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text model · Ornith · macOS

Can I run Ornith 1.0 35B on Apple M4 Pro (48GB)?

Compatibility verdict VRAM threshold engine
Yes, it runs GPU accelerated

Yes. Ornith 1.0 35B runs on Apple M4 Pro (48GB) at Q4_K_M (~23.2 GB of ~32 GB usable).

Needs ~23.2 GB Device usable ~32 GB

Runs at Q4_K_M using ~23.2 GB of ~32 GB usable.

That figure is at a 4k context and moves about ±15% as context length changes. Apple M4 Pro (48GB) leaves ~8.8 GB of headroom.

Q4_K_M needed
~23.2 GB
Usable on device
~32 GB
Device memory
48 GB
Best quant
Q4_K_M
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Which quant fits

Quant ladder vs ~32 GB usable
Q2_K
~16.9 GB
Q3_K_M
~19.3 GB
Q4_K_M
~23.2 GB
Q5_K_M
~27.1 GB
Q6_K
~30.9 GB
Q8_0
~39.2 GB
FP16
~71.2 GB
The line marks Apple M4 Pro (48GB)'s ~32 GB budget; rungs past it are too large.

Run it

Install commands macOS

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

Ollama
$ ollama run ornith:35b
llama.cpp
$ llama-cli -hf deepreinforce-ai/Ornith-1.0-35B-GGUF:Q4_K_M
LM Studio
$ lms get deepreinforce-ai/Ornith-1.0-35B-GGUF
Model Ornith
Parameters
35B (MoE, 3B active)
Q4_K_M size
21 GB
Q8_0 size
37 GB
Context
256k
Ollama tag
ornith:35b
Full Ornith 1.0 35B requirements →
Device macOS
Memory
48 GB unified
Usable for weights
~32 GB
Power draw
~140 W
Best runtime
Ollama (MLX backend) / MLX direct
Best models for Apple M4 Pro (48GB) →

You could also run

Run Ornith 1.0 35B on other hardware

FAQ

Can Apple M4 Pro (48GB) run Ornith 1.0 35B?

Yes. Ornith 1.0 35B runs on Apple M4 Pro (48GB) at Q4_K_M (~23.2 GB of ~32 GB usable).

How much memory does Ornith 1.0 35B need?

Apple M4 Pro (48GB) has room to spare. At Q4_K_M the weights are ~21 GB; with KV cache and runtime overhead, budget ~23.2 GB at a 4k context. It is a Mixture-of-Experts model (35B total / 3B active), so all experts must stay in memory; memory tracks total params, not active params.

What is the best tool to run Ornith 1.0 35B 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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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.