Yes, just — 3.1 GB spare

Ornith 1.5 35B-A3B at Q4_K_M fits your M2 Pro · 32 GB entirely in unified memory at 8K context, at an estimated 28 tokens per second. Past 164K the KV cache pushes it over — quantise the cache to q8_0, or step down a quantisation, to go longer.

Fully in unified memory 8K context Q4_K_M · 20.2 GB MIT Released 19 Aug 2026 New this week

A reasoning-first MIT build on the Qwen3.6 35B-A3B architecture (thinks before every answer). Same VRAM as its base.

What hardware do I need for Ornith 1.5 35B-A3B? →

The VRAM budget

weights 20.2 GB

Weights 20.2 GB KV cache @ 8K 0.16 GB Runtime overhead 0.6 GB Free 3.1 GB of 24.0 GB

Quantisation ladder

Quant	Weights	Total @ 8K	Max context	Tok/s	Quality	Fit
Q8_0	35.5 GB	36.3 GB	—	~16	−0.1% ppl	12.3 GB over
Q6_K	27.4 GB	28.2 GB	—	~21	−0.4% ppl	4.2 GB over
Q5_K_M	23.7 GB	24.5 GB	—	~24	−0.8% ppl	0.5 GB over
Q4_K_M	20.2 GB	20.9 GB	164K	28	−1.9% ppl	Recommended
Q3_K_M	16.3 GB	17.1 GB	256K	35	−5.4% ppl	Long context
Q2_K	14.0 GB	14.8 GB	256K	41	−15% ppl	Long context

Quality is the published perplexity delta against f16 weights. Max context assumes an f16 KV cache; q8_0 roughly doubles it. Only 10 of its 40 blocks keep a per-token KV cache; the rest are linear-attention, Mamba or convolution blocks with a fixed-size state.

How to run it

Ollama llama.cpp LM Studio MLX

terminal

$ pip install mlx-lm
$ mlx_lm.generate --model mlx-community/Ornith-1.5-35B-A3B-4bit \
    --max-tokens 512 --prompt "Hello"

Apple's own array framework. The fastest path on Apple Silicon. More on MLX.

01Download is 20.2 GB. Keep it on an SSD — a first load off a spinning disk takes minutes.

02macOS caps what the GPU may wire down at about 24.0 GB of 32 GB. Raising it with iogpu.wired_limit_mb is possible, and risky.

03Only 3.1 GB is spare, so a long system prompt can still push it over. Its real ceiling here is 164K context.

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