hermes-agent/website/docs/user-guide/local-models.md

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4	Local Models	Run models entirely on your own machine — no account, no API key, nothing leaves your computer.

Local Models

Hermes can run open models entirely on your own machine. It downloads and manages the inference engine (llama.cpp), picks the right build of each model for your hardware, and handles memory so you never configure context sizes, GPU layers, or quantization. You pick a model; Hermes does the rest.

Nothing leaves your computer: no account, no API key, and no network access after a model is downloaded.

Getting started

1. Open Settings → Providers → Local Models (or choose Run models locally during onboarding).
2. Click Install runtime. Hermes downloads the official llama.cpp build for your hardware (a few hundred MB), verifies it, and keeps it updated.
3. Pick a model from the catalog and click Download.
4. Click Use. New chats now run on the local model.

That's the whole flow. The server starts and stops with Hermes, restarts survive app restarts, and switching back to a cloud provider is one click in the model picker.

How Hermes picks what to download

Every model in the catalog is priced against your machine before you download anything. Each row shows:

• Memory fit — green (Fits your GPU: runs entirely in GPU memory), amber (Uses system RAM: works, but slower), or red (Too big for this machine).
• Context — the window the model starts with and the maximum it can grow to.
• The download size of the build selected for your hardware.

Models ship in several quality grades (quantizations). Hermes picks the highest-quality build that runs fully on your GPU; machines with less memory get a more compact build of the same model with the same guarantees. Below 4-bit the quality loss is too severe, so Hermes never offers builds smaller than that — a machine that can't run the 4-bit build spilled to system RAM simply can't run that model.

Models that don't fit stay visible with the reason, so you always know what a hardware upgrade would unlock.

How memory management works

Local models live or die by memory placement, so Hermes manages it end-to-end and exposes no knobs:

• Models start at a context window that fully fits your GPU and grow toward their native maximum as your conversation needs more room. You may see "Context window grown" in the status feed during long sessions — that's the window expanding, not an error.
• Every recommended model gets at least a 64K context window. When a model is larger than your GPU's memory, Hermes deliberately places the overflow in system RAM in the order that hurts least (expert weights first, never the attention cache), trading some speed to protect the context guarantee.
• Conversation compression only kicks in at the model's maximum window — growth always comes first.
• Idle models are unloaded after 15 minutes to free GPU memory; they reload automatically on the next message.

The status bar

Right-click the status bar and enable System resources to see live GPU utilization, GPU memory, and RAM while local models run. The context meter always reflects the window the model is actually running with.

Finding more models

The catalog is a curated starting point, not a boundary. The Find more models section on the same page searches all of Hugging Face:

• Results show download counts and a per-file fit check sized to your machine, so you know before downloading whether a build runs fully on your GPU.
• Anything you download behaves exactly like a catalog model — Hermes reads the model file itself to pick its context window and memory placement. The only difference: community models don't carry our "validated" testing badge.
• Already have a .gguf file on disk? Add model file links it into your library without copying it (the original stays where it is), and it's usable immediately.

Using your own llama-server

If a llama-server is already running on your machine, Hermes detects it and uses it instead of starting its own. Point a custom endpoint at any OpenAI-compatible server for full manual control — the managed runtime is a default, not a requirement. For manual setups (Ollama, MLX, custom builds, headless CLI machines), see Run Hermes Locally with Ollama and Run Local LLMs on Mac.

Configuration

The managed runtime is controlled by the local_runtime section of config.yaml. The desktop UI writes these values for you; they're documented for CLI and headless use:

local_runtime:
  enabled: false     # true = start the managed server with Hermes.
                     # The desktop "Use" button sets this automatically.
  backend: auto      # auto | cuda | metal | vulkan | hip | cpu
  tag: b10362        # pinned llama.cpp release; Hermes updates it with
                     # each release after re-validation

Models and runtime builds live under the Hermes home directory (models/ and runtimes/llamacpp/). Selecting a local model as your main model uses the standard model.provider: llamacpp + model.default settings — the same shape as every other provider.

Requirements and limits

• Windows and Linux: NVIDIA GPU (CUDA) or CPU. macOS: Apple Silicon (Metal). Vulkan builds serve AMD GPUs.
• A GPU with 8 GB+ of memory runs the small catalog models comfortably; 16 GB+ runs the 27–35B models at high quality.
• Model downloads are byte-size checked against the catalog during the transfer; an incomplete download is deleted and reported, never half-used. (Only the runtime engine zips are SHA-256 verified.)
• Deleting a model removes every file it staged, including vision adapters and speculative-decoding companions.