Models and hardware¶

Jarvis Automation runs its models on your own machine: a language model that understands what you say and calls the right tool, plus speech-to-text, a voice, embeddings and speaker recognition. This page lists the models Jarvis can download for you and which language model to pick for your hardware.

Jarvis Automation is a pre-release (release candidate rc18). The catalog below is the one rc18's server ships; it can change between release candidates.

Which language model to pick¶

The language model is the choice that matters: it decides how often Jarvis picks the right action and how long you wait for an answer. The others have sensible defaults.

Your machine	Pick	Why
Mac with 16 GB	Qwen 3 4B (Q4_K_M)	98.8 % right, about 1.2 s per warm turn.
Mac with 24 GB or more	Qwen 3.5 9B (Q4_K_M, vision)	Headroom for harder requests; reads images.
Mac with 32 GB or more	Qwen 3.5 9B, or try Qwen 3.5 27B or 35B-A3B (MLX 4-bit)	Larger, text-only, not benchmarked yet.
PC or Linux box with a GPU	The largest model whose badge says Fits	Qwen 3.8 27B needs a 24 GB card.
No usable GPU (CPU only)	Qwen 3 4B (Q4_K_M)	The smallest; still slow on a CPU.

The 16 GB figures were measured by the project's author on a Mac mini M4 (16 GB) on 2026-10-21: 98.8 % on the 80-utterance benchmark, about 1.2 s per warm turn and about 15 s for the first turn after a restart. On a CPU even the 4B is slow, so a language model on another machine may be worth it. Benchmarks has all the numbers and how they were measured.

What the setup wizard suggests¶

The setup wizard (http://<host>:7710/setup) and the Models page of the admin estimate, for each model, whether it fits the hardware Jarvis detected, and show it as a badge:

Badge	Meaning
Fits	Fits one GPU (or a Mac's memory) at its default context.
Tight	Fits, but within 10 % of the card's memory.
Needs 2+ GPUs	Only fits split across several cards.
Runs on CPU	No usable GPU, so it runs in system RAM.
Too big	Does not fit the card, or the RAM on the CPU, or the machine has less memory than the model asks for.
Built in	Runs inside the server itself (the voice and speaker recognition).

The suggestion is the largest model that fits, which is not always the fastest. On a 16 GB Mac, pick Qwen 3 4B even if a larger model fits. You can change the model on the Models page at any time.

The catalog¶

Sizes are the download sizes. A model needs that much memory plus room for its context, which the fit badge accounts for.

Language models¶

Jarvis uses a language model for two jobs: live (answering you) and background (work nobody is waiting on, such as memory). Both use the same model unless you set them apart. The MLX models run on MLX (Apple Silicon only), the others on llama.cpp; the default context is 16,384 tokens, 32,768 for the Qwen 3.5 9B models.

Model	Download	Notes
Qwen 3 4B (Q4_K_M)	2.5 GB	The pick for 16 GB Macs and CPU-only machines.
Qwen 3 8B (Q4_K_M)	5.0 GB
Qwen 3.5 9B (Q4_K_M, vision)	5.7 GB + 0.9 GB projector	Reads images; the projector downloads with it.
Qwen 3 14B (Q4_K_M)	9.0 GB
Qwen 3.8 27B (UD-Q4_K_M, vision)	16.5 GB + 0.9 GB projector	Needs a 24 GB card. For long contexts, give it a second card or use a q8_0 KV cache.
Qwen 3.5 9B (MLX 4-bit, Apple Silicon)	5.1 GB	Text-only. Not suggested: it called a tool on 88.8 % of the tool benchmark, against 100 % for the 9B GGUF.
Qwen 3.5 27B (MLX 4-bit, Apple Silicon)	15.2 GB	Text-only. Needs a Mac with 32 GB or more.
Qwen 3.5 35B-A3B (MLX 4-bit, Apple Silicon)	19.7 GB	Text-only. Needs a Mac with 32 GB or more.

Speech, voice and the rest¶

Model	Download	Used for
Whisper large-v3-turbo	1.6 GB	Speech-to-text. Suggested on a GPU when it fits next to the language model.
Whisper large-v3-turbo (Q5_0)	574 MB	Speech-to-text. The same model, quantized.
Whisper small.en	488 MB	Speech-to-text. Suggested otherwise.
Whisper base.en	148 MB	Speech-to-text. Suggested on a CPU with under 8 GB of RAM.
Kokoro v1.0 multi-language	350 MB	Jarvis's voice. Default voice bm_george (British English) at 1.25× speed. Built in.
all-MiniLM-L6-v2 embeddings	46 MB	Memory search.
3D-Speaker ERes2Net	26 MB	Speaker recognition. Built in.

Engines¶

An engine is the program that runs a language model. Jarvis downloads and starts it for you.

Engine	Runs	Status
llama.cpp	GGUF models, on macOS, Windows and Linux; on the GPU (Metal, CUDA, ROCm or Vulkan) or the CPU	Ready
MLX (mlx-lm)	MLX models, on Apple Silicon only	Ready
vLLM	Hugging Face models on NVIDIA GPUs	In progress
Transformers	Hugging Face models	In progress

vLLM and Transformers appear in the admin, but they are in progress: use llama.cpp or MLX for now.