text model · Ornith · macOS

Can I run Ornith 1.0 9B on Apple M3 Pro (18GB)?

Compatibility verdict VRAM threshold engine

Yes, it runs GPU accelerated ~14 tok/s est.

Yes. Ornith 1.0 9B runs on Apple M3 Pro (18GB) at Q4_K_M (~7.1 GB of ~12 GB usable).

Needs ~7.1 GB Device usable ~12 GB

Runs at Q4_K_M using ~7.1 GB of ~12 GB usable. You have room for Q8_0 for higher quality.

That figure is at a 4k context and moves about ±15% as context length changes. Apple M3 Pro (18GB) leaves ~4.9 GB of headroom, room to step up to Q8_0 for higher quality.

Q4_K_M needed: ~7.1 GB
Usable on device: ~12 GB
Device memory: 18 GB
Best quant: Q4_K_M

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Which quant fits

Quant ladder vs ~12 GB usable

Q2_K

~5.3 GB

Q3_K_M

~5.9 GB

Q4_K_M

~7.1 GB

Q5_K_M

~7.9 GB

Q6_K

~8.9 GB

Q8_0

~11 GB

FP16

~19.5 GB

The line marks Apple M3 Pro (18GB)'s ~12 GB budget; rungs past it are too large.

Running cost · estimate

Power & economics est.

Power draw: ~70 W
Electricity / 1M tokens: ~$0.21
Pays for itself after: ~4,479M tok

At ~$0.15/kWh and the estimated ~14 tok/s, a million generated tokens costs about $0.21 in electricity. Versus a hosted API at ~$0.5 per million tokens, the ~$1,299 Apple M3 Pro (18GB) pays for itself after roughly 4,479 million tokens, so local hardware is mostly a fixed cost, not a per-token one. TDP is the peak draw, so this is an upper bound. Assumptions.

Run it

Install commands macOS

Pick your tool. All 3 load the same Q4_K_M weights.

Ollama

$ ollama run ornith:9b

llama.cpp

$ llama-cli -hf deepreinforce-ai/Ornith-1.0-9B-GGUF:Q4_K_M

LM Studio

$ lms get deepreinforce-ai/Ornith-1.0-9B-GGUF

Model Ornith

Parameters: 9B
Q4_K_M size: 5.6 GB
Q8_0 size: 9.5 GB
Context: 256k
Ollama tag: ornith:9b

Full Ornith 1.0 9B requirements →

Device macOS

Memory: 18 GB unified
Usable for weights: ~12 GB
Power draw: ~70 W
Best runtime: Ollama (llama.cpp Metal backend) / MLX

Best models for Apple M3 Pro (18GB) →

You could also run

Run Ornith 1.0 9B on other hardware

Nvidia GeForce RTX 3060 (12GB)Nvidia GeForce RTX 4070 (12GB)Samsung Galaxy S24 Ultra Generic Android Phone (12GB RAM)iPhone 17 Pro iPhone Air

FAQ

Can Apple M3 Pro (18GB) run Ornith 1.0 9B?

Yes. Ornith 1.0 9B runs on Apple M3 Pro (18GB) at Q4_K_M (~7.1 GB of ~12 GB usable).

How much memory does Ornith 1.0 9B need?

Apple M3 Pro (18GB) has room to spare. At Q4_K_M the weights are ~5.6 GB; with KV cache and runtime overhead, budget ~7.1 GB at a 4k context.

What is the best tool to run Ornith 1.0 9B on macOS?

LM Studio for a simple setup; mlx-lm for the most speed. vLLM is NOT a Mac tool, it is a CUDA/Linux serving engine. Unified memory is not a fixed VRAM slice; ~70% is usable for weights.

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Markdown

[![Ornith 1.0 9B on Apple M3 Pro (18GB)](https://localmodel.run/badge/ornith-1.0-9b/apple-m3-18gb.svg)](https://localmodel.run/can-i-run/ornith-1.0-9b/apple-m3-18gb)

Sources

Weights are measured from GGUF files; KV cache and overhead are computed, so totals can vary ~15% with context and runtime. Any tok/s is a bandwidth estimate. See methodology.