Overview
A Mac mini AI agent is an agent program such as OpenClaw or Hermes Agent talking to a model served from the same Mac, and the number that decides which Mac mini you need is context: both agents’ docs ask local models for at least 64K tokens, and the memory for that context is added on top of the model weights. Ollama’s docs list gemma4 at about 16 GB and qwen3.5 at about 11 GB for OpenClaw, and the Hermes docs recommend a 9B Qwen3.5 build for Macs. Speed is unknown on this site, and no tokens-per-second figure is given; measure your own with measure-tokens-per-second-mac. All sources were read on 2026-10-13.
What is a local AI agent on a Mac?
It is a gateway or agent process plus a model server, both on your Mac. OpenClaw describes itself as “an open-source AI assistant that runs on your own hardware”, an MIT-licensed Gateway that connects chat apps (Discord, Slack, Telegram, WhatsApp, iMessage and others) to an agent. Its install tabs cover macOS, Linux and Windows (WSL2 or PowerShell), and it asks for Node 26 (recommended), Node 24.16+ or Node 26.1+. Hermes Agent is from Nous Research; the Ollama page lists Telegram, Discord, Slack, WhatsApp, Signal and Email and says it ships with more than 70 skills.
The model part is the same as any local LLM: weights, KV cache and overhead, sized on how-much-ram-for-local-llm. What is different is that an agent sends a long prompt every turn. OpenClaw’s local models page says: “A model that loads or answers a short prompt may still fail an agent turn.”
How do I set up OpenClaw on a Mac mini?
The shortest documented path is Ollama. Ollama’s OpenClaw page gives ollama launch openclaw, which launches OpenClaw through Ollama; --config changes the model without starting the gateway, and --model <name> picks one directly. Install Ollama first (ollama-on-mac).
OpenClaw’s own recommended path is different: run openclaw onboard and choose “Managed local server” (the llama.cpp plugin), which shows host, model, download size and backend before downloading. LM Studio and Ollama are listed as alternatives. The docs say memory needs depend on weights, context, runtime and other workloads, and that the curated recipes use a 64K context, with the smallest having an 8 GiB host-memory floor. They add that those floors “do not guarantee fit or speed.”
The OpenClaw docs also warn that “Local models do not provide hosted providers’ safety filters” and advise keeping agents narrow with compaction on. Treat any agent with tool access as able to act on your Mac.
How do I run Hermes Agent on a Mac mini?
Run ollama launch hermes, per Ollama’s Hermes page; it handles installation, model selection and onboarding. Later, hermes model changes the model. For a manual connection, choose “Custom endpoint” and set the base URL to http://127.0.0.1:11434/v1, leaving the API key blank.
The Hermes guide for local LLMs on Mac uses llama.cpp (brew install llama.cpp) or omlx (MLX) instead. Its example server command is llama-server -m ~/models/Qwen3.5-9B-Q4_K_M.gguf -ngl 99 -c 131072 -np 1 -fa on --cache-type-k q4_0 --cache-type-v q4_0 --host 0.0.0.0; --host 0.0.0.0 listens on all interfaces, and the guide says to use 127.0.0.1 if you do not need network access. It notes that Hermes’ minimum context is 64K and that -np 1 avoids splitting memory across parallel slots. It also warns that slow first turns are expected on slower hardware because Hermes resends its system prompt and tool schemas on each call.
What is the best local LLM for OpenClaw or Hermes?
There is no benchmarked winner here, because this site publishes no agent-quality or speed measurements. What the docs list:
| Agent docs | Local model named | Memory the doc states | Wording |
|---|---|---|---|
| Ollama, OpenClaw | gemma4 | about 16 GB | ”VRAM” |
| Ollama, OpenClaw | qwen3.5 | about 11 GB | ”VRAM” |
| Ollama, Hermes | gemma4 | about 16 GB | ”VRAM” |
| Ollama, Hermes | qwen3.6 | about 24 GB | ”VRAM” |
| Hermes Mac guide | Qwen3.5-9B Q4_K_M | about 10 GB RAM at 128K with quantized KV cache | Mac RAM |
Those are the docs’ own figures; the Ollama pages say VRAM, which on a Mac means unified memory (unified-memory-vs-vram). OpenClaw’s page tells you to test real tasks before making a model the default, because short text checks do not prove multi-step reliability. OpenClaw’s docs also say to avoid heavily quantized variants in LM Studio. More on the family is on qwen-on-mac.
How much memory does an agent’s context need?
Check it with the formulas on methodology. Taking the model the Hermes guide recommends, Qwen3.5-9B (card: 9B parameters, Apache-2.0, 262,144 native context), the config.json text settings give 32 layers, 4 KV heads and head dimension 256. Only every fourth layer uses full attention (8 layers); the other 24 use linear attention.
- Weights at 4.0 bits:
9e9 × 4 / 8 / 1e9= 4.5 GB. (The card’s Safetensors metadata says 10B params, which would be 5.0 GB.) - KV cache, counting the 8 full-attention layers only:
2 × 8 × 4 × 256 × context × 2 bytes / 1e9.- At 65,536 tokens: 2.147 GB.
- At 131,072 tokens: 4.295 GB.
