Hardware Requirements

Minimum Specs by Model Size

VPS Cost Comparison

Ollama Setup for Hermes Agent

Install Ollama

Pull a Model

Configure Hermes Agent

Model-by-Model Configuration

Llama 4 Maverick — Best Overall

Qwen 3 8B — Best for Budget VPS

Mistral Small — Best Lightweight

DeepSeek R1 Distill 14B — Best for Reasoning

How Hermes Agent Handles Local Tool Calling

Limitations and Tradeoffs

Related Guides

Can I run Hermes Agent completely free with no API costs?

What is the minimum hardware to run Hermes Agent with a local model?

Does Hermes Agent auto-detect Ollama models?

Can I use a GPU to speed up local models with Hermes Agent? The best open-source model for Hermes Agent is Llama 4 Maverick for overall quality, Qwen 3 8B for budget VPS deployments, and Mistral Small for the best balance of size and capability. Hermes Agent auto-detects models installed through Ollama and includes per-model tool call parsers that optimize function calling for each local model. Running open-source models eliminates API costs entirely — your only expense is the hardware or VPS hosting the model. Open-source models for Hermes Agent must handle two things well: general instruction following and structured tool calling. As of April 2026, these are the top open-weight models ranked by agent task performance when run through Ollama. 400B MoE (17B active) Best overall local performance Strong reasoning, multilingual Best quality-to-size ratio Budget VPS, zero-cost agent 109B MoE (17B active) Large context on modest hardware DeepSeek R1 Distill (14B) Reasoning-heavy tasks Google ecosystem compatibility RAM requirements above assume Q4_K_M quantization, which reduces memory usage by 50–75% compared to full-precision weights with minimal quality loss. All listed models are available through ollama pull and work with Hermes Agent immediately after download. Running open-source models locally means your hardware becomes the bottleneck, not your API budget. The primary constraint is RAM — models must fit entirely in memory (system RAM or VRAM) to run at usable speeds. Qwen 3 8B, Llama 3.3 8B Gemma 3 12B, DeepSeek R1 14B distill Mistral Small, Qwen 3 32B Llama 3.3 70B, Qwen 2.5 72B A GPU is not required — Ollama runs on CPU. However, a GPU with sufficient VRAM significantly improves response speed. On CPU-only hardware, expect 2–10 tokens per second for 7-8B models and 0.5–3 tokens per second for 32B models. For a detailed guide on VPS hardware for Hermes Agent, see our self-hosted Hermes Agent guide. Self-hosting on a VPS replaces API costs with hosting costs. As of April 2026, here is what it costs to run different model sizes on popular VPS providers: At $8–$10 per month for a VPS running Qwen 3 8B, you get unlimited agent interactions with zero per-token cost. This breaks even against DeepSeek V4 API usage at roughly $8–$10 per month of moderate use, and saves substantially compared to Claude Sonnet at $20–$80 per month. Ollama is the recommended way to run open-source models with Hermes Agent. Install Ollama, pull a model, and Hermes Agent detects it automatically. Or set it directly in ~/.hermes/config.yaml: No API key is needed — Hermes Agent connects to the local Ollama server on the default port (11434). For full installation steps including Docker deployment, see our Hermes Agent setup guide. Free skills and AI personas for OpenClaw — browse the marketplace. Browse the Marketplace → Each open-source model has different strengths for Hermes Agent workflows. Below are specific recommendations and configuration notes for the top options. Meta's Llama 4 Maverick uses a Mixture-of-Experts architecture with 400B total parameters but only activates 17B per token, keeping resource usage manageable. The 1M token context window matches cloud models like Claude Sonnet and DeepSeek V4, which is critical for Hermes Agent's context-heavy requests. Tool calling quality approaches cloud-level performance. Requires 16+ GB RAM. Best suited for dedicated hardware or a VPS with at least 16 GB RAM. Qwen 3 8B from Alibaba runs on a VPS with just 8 GB RAM and delivers functional tool calling for straightforward agent tasks. It supports 29 languages, making it the best budget option for multilingual Hermes Agent deployments. The 32K context window is a limitation — long agent sessions with many tool calls may truncate earlier context. Mistral Small offers 128K context in a 22B model that fits in 16 GB RAM. Its function calling capabilities are strong relative to its size, and the larger context window means less truncation during extended agent sessions compared to Qwen 3 8B. The distilled version of DeepSeek R1 brings chain-of-thought reasoning to local hardware. At 14B parameters, it fits in 12 GB RAM and handles multi-step reasoning better than other models in its size class. The tradeoff is slower response times due to the reasoning process. Hermes Agent includes per-model tool call parsers that are specifically designed for local models. This is a key advantage over other agent frameworks when running with Ollama. Different models format tool calls differently — Llama uses one XML-like format, Qwen uses another, and Mistral has its own convention. Hermes Agent's parsers handle these differences automatically. According to the official Hermes Agent documentation, the agent detects which model is loaded through Ollama and applies the correct parser. This reduces malformed tool call errors that are common when running local models through generic agent frameworks. For models not yet in the parser registry, Hermes Agent falls back to a generic OpenAI-compatible parser. You can also define custom parsers in the configuration for any model that uses a non-standard tool calling format. Self-hosted open-source models trade API costs for hardware costs and operational complexity. These are honest tradeoffs to consider before switching from a cloud API model. When NOT to self-host: if you need fast interactive responses, if you run complex multi-step agent workflows that require high tool calling reliability, or if you do not want to manage server infrastructure. For cloud API alternatives at low cost, see our DeepSeek models for Hermes Agent guide. Yes. Install Ollama on your local machine or VPS, pull an open-source model like Qwen 3 8B, and configure Hermes Agent to use the Ollama provider. No API key is needed and there are no per-token charges. Your only cost is the hardware or VPS hosting — which starts at roughly $8 per month for a VPS with 8 GB RAM capable of running 7-8B models. The minimum viable setup is 8 GB RAM with a 4-core CPU running a 7-8B parameter model like Qwen 3 8B through Ollama. This handles basic agent tasks at 2–10 tokens per second on CPU. For comfortable performance with a stronger model, 16 GB RAM with Mistral Small or Llama 4 Maverick is recommended. Yes. Hermes Agent queries the local Ollama server on startup and lists all downloaded models as available options. Run hermes model to see the list and select one. The agent also applies per-model tool call parsers automatically based on the detected model, optimizing function calling for each model's format. Llama 4 Maverick has the best tool calling among open-source models for Hermes Agent as of April 2026. It approaches cloud-model quality for structured function calls while supporting a 1M token context window. Mistral Small is the second-best option with reliable function calling at a smaller model size. Qwen 3 8B handles basic tool calling but generates malformed calls more frequently on complex tasks. Yes. Ollama automatically uses GPU acceleration when a compatible NVIDIA, AMD, or Apple Silicon GPU is available. GPU inference is 5–20x faster than CPU for most models. On Apple Silicon Macs, the unified memory architecture means models use the same memory pool as the system, and Ollama leverages the Metal framework for acceleration without separate VRAM requirements. Templates let you quickly answer FAQs or store snippets for re-use. Hide child comments as well For further actions, you may consider blocking this person and/or reporting abuse