--- sidebar_position: 4 title: Local Models description: 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. - **Memory fit includes the launch configuration**, not just the model file: context state, runtime buffers, the vision projector, and MTP buffers all count. For multi-token prediction (MTP), Hermes uses smaller batches when larger batches would spill at the same context window. MTP stays enabled. The same calculation runs when a grown window is restored after restart. - **Conversation compression follows a growth check.** If a larger window cannot fit, generation is too slow, or the native maximum is reached, Hermes compresses instead of claiming a window the server did not receive. - 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. You can enter the server root (for example `http://127.0.0.1:8080`) or the full `/v1` URL: the endpoint test tries both and saves the variant that actually served `/models`, so chat requests go to the same prefix the model list came from. For manual setups (Ollama, MLX, custom builds, headless CLI machines), see [Run Hermes Locally with Ollama](../guides/local-ollama-setup.md) and [Run Local LLMs on Mac](../guides/local-llm-on-mac.md). ## 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: ```yaml 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 detect_ports: [8081] # extra ports to probe for a llama-server you run # yourself (the default probe is :8080 only) ``` Running `llama-server` yourself on a fixed port works with the same `model.provider: llamacpp` selection — either list the port in `local_runtime.detect_ports`, or define the endpoint explicitly under `providers:` (an explicit entry wins over server detection): ```yaml providers: llamacpp: base_url: http://127.0.0.1:8081/v1 model: my-model ``` The `/model` → **Local** picker row and `provider: llamacpp` resolve to that server; with no server reachable the error names the local runtime ("the local model server isn't running") instead of an unknown-provider or missing-API-key message. 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.