- Overhead: 2 GB, this site’s labelled assumption.
| Context | Weights | KV cache | Overhead | Total |
|---|---|---|---|---|
| 65,536 | 4.5 GB | 2.147 GB | 2 GB | 8.647 GB |
| 131,072 | 4.5 GB | 4.295 GB | 2 GB | 10.795 GB |
These totals leave out the fixed-size state of the linear-attention layers, which this page does not compute (unknown), and use 2 bytes per KV value, so a quantized KV cache would be smaller. Using the 8-layer count instead of all 32 is this page’s reading of the config; the site’s general formula assumes every layer caches. The Hermes guide’s own figure for the same model, about 10 GB RAM at 128K, is the vendor’s number and sits close to the 10.795 GB here. The macOS reserve is unknown, so a total near installed memory is unconfirmed. Longer-context trade-offs are on long-context-on-mac.
Which Mac mini should I buy for an AI agent?
Pick by the model’s total plus whatever else runs on the Mac. Apple’s Mac mini specs list these options:
| Chip | Unified memory | Bandwidth (Apple) |
|---|---|---|
| M6 | 16 GB base; 24 or 32 GB configurable | 153 GB/s (170 GB/s at 24 or 32 GB) |
| M6 (other model) | 24 GB base; 32 GB configurable | 170 GB/s |
| M5 Pro | 24 GB base; 48 or 64 GB configurable | 307 GB/s |
Against the numbers above: 16 GB is above the 9B totals of 8.647 and 10.795 GB with 5.2 GB and 7.4 GB left (16 - 10.795 = 5.205; 16 - 8.647 = 7.353), before the unknown macOS reserve and before the agent’s own processes. gemma4 at about 16 GB per Ollama’s docs would not leave room on a 16 GB Mac; 24 GB is the smallest option above it, and qwen3.6 at about 24 GB per Ollama needs more than 24 GB. Hermes’ guide says 27B and 35B models need 32 GB+ of unified memory, and that on 32 GB or more you can run larger models or multiple parallel slots. Memory and bandwidth by generation are on mac-mini-llm and mac-mini-m6-llm; always-on setups are on mac-llm-server. Prices are unknown here; use Apple’s configurator.
OpenClaw or Hermes Agent: which is better on a Mac?
The docs do not rank them, and this site has not benchmarked either. What they state:
| OpenClaw | Hermes Agent | |
|---|---|---|
| Local model floor | 64k context (Ollama doc); 64K recipes | 64K minimum context |
| One-line Ollama setup | ollama launch openclaw | ollama launch hermes |
| Non-Ollama local path | llama.cpp managed server, LM Studio | llama.cpp, omlx (MLX) |
| Chat channels (docs) | Discord, Slack, Telegram, WhatsApp, iMessage and more | Telegram, Discord, Slack, WhatsApp, Signal, Email |
| Licence | MIT | unknown (not on pages read) |
Choose on channels and workflow, not on a speed claim. For agents that write code, see local-coding-llm-on-mac. The Windows and NVIDIA side of OpenClaw memory is on the sister site, VRAM requirements for local models.
Questions
What is the best local LLM for OpenClaw?
The sources name gemma4 (about 16 GB) and qwen3.5 (about 11 GB) in Ollama’s OpenClaw docs. None is benchmarked here; the model must support at least 64k context, and OpenClaw advises testing real tasks before choosing a default.
How do I set up OpenClaw on a Mac mini?
Install Ollama, then run ollama launch openclaw; it launches OpenClaw through Ollama (Ollama’s page says it installs OpenClaw via npm if missing and gives no Node version; OpenClaw’s own docs ask for Node 24.16+ or 26). Alternatively run openclaw onboard and choose “Managed local server”.
Can a Mac mini run Hermes Agent?
Yes, if the model fits. Run ollama launch hermes, or follow the Hermes Mac guide with llama.cpp. Its Qwen3.5-9B Q4_K_M example is stated at about 10 GB RAM at 128K context.
OpenClaw vs Hermes Agent: which should I use?
The docs do not rank them. Both ask for at least 64K context for local models and both offer one-line Ollama setup; compare the chat channels you need in the table above.
Which Mac mini do I need for an AI agent?
Add the model weights, the KV cache at 64K or more context and 2 GB overhead. For a 9B Qwen3.5 at 4 bits that is 8.647 GB at 64K, so 16 GB is a floor and 24 GB gives headroom; the macOS reserve is unknown.
How fast is a local agent on a Mac mini?
Unknown. No primary source read gives a figure for a specific Mac, and this site publishes none. Hermes’ docs say slow first turns are expected on slower hardware; measure yours with measure-tokens-per-second-mac.
Sources read on 2026-10-13
- OpenClaw docs index: what it is, MIT, Node versions, channels.
- OpenClaw local models: managed local server, 64K recipes, 8 GiB floor, “do not guarantee fit or speed”, safety warning.
- Ollama, OpenClaw: setup commands, 64k context,
gemma4about 16 GB,qwen3.5about 11 GB. - Ollama, Hermes: setup commands,
gemma4about 16 GB,qwen3.6about 24 GB, channels, 70+ skills. - Hermes local LLM on Mac: memory guidance, Qwen3.5-9B, llama.cpp command, 64K minimum.
- Qwen3.5-9B card and config.json: 9B parameters, Apache-2.0, layers, KV heads, head dimension, layer types.
- Apple Mac mini specs: memory options and bandwidth.
- methodology: weight and KV formulas, 2 GB overhead assumption.