text model · Ornith · macOS
Can I run Ornith 1.0 35B on Apple M3 Ultra (256GB)?
Yes. Ornith 1.0 35B runs on Apple M3 Ultra (256GB) at Q4_K_M (~23.2 GB of ~192 GB usable).
Runs at Q4_K_M using ~23.2 GB of ~192 GB usable. You have room for FP16 for higher quality.
That figure is at a 4k context and moves about ±15% as context length changes. Apple M3 Ultra (256GB) leaves ~168.8 GB of headroom, room to step up to FP16 for higher quality.
- Q4_K_M needed
- ~23.2 GB
- Usable on device
- ~192 GB
- Device memory
- 256 GB
- Best quant
- Q4_K_M
Which quant fits
Run it
Pick your tool. All 3 load the same Q4_K_M weights.
ollama run ornith:35b llama-cli -hf deepreinforce-ai/Ornith-1.0-35B-GGUF:Q4_K_M lms get deepreinforce-ai/Ornith-1.0-35B-GGUF - Parameters
- 35B (MoE, 3B active)
- Q4_K_M size
- 21 GB
- Q8_0 size
- 37 GB
- Context
- 256k
- Ollama tag
- ornith:35b
- Memory
- 256 GB unified
- Usable for weights
- ~192 GB
- Power draw
- ~270 W
- Best runtime
- MLX direct / Ollama (MLX backend)
You could also run
Run Ornith 1.0 35B on other hardware
FAQ
Can Apple M3 Ultra (256GB) run Ornith 1.0 35B?
Yes. Ornith 1.0 35B runs on Apple M3 Ultra (256GB) at Q4_K_M (~23.2 GB of ~192 GB usable).
How much memory does Ornith 1.0 35B need?
Apple M3 Ultra (256GB) 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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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.