Aris Server

Run open-weight models on your Mac and use them from Claude Code, Aris Code, or anything else that speaks the Anthropic Messages API.

uv tool install git+https://github.com/kennysarnoski-jarvis/aris-server
aris-server serve --model mlx-community/Qwen3.5-9B-MLX-8bit

Then point Claude Code at it:

ANTHROPIC_BASE_URL=http://127.0.0.1:8484 ANTHROPIC_AUTH_TOKEN=local claude

Claude Code assumes a 200k-token window and compacts its conversation about 33k tokens short of the window it's given. A local model's window is whatever fits beside its weights, so tell Claude Code what it is: the server logs it at startup, and /v1/models lists it as context_window. With CLAUDE_CODE_MAX_CONTEXT_TOKENS set to it, the conversation compacts before the server's limit:

CLAUDE_CODE_MAX_CONTEXT_TOKENS=124928 ANTHROPIC_BASE_URL=http://127.0.0.1:8484 ANTHROPIC_AUTH_TOKEN=local claude

Aris 3.8-27B

Aris 3.8-27B is built in as aris-3.8-27b. It's Aris's coding-agent fine-tune of Arete-27B by Nous Research, which is built on Qwen3.8-27B by the Qwen team at Alibaba Cloud. pull picks the best build this Mac can hold, before anything big downloads, and downloads it:

aris-server pull aris-3.8-27b
aris-server serve --model aris-3.8-27b

The 8-bit build takes 27 GB, so it needs a Mac with 48 GB of memory or more, where it has about 125k tokens of context. Its model card has the details and evals.

Why not Ollama?

Aris Server does one job: serve a local model to coding agents properly.

• Native /v1/messages. Thinking, tool calls and text stream as real Anthropic events from the first byte, with pings while a long prompt is read or a long tool call is written, so clients never time out on silence, and a client that hangs up stops its generation.
• Effort that works. Claude Code's effort setting reaches models that have trained effort levels.
• A think budget. Reasoning models that overthink get their thinking closed at the end of the current line once they pass the budget for the requested effort, so they answer instead of burning every token deliberating.
• Claude Code's tool search. Deferred tools stay out of the prompt until the agent loads them.
• Fast on Apple Silicon. Built on MLX and mlx-lm: continuous batching and a prompt cache that keeps the system prompt warm across conversations.

Models

Any MLX model folder or Hugging Face repo that mlx-lm can load works by name. To give a model a short name, effort mapping or think budgets, add it to ~/.aris-server/models.toml:

[models."arete:27b"]
path = "~/models/arete-27b-mlx-8bit"
context = 131072
effort_kwarg = "reasoning_effort"
effort_map = { low = "low", medium = "medium", high = "medium", xhigh = "xhigh", max = "xhigh" }
think_budget = { low = 1024, medium = 4096, high = 8192, xhigh = 16384, max = 0 }

aris-server serve --model arete:27b

A fine-tuned model can ship as a LoRA adapter on top of its base: add adapter = "~/adapters/expert1" (a folder with adapter_config.json and adapters.safetensors, as mlx-lm's trainer writes them) to a model entry, or pass --adapter. Switching between a base model and its adapter is a restart.

kv_bits = 8 in a model entry (or --kv-bits 8) keeps the KV cache at 8 bits: about twice the context in the same memory. --kv-bits 16 turns it back to full precision.

To serve the best weights each Mac can hold, give path a list, best first. Before loading, the server works out how much context each would leave on this Mac and loads the first that leaves min_context tokens (65536 unless set), or else the one that leaves the most. Hugging Face repos are sized from the Hub before anything downloads, so a Mac only ever fetches weights it can run:

[models."arete:27b"]
path = ["~/models/arete-27b-mlx-8bit", "~/models/arete-27b-mlx-4bit"]
kv_bits = 8

With the 8-bit KV cache, Arete-27B's 8-bit weights leave about 124k tokens of context on a 48 GB Mac and more than the model's whole context on a 64 GB one. A 32 GB Mac can't hold them and gets the 4-bit weights, with about 63k.

Run it as a service

aris-server service install -- --model aris-3.8-27b
aris-server service status
aris-server service uninstall

aris-server pull --json reports each step of a download as a JSON line, for apps that set the server up for their users. aris-server models lists configured and built-in models.

The service starts at login and restarts the server within seconds of any exit (it exits on purpose if the GPU runs out of memory). Saved prompt caches mean the conversation picks up where it left off; they take up to 20 GB of disk, all models together (--disk-cache-gb). Logs go to ~/.aris-server/server.log.

Status

Early. macOS on Apple Silicon only. See docs/design.md for what's in and out.

License

Apache-2.0