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Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Repair Windows errors before they cause bigger problemsFix Now →You can run Hermes Agent against a model served by Ollama on your own machine. The connection uses Ollama’s local OpenAI-compatible API, but a successful chat is not enough: the model must also support tool calls, and the served context window must meet Hermes’ documented requirement of at least 64,000 tokens for agentic work with tools. The setup below covers Ollama’s guided launcher and a manual configuration, plus the checks that help distinguish a working agent from a slow or misconfigured chat session.
What runs locally—and what may not
In the Ollama setup, Hermes sends model requests to Ollama’s loopback endpoint on your computer. The Hermes guide uses http://localhost:11434/v1; Ollama’s integration guide shows the equivalent loopback address http://127.0.0.1:11434/v1. Neither is a remote server address, and local Ollama does not require an API key. See the Hermes Ollama setup guide and Ollama’s Hermes integration guide.
Local inference does not make every part of a Hermes workflow offline. Optional web browsing, messaging gateways such as Telegram or Discord, and cloud fallback providers can communicate outside your machine. For a workflow intended to stay offline, do not configure cloud fallback providers or network-facing tools and integrations; review the Hermes provider documentation and the tools you enable. The local-model setup alone is not a blanket guarantee that no data leaves the computer.
Choose a model that can act, not just chat
Agentic tasks depend on tool calls: Hermes must be able to ask the model to take actions, such as operating on files or using terminal commands. A model that produces fluent text but lacks tool-call support may answer questions while failing at those tasks. Hermes’ documentation puts it plainly: “Models without tool-call support can only chat; they can’t take actions.” Check current support and test the specific model and workflow you intend to use rather than treating an example model name as a permanent endorsement.
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The Hermes setup guide lists Gemma 4 31B as an example with tool calling, and Gemma 2 27B, Gemma 2 9B, and Llama 3.2 3B as examples without tool calling for the tasks shown there. Those examples can become stale as model catalogs and templates change. Ollama’s integration page currently names Gemma 4 and Qwen 3.6 as local options. Confirm tool behavior, context capacity, and fit in your actual Ollama configuration before settling on a model.
Check hardware and context before downloading
The following are Hermes Agent documentation guidance, accessed October 5, 2026—not compatibility guarantees. Actual fit and speed depend on the model, quantization, available memory, context length, and workload.
| Resource or behavior | Hermes documentation guidance |
|---|---|
| System memory | 8 GB RAM minimum guidance for 3B models; 32+ GB recommended for 27B+ models. |
| Free storage | 5 GB minimum guidance; 30+ GB recommended for multiple models. |
| CPU | 4 cores minimum guidance; 8+ cores recommended. |
| GPU | NVIDIA GPU with 8+ GB VRAM recommended, not required. |
| Context | At least 64,000 tokens for agentic tool workflows, according to Hermes; the guide identifies 2,048 tokens as Ollama’s default context in the documented setup. |
| Illustrative CPU speed | The guide gives about 10 tokens/second for a 9B model on a modern 8-core CPU and about 2–5 tokens/second for a 31B model on CPU; it describes example responses taking 30–120 seconds. These are guide estimates, not reproducible independent benchmarks or guarantees. |
| Partial GPU offload example | The guide says a 31B model on a 12 GB GPU may partially offload about 40 layers; this is an example, not a card recommendation or guaranteed result. |
These figures and the context requirement are in the Hermes local Ollama guide. The practical consequence of the context gap is important: do not assume that downloading a model automatically gives Hermes the context length its tool workflow needs. Configure and verify the context actually served by your runtime. Larger models and multiple downloads also increase memory and disk demands. If the machine starts swapping to disk under memory pressure, the guide recommends using a smaller model or adding memory.
Use Ollama’s guided Hermes launcher
Ollama documents ollama launch hermes as a quick-start path. It can prompt to install Hermes, let you choose a model, connect Hermes to Ollama’s local endpoint, and optionally continue into messaging gateway setup. Prompts and model choices may change, so follow the choices shown by the installed version and verify the selected model against the requirements above. Details are in Ollama’s Hermes integration guide.
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Install and start Ollama, then run
ollama launch hermesin a terminal. -
Answer the installation and model prompts. If you want a strictly local workflow, skip optional messaging setup and avoid cloud fallback providers or external tools.
-
After setup, confirm Hermes is using the intended local model and that the model’s context configuration is sufficient. Run a harmless task that requires a tool action, not only a conversational prompt.
Configure Hermes manually
Use this route if you want to choose the endpoint and model configuration yourself. The sequence follows the Hermes guide; use one loopback URL consistently. The example below uses http://localhost:11434/v1.
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Install and start Ollama. Check that it is available with
ollama --version. You can check the local model-tags endpoint in a browser or with a suitable HTTP client athttp://localhost:11434/api/tags; it should return the models Ollama currently knows about. -
Pull a suitable model. Use
ollama pull MODEL_NAME, replacingMODEL_NAMEwith the current Ollama model identifier you have selected. Choose one that supports tool calls for the actions you need and fits your memory, storage, speed, and context requirements. -
Point Hermes at Ollama. Run
hermes setupand choose a custom endpoint/provider when prompted. Enterhttp://localhost:11434/v1as the base URL, the exact pulled model name, and no API key for local Ollama. -
Or set the configuration directly. In
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Start Hermes and verify an action. Give it a safe, low-impact task that requires a tool, such as listing files in a test directory. Confirm that the action occurred as expected; a normal text answer does not prove tool calling works.
The manual endpoint and configuration steps are documented in the Hermes Ollama guide. Ollama’s integration page uses http://127.0.0.1:11434/v1 instead; it is another loopback spelling, not a different remote service.
Diagnose slow starts and common errors
The first response is silent or very slow
Hermes sends its system prompt and schemas for enabled tools with model requests. On CPU-only or low-VRAM hardware, processing that prompt (prefill) can take a long time before any generated text appears. The Hermes guide says this can mean minutes and describes it as expected behavior, not necessarily a hang. Its suggested adjustments include keeping the model loaded, widening Hermes’ timeout, checking prompt size, and disabling toolsets you do not use.
The model unloads between tasks
The Hermes guide says Ollama unloads models after five minutes idle by default. It describes setting a longer keep-alive when you want to avoid repeated reloads. Check ollama ps to see whether the model is loaded and whether GPU layers were offloaded; consult the current Ollama and Hermes documentation for the exact keep-alive setting supported by your installed versions.
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Hermes reports no endpoint is configured
Set the Ollama base URL in Hermes’ provider configuration. For this manual setup, use http://localhost:11434/v1 and make sure the configured model name matches the one pulled into Ollama.
Responses are unexpectedly slow or the machine swaps
CPU-only use is possible, but large models can be slow. The speed examples in the hardware table are illustrative figures from Hermes documentation, not guarantees for your machine. If memory pressure leads to disk swapping, reduce model size or add memory; also consider whether the chosen context length and enabled tool schemas are appropriate for the task.
When Ollama is not the right local path
Ollama is one supported way to serve a local model to Hermes. Hermes Desktop also offers a managed local-model path using a llama.cpp runtime, which is distinct from the Ollama workflow in this guide. Hermes additionally documents cloud and other provider options; they involve a different operational choice from running inference on your own hardware. Compare options by tool-call reliability, context available in the served configuration, memory and speed on your machine, and whether any part of the workflow sends data to external services. See the Hermes local models guide and provider documentation.
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