over Thunderbolt 5 RDMA gives near-linear speedup. One request to a 70B+ model completes faster on two MLX Distributed nodes than on one Herd-routed node.
3. First-party status. Apple ships it, Apple maintains it, the OS provides the RDMA primitives. No risk of an OS update breaking the interconnect.
4. Zero-tuning collective communication. JACCL picks mesh vs ring topology automatically at runtime based on message size.
5. Free RDMA. If you already own Thunderbolt 5-connected Mac Studios, you get datacenter-class interconnect without buying InfiniBand.

Where Ollama Herd Wins

1. Mixed hardware. Apple's stack is Apple-only by design. Herd routes across Apple Silicon Macs, Linux boxes, and Windows workstations in the same fleet. A lab with three M3 Ultras and two Linux GPU boxes can't use MLX Distributed across all five, Herd can.
2. Multi-model, multi-user fleets. MLX Distributed dedicates the whole cluster to one model. Real teams run coding assistants, embeddings for RAG, image generation, and transcription simultaneously. Herd routes each request to the best node for that model.
3. Multimodal routing. Five model types with capability-aware routing. Apple's stack is text LLM via MLX.
4. Multi-backend flexibility. Herd routes to MLX, Ollama, fastembed, and vision embedding servers on the same fleet. MLX Distributed serves only what MLX serves.
5. 8-signal device-aware scoring. Thermal state, memory fit, queue depth, latency history, role affinity, availability trend, context fit, session affinity. Apple's stack has no concept of per-request scoring because the whole cluster serves one model.
6. Ready-made API surface. OpenAI + Ollama + Anthropic Messages out of the box. Point Claude Code CLI at your fleet with one env var. With MLX Distributed you build and maintain the HTTP layer yourself.
7. Adaptive capacity learning. Herd learns each device's weekly usage patterns and de-prioritizes busy machines; meeting detection pauses routing during video calls. Apple's stack ignores what else the machine is doing.
8. Operational visibility. 8-tab dashboard, 30+ health checks, request tracing, per-model per-node analytics. Apple ships a library and a WWDC session.
9. Zero-config discovery. A new node joins the LAN and appears in the fleet within 60 seconds via mDNS. No cluster reconfiguration.

Why They're Complementary

Apple's Distributed MLX and Ollama Herd operate at different layers, exactly the way exo and Herd do:

+--------------------------------------------------+
|              Ollama Herd (routing)                |
|  Routes requests to the best node                 |
+----------+----------+-----------------------------+
|  Mac #1  |  Mac #2  |  MLX Distributed cluster    |
|  Ollama  |  Ollama  |  (Mac Studio #3 + #4)       |
|  7B-70B  |  7B-70B  |  Running a frontier model   |
+----------+----------+-----------------------------+

Expose the MLX Distributed cluster behind an OpenAI-compatible or Ollama-compatible endpoint and Herd routes to it as one very powerful node:

• Small/medium models get routed across individual Ollama/MLX nodes by Herd
• Frontier-scale models get served by the MLX Distributed cluster, also reachable through Herd
• Multimodal workloads (embeddings, image gen, STT, vision) get routed by Herd to capable nodes
• One unified API for every application, OpenAI, Ollama, or Anthropic Messages

When to Choose

Bottom Line

Apple's Distributed MLX is a distributed compute layer for Apple Silicon, it makes a small cluster of Macs act like one much bigger Mac. Ollama Herd is a distributed routing layer, it makes many machines of any kind serve many workloads intelligently. They solve adjacent problems.

The typical MLX Distributed user has 2-4 Macs hardwired via Thunderbolt 5 running one frontier model. The typical Herd user has 3-8 machines on a network running a dozen different models for a team or agent workload. Apple shipping first-party distributed inference validates the whole "local AI fleet" category, and more MLX Distributed clusters in the world means more infrastructure that eventually wants a router in front of it.

Getting Started

Ollama Herd works alongside Apple's stack. If you already have Ollama or MLX running on your Macs, Herd discovers them automatically and starts routing in under two minutes.

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

FAQ

Is Ollama Herd made obsolete by Apple's Distributed MLX?

No, they solve different problems. Apple's stack shards one model across a pure-Apple cluster for maximum single-model throughput. Herd routes many models across a heterogeneous fleet with device-aware scoring. If your fleet is mixed hardware, you run multiple model types, or you want intelligent request distribution, Herd remains the answer.

Can I use Ollama Herd with Apple's MLX Distributed?

Yes. Expose your MLX Distributed cluster as an OpenAI-compatible or Ollama-compatible endpoint (a small HTTP wrapper) and Herd routes to it as if it were a single very powerful node. You get sharded inference for the biggest models plus intelligent routing across the rest of the fleet.

Does Apple's stack support Linux or Windows?

No. Apple's Distributed MLX is Apple Silicon only, requires macOS 26.2+, and performs best on Thunderbolt 5. If your fleet includes Linux boxes with GPUs or Windows workstations running Ollama, Apple's stack can't include them. Herd can.

Does Ollama Herd require Thunderbolt 5?

No. Ollama Herd works over standard WiFi or Ethernet. It routes requests to devices rather than sharding model layers, so it does not need the high-bandwidth interconnect that MLX Distributed benefits from.

Is Ollama Herd free?

Yes. Ollama Herd is open-source under the MIT license. No paid tiers, no API keys, no subscriptions.

See Also

• Ollama Herd vs exo, community distributed inference framework, same sharding-vs-routing distinction
• Ollama Herd vs LM Link, LM Studio's Tailscale-based remote access for local models
• Ollama Herd vs GPUStack, enterprise GPU cluster manager with multi-backend support