AMD’s Lemonade quietly getting Nvidia GPU support is the sort of small release that solves a big real-world problem. Anyone running local LLMs on more than one machine has had to duplicate their model store, retune their quantisations, and juggle two different runtimes to keep their stack usable across a home desktop and a work laptop. Portability is the entire pitch of local AI, and until this year most of the runtimes were locked to a single hardware family.
We tested eight desktop local AI runtimes that make hardware portability a first-class feature. The list mixes purpose-built cross-vendor tools with older runtimes that have caught up on the multi-backend side. Every pick runs on at least Windows and Linux, six run on macOS with Apple Silicon acceleration, and all eight can move a GGUF model file from one machine to another with no rework.
What to look for in a portable local AI runtime
Portability is what earns a runtime a spot on the list, but a few things separate the picks that actually move well from the ones that claim to.
- Cross-vendor GPU support. Nvidia CUDA, AMD ROCm or Vulkan, Apple Metal, Intel Arc. Two vendors is the minimum; three is portable
- Model format neutrality. GGUF is the current lingua franca. Anything that only reads its own format loses points
- OpenAI-compatible API. If a runtime doesn’t expose one, your existing client code breaks on the move
- Config portability. A model card that ships in the runtime beats one you build from scratch on the target machine
- Install footprint. A single executable moves easier than a five-service Docker stack
- Model store. The runtime should not force a re-download of the model you already have on disk
Quick comparison
| Runtime | Best for | GPU vendors | OS | Standout |
|---|---|---|---|---|
| Ollama | Fastest zero-to-first-token | Nvidia, AMD, Apple, CPU | Windows, macOS, Linux | Model store you can point across machines |
| LM Studio | GUI-first cross-vendor testing | Nvidia, AMD, Apple, Intel | Windows, macOS, Linux | MLX backend on Apple Silicon |
| llama.cpp | The runtime everyone else forks | Nvidia, AMD (Vulkan), Apple | Windows, macOS, Linux | Single-binary release |
| Jan | Open-source ChatGPT replacement | Nvidia, AMD, Apple, CPU | Windows, macOS, Linux | Local OpenAI-compatible server on port 1337 |
| GPT4All | Beginners on modest hardware | CPU, Nvidia, AMD | Windows, macOS, Linux | Runs cleanly on Snapdragon X ARM |
| AMD Lemonade | AMD-first with Nvidia now supported | AMD NPU, AMD GPU, Nvidia GPU | Windows, Linux | NPU-aware scheduling for Ryzen AI 300 |
| KoboldCpp | Single-file long-context runtime | Nvidia, AMD, Apple, CPU | Windows, macOS, Linux | Ships image gen and TTS in the same binary |
| Text Generation WebUI | Power users who want every knob | Nvidia, AMD, Apple, CPU | Windows, macOS, Linux | Every loader and every quant type in one UI |
1. Ollama — Best for fastest zero-to-first-token
Ollama is the shortest path from ollama pull to a running model, and the Ollama model store is the closest thing local AI has to a package manager. The portability story got stronger in 2025 when the Windows and Linux builds moved to a shared model directory format, so pointing OLLAMA_MODELS at a synced folder means the same models appear on both machines.
Where it falls short: The default five-minute keep-alive reloads models constantly. Set OLLAMA_KEEP_ALIVE to a longer value on any single-user machine.
Pricing: Free, MIT licensed.
Platforms: Windows, macOS, Linux.
Download: ollama.com
Bottom line: Buy in if you want the fastest ergonomics. Ollama’s ecosystem is now bigger than every other runtime combined.
2. LM Studio — Best GUI-first cross-vendor testing
LM Studio ships one of the best GUI experiences for local AI, and its multi-backend support puts it a step ahead for cross-vendor testing. The MLX backend on Apple Silicon reads about 30 to 50 percent faster than the same model on llama.cpp Metal, which is a big margin for the Mac Studio and MacBook Pro crowd.
Where it falls short: The team commercial license changed pricing in 2025. Personal use is still free.
Pricing: Free for personal use.
Platforms: Windows, macOS, Linux.
Download: lmstudio.ai
Bottom line: The pick if you want a proper GUI and you split your work between Nvidia and Apple Silicon.
3. llama.cpp — Best runtime everyone else forks
llama.cpp is the reference implementation that most other runtimes wrap. The single-binary release since 2024 makes portability trivial, and the Vulkan backend added first-class AMD support without the ROCm install pain. Metal, CUDA, Vulkan, and CPU builds all read the same GGUF files.
Where it falls short: The CLI is the interface. There is no default GUI.
Pricing: Free, MIT licensed.
Platforms: Windows, macOS, Linux.
Download: github.com/ggerganov/llama.cpp
Bottom line: The pick if you want the leanest binary. Everyone else’s runtime sits on top of this.
