LM Link makes one remote LM Studio instance feel local, over an encrypted Tailscale mesh. Herd routes your whole workload across a fleet with 8-signal scoring. Remote access vs orchestration, different questions, different tools.
LM Link is a feature from LM Studio and Tailscale, launched in early 2026 and opened to everyone (waitlist removed) in June 2026 with LM Studio 0.4.16. It uses tsnet, Tailscale's embedded library, to create end-to-end encrypted peer-to-peer connections between devices signed into the same LM Studio account.
When enabled, a remote LM Studio instance appears as if it were running on localhost:1234, the standard local LM Studio endpoint. Any tool that already talks to local LM Studio, Claude Code, Codex, OpenCode, Continue.dev, works over LM Link without configuration changes. Automatic device discovery, no open ports, works across LAN, cloud VMs, and hybrid setups.
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.
LM Link is a transport layer, not a routing layer:
tsnet).localhost:1234.localhost:1234 transparently uses the remote instance.It's excellent at what it does: secure remote access to a single LM Studio server. It does not distribute requests across multiple servers, score which server is best, support backends other than LM Studio, or aggregate models across instances.
| Feature | LM Link | Ollama Herd |
|---|---|---|
| Core approach | Remote-access transport via Tailscale mesh | Multi-node request routing with scoring |
| Primary use case | Reach one LM Studio server from anywhere | Route requests across a fleet, pick the best node |
| Devices served per request | One remote server at a time | Any node in the fleet, scored per request |
| Backends supported | LM Studio only | MLX + Ollama + fastembed + vision embedding |
| Network model | Encrypted mesh VPN (Tailscale), works over WAN | LAN (mDNS) or any VPN you already run |
| Intelligent routing | None, one remote server per session | 8-signal weighted scoring per request |
| Request distribution | None | Per-node:model queues, dynamic concurrency |
| Health monitoring | None | 30+ automated health checks |
| Auto-retry on failure | No, single server | Transparent retry on next-best node |
| Adaptive scheduling | None | 168-slot weekly behavioral model + meeting detection |
| Multimodal routing | Whatever the one remote server does | LLM + embeddings + image gen + STT + vision, capability-aware |
| Anthropic Messages API | No (OpenAI-compat only) | Yes, native, no format conversion |
| Claude Code CLI | Via OpenAI-compat shim | Native, one env var |
| Dashboard | LM Studio's local UI | 8-tab live dashboard with SSE |
| Multi-user teams | Single-account by design | Team fleets are the core use case |
| License | Proprietary (LM Studio) + Tailscale | MIT, fully open source |
| Setup | Toggle in LM Studio, sign in | pip install ollama-herd on one machine |
LM Link answers "how do I use my remote model from anywhere?" Herd answers "how do I get the best node in my fleet to serve this request?"
They can even layer: run a VPN mesh (Tailscale, WireGuard) across your machines, and Herd's routing works over it, Herd's routing brain on Tailscale-class transport. LM Link bakes the transport in; Herd stays transport-agnostic.
| Scenario | Choose |
|---|---|
| One Mac at home, want to reach it from a laptop anywhere | LM Link |
| Solo LM Studio user, wants secure remote access, zero setup | LM Link |
| Team of 2-10 sharing a fleet of machines | Ollama Herd |
| Fleet mixes MLX + Ollama + fastembed backends | Ollama Herd |
| Multiple model types (LLM + embeddings + image gen + STT + vision) | Ollama Herd |
| Native Anthropic Messages API for Claude Code CLI | Ollama Herd |
| Fleet includes Linux boxes running Ollama | Ollama Herd |
| Secure cross-WAN access AND fleet routing | Both, layered (Herd routing over a VPN mesh) |
LM Link is one of the more thoughtful local-AI products of 2026, it solved secure remote access with a genuinely elegant Tailscale design. But it's a remote-access primitive, not a fleet router. Ollama Herd is a routing engine that scores every available node against every incoming request.
If you're all-in on LM Studio and need to reach one machine remotely, use LM Link. If you have multiple machines that should serve as one intelligent fleet, especially a heterogeneous one, especially for agents, use Herd.
If you already have Ollama or MLX running on your machines, 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
Then point Claude Code CLI at the fleet:
export ANTHROPIC_BASE_URL=http://<router-ip>:11435
export ANTHROPIC_AUTH_TOKEN=dummy
claude
Yes, via LM Studio's OpenAI-compatible endpoint through a compat layer. Ollama Herd supports Claude Code natively via the Anthropic Messages API, one env var, no shim, no format loss, plus three-layer context management for long sessions.
Not as of the 2026 preview. LM Link exposes one remote LM Studio server at a time as localhost:1234. It does not evaluate which of several servers is best for a given request. That's what Herd does.
Yes, Herd's routing is transport-agnostic. Run a VPN (Tailscale, WireGuard, or similar) across your machines and Herd's discovery and routing work over the mesh. LM Link bakes Tailscale in; Herd stays out of the transport layer.
Possibly, LM Link's mesh transport could eventually gain routing on top. As of mid-2026 it hasn't, and LM Studio's product direction emphasizes the polished single-user experience rather than multi-user fleet operations.
Yes. Ollama Herd is open-source under the MIT license. No paid tiers, no API keys, no subscriptions, no accounts.