XDA’s writeup on Nvidia’s Personal AI Router hit a nerve: two PCs behind one endpoint, model requests routed by weight and health, no editing config files in five apps. The kicker was that Nvidia’s release is one flavor of a category the open-source stack already covers, and covers with wider model support. If you have a workstation with a 4090 and a mini PC running Ollama in the closet, a proxy that unifies them is the missing piece.
We tested seven load balancers and gateways for local LLM inference in 2026. Every pick sits in front of Ollama, vLLM, LM Studio, llama.cpp, or a mix, exposes one OpenAI-compatible endpoint, and handles routing when one backend is busy, offline, or the wrong tool for the job.
What to look for in a local AI load balancer
- OpenAI-compatible surface. Every serious client (Open WebUI, Cline, Continue, Aider, Zed) expects
/v1/chat/completions. If your router speaks a bespoke API, you spend the next weekend patching clients. - Backend coverage. Ollama, vLLM, LM Studio, llama.cpp, and SGLang are the common local engines. Cover them all or you rip out the router in three months.
- Health checks and failover. Local rigs go offline. A router that keeps sending requests to a downed GPU is worse than round-robin.
- Cost-and-capability routing. Not every prompt needs a 70B model. Route lightweight prompts to a small local model and reserve the big one for hard cases.
- Observability. Logs, per-model latency, and request traces are the difference between “it’s slow” and “the 8B is eating all traffic because it always answers first.”
Quick comparison
| App | Best for | Backends | License | Open source |
|---|---|---|---|---|
| LiteLLM | Universal proxy with 100+ providers | Any OpenAI-compatible | MIT | Yes |
| Olla | Pure local, tiny footprint | Ollama, vLLM, LM Studio, SGLang, llama.cpp | Apache 2.0 | Yes |
| Nvidia Personal AI Router | Nvidia-hardware setups on Windows | Nvidia-optimized backends | Free (registration) | No |
| vLLM | High-throughput single-model serving | vLLM engine | Apache 2.0 | Yes |
| Ollama | Simplest multi-model host | Native | MIT | Yes |
| LocalAI | Drop-in OpenAI clone with more media models | GGUF, ONNX, diffusers | MIT | Yes |
| Portkey | Enterprise gateway with policy controls | 200+ providers | AGPL / paid tier | Source-available |
The 7 best apps for load-balancing local AI inference
1. LiteLLM — best overall
LiteLLM runs as a Python or Rust proxy in front of your models and hands out one /v1 endpoint that speaks OpenAI. Point it at any mix of Ollama, vLLM, hosted Anthropic and OpenAI, and it will load-balance across them, fall back on 429s, cache repeated prompts, and enforce per-key budgets. The 2026 Rust rewrite pushes throughput past 1500 requests per second on a single node, which is far more than a home lab will ever need but pleasant on a shared team gateway.
Where it falls short: The config file grows quickly once you add fallback trees and cost tiers. The dashboard is functional but nothing you would show off.
Pricing: Free and open source (MIT). A paid enterprise tier adds SSO and audit logs.
Platforms: Windows, macOS, Linux, Docker.
Download: litellm.ai · GitHub
Bottom line: If you want one router that covers local rigs today and hosted APIs tomorrow, this is where to start.
2. Olla — best pure-local option
Olla is a purpose-built LLM proxy for self-hosted inference. It talks to Ollama, LM Studio, vLLM, llama.cpp, SGLang, and LiteLLM itself, discovers models across each backend automatically, and stays under 50 MB of memory. Failover between two Ollama boxes is a five-line YAML file, and the exposed metrics feed Prometheus without a plugin.
Where it falls short: No built-in cost tracking or budget enforcement, because it does not touch hosted APIs. Community is smaller than LiteLLM’s.
Pricing: Free and open source (Apache 2.0).
Platforms: Windows, macOS, Linux, Docker. A single static binary.
Download: thushan.github.io/olla · GitHub
Bottom line: Pick this if your entire stack is on hardware you own and you want a router that boots in a second.
3. Nvidia Personal AI Router — best turnkey option on Nvidia hardware
Nvidia Personal AI Router is the XDA piece’s subject. It ships as a Windows app that scans your LAN, finds Nvidia-based inference nodes, and pools them behind a local endpoint. Model routing, streaming, and quantization handoffs are handled inside the router, and the client apps in the same suite (Nvidia ChatRTX and Nvidia AI Workbench) plug straight in.
Where it falls short: Nvidia-first: Radeon and Intel Arc rigs are second-class citizens. The router does not expose an OpenAI-compatible endpoint natively, so third-party clients need a shim.