4. Jan — Best OpenAI-compatible ChatGPT replacement
Jan is one of the strongest open-source ChatGPT replacements on desktop. Its OpenAI-compatible server on port 1337 means every OpenAI client library talks to it without rework, and the MCP support since 2024 lets Jan wire into tool-calling agents.
Where it falls short: Model store is smaller than Ollama’s.
Pricing: Free, Apache 2.0.
Platforms: Windows, macOS, Linux.
Download: jan.ai
Bottom line: Buy in if you want a ChatGPT replacement that keeps your existing OpenAI client code portable.
5. GPT4All — Best for modest hardware
GPT4All runs on hardware that other runtimes refuse to boot on. Snapdragon X ARM support has been a first-class citizen since 2024, and CPU-only inference is treated as a target, not a fallback. Nomic’s Local Docs feature keeps documents indexed on-device.
Where it falls short: Model catalog is curated smaller than Ollama or LM Studio.
Pricing: Free, MIT licensed, permissive commercial use.
Platforms: Windows, macOS, Linux.
Download: gpt4all.io
Bottom line: The pick if the target machine is a work laptop with no dedicated GPU.
6. AMD Lemonade — Best AMD-first with Nvidia support
AMD Lemonade started as AMD’s local AI stack for Ryzen AI 300-series NPUs, and the 2026 Nvidia support puts it in the portability conversation. Lemonade’s NPU-aware scheduling makes it the fastest local runtime on a Ryzen AI 300 laptop, and the Nvidia support means you can carry the same config across a work AMD laptop and a home Nvidia desktop.
Where it falls short: Documentation lags Ollama and LM Studio. macOS is not supported.
Pricing: Free, MIT licensed.
Platforms: Windows, Linux.
Download: lemonade-sdk.github.io
Bottom line: Best runtime for anyone on a Ryzen AI machine. Now portable to Nvidia if you split time between the two.
7. KoboldCpp — Best single-file long-context runtime
KoboldCpp ships as one executable, no installer, no service, and no dependencies. It reads GGUF, ships image generation and TTS in the same binary, and its context management is the strongest for long-form writing.
Where it falls short: The UI is functional, not pretty.
Pricing: Free, AGPL.
Platforms: Windows, macOS, Linux.
Download: github.com/LostRuins/koboldcpp
Bottom line: The runtime to keep on a USB stick.
8. Text Generation WebUI — Best for power users
Oobabooga’s Text Generation WebUI is the runtime that exposes every knob. Every loader, every quant type, every character card format, all in one Gradio interface. Not for anyone who wants “it just works”, but the right pick for readers who tune their setup.
Where it falls short: First-run setup takes longer than every other runtime on the list.
Pricing: Free, AGPL.
Platforms: Windows, macOS, Linux.
Download: github.com/oobabooga/text-generation-webui
Bottom line: The pick if your workflow depends on switching loaders and quantisations without editing config files.
How to pick the right one
If your priority is fastest first-token time on any hardware, buy in on Ollama. Its ergonomics are still ahead of everything else, and its ecosystem multiplier is real.
If you split time between an Apple Silicon Mac and a Windows Nvidia desktop, LM Studio is the pick. If your target hardware includes AMD Ryzen AI 300, Lemonade is the pick and its new Nvidia support means you don’t have to switch runtimes when you move to a different machine.
Jan is the ChatGPT replacement. GPT4All is the pick for modest hardware. KoboldCpp is the pick for a single-file, USB-portable setup. Text Generation WebUI is the pick if you want to swap loaders per model. llama.cpp is the pick if you want a shell script and nothing else.
Skip proprietary runtimes that only run on one vendor’s hardware unless that vendor is your whole stack.
FAQ
What is the fastest local AI runtime on Apple Silicon?
LM Studio with MLX runs about 30 to 50 percent faster than llama.cpp Metal on the same model. Ollama is closing the gap since its MLX backend landed.
Can I move my Ollama model store to another machine?
Yes. Point OLLAMA_MODELS at a synced folder (iCloud, Syncthing, Dropbox) and the same models appear on both machines. Version-lock the Ollama binary if you cross OS boundaries.
Does AMD Lemonade really run on Nvidia now?
Yes, as of the 2026 release. It is not a full CUDA implementation, but the OpenAI-compatible endpoint works cross-vendor, and Nvidia inference is on par with Ollama’s Nvidia backend for most GGUF quants.
Which local AI runtime uses the least VRAM?
llama.cpp with a Q4_K_M quantisation. KoboldCpp matches it because it wraps the same engine. LM Studio and Ollama use the same math but ship higher-default context that eats VRAM.
Is any local AI runtime available for iOS or Android?
No, none of the eight run on mobile. On Android, look at MLC LLM or Layla. On iOS, look at Private LLM or Enclave.