Pricing: Free with an Nvidia developer account.
Platforms: Windows.
Download: nvidia.com/ai-router
Bottom line: Easiest way to pool two Nvidia rigs into one AI endpoint if you never leave the Nvidia ecosystem.
4. vLLM — best for maxing a single beefy GPU
vLLM is not a router by itself, but its OpenAI-compatible server with continuous batching and PagedAttention pushes throughput on a single GPU past what Ollama can reach by a wide margin. In a two-node setup, running vLLM behind LiteLLM or Olla is the standard pattern for serving one big model to a team, while cheaper backends handle the long tail.
Where it falls short: Model loading is slower than Ollama; no CPU-only fallback. Quantization support lags llama.cpp.
Pricing: Free and open source (Apache 2.0).
Platforms: Linux with CUDA, ROCm, or Intel XPU. Windows via WSL2. macOS Metal support is community-driven.
Bottom line: Use this as one of the backends your router hands work to, not as the router itself.
5. Ollama — best for simple multi-model hosting
Ollama stays on the list because most home setups already run it, and its built-in queue handles multiple concurrent requests reasonably well. Pair it with a router in front for high availability, or run two Ollama boxes with Olla for hot spare failover.
Where it falls short: Throughput per GPU trails vLLM by 4-8x on batch workloads. No cost tracking, no routing intelligence.
Pricing: Free and open source (MIT).
Platforms: Windows, macOS, Linux, Docker.
Download: ollama.com · GitHub
Bottom line: The reliable backend, not the router. Keep it, and put something smarter in front.
6. LocalAI — best if the router doubles as an inference host
LocalAI is a single binary that speaks the OpenAI API and hosts LLMs, embedding models, TTS, image generation, and speech-to-text. It can call other Ollama or vLLM backends behind the scenes, which makes it a decent all-in-one for a single-node home lab where you would rather run one process than three.
Where it falls short: Less flexible than LiteLLM for hybrid local-plus-hosted setups. Media-model support means the binary is heavy.
Pricing: Free and open source (MIT).
Platforms: Windows, macOS, Linux, Docker.
Download: localai.io · GitHub
Bottom line: Good pick if you want a router and a full media-model host on the same box.
7. Portkey — best if you need policy controls
Portkey is the enterprise-flavored option. It fronts local and hosted models the way LiteLLM does but adds guardrails, PII redaction, and per-team routing policies out of the box. The self-hosted OSS version covers the routing basics; SSO, audit, and the managed dashboard sit behind the paid tier.
Where it falls short: AGPL scares some homelabbers off. The full policy engine is only useful if a team leans on it.
Pricing: Free self-hosted (AGPL); paid managed tiers start at a modest per-seat monthly fee.
Platforms: Docker on any host OS.
Download: portkey.ai · GitHub
Bottom line: Overkill for solo homelab, right-sized for a small team gateway.
How to pick the right one
- If you want one router that grows from home lab to hosted APIs: LiteLLM.
- If your entire stack is local and you want the smallest possible footprint: Olla.
- If both PCs are Nvidia and you never leave Windows: Nvidia Personal AI Router.
- If a small team needs guardrails and policy: Portkey.
- If you want a router that also hosts diffusers and TTS: LocalAI.
- Keep Ollama as your reliable backend and vLLM for the single beefy GPU.
FAQ
What is the difference between a load balancer and an inference engine?
An inference engine (vLLM, Ollama, llama.cpp) runs the actual model. A load balancer or gateway (LiteLLM, Olla) sits in front and decides which engine gets each request. You need both: one to serve, one to route.
Can I load-balance local models with a hosted API as a fallback?
Yes. LiteLLM and Portkey are built for exactly this. Configure the local model first with a lower cost weight, then a hosted provider as fallback; the router uses the hosted API only when your GPU is down or overloaded.
Do I need a router if I only have one GPU?
Not strictly, but it still helps. A router gives you a stable endpoint, retry logic, and observability, so if you swap Ollama for vLLM later you do not need to touch any client apps.
Does the Nvidia Personal AI Router work with AMD or Intel GPUs?
Not officially. It targets Nvidia hardware and CUDA. For mixed setups, LiteLLM or Olla will treat any OpenAI-compatible backend the same regardless of vendor.
Which one has the lowest latency?
Olla adds under 5 ms on a local network in tests, thanks to its Go core. LiteLLM sits around 10-15 ms with the Rust engine on a warm cache. For most chat and coding workloads, either is invisible next to the model’s own decode time.