Can I Run Ternary Bonsai 2 on Apple M4?

Written by Jakub Rusinowski · Last updated October 15, 2026

Yes, comfortably — Ternary Bonsai 2 27B at PTQ1_0 ternary needs 7.3 GB at 8K context (5.9 GB weights + 1.4 GB KV cache/overhead), leaving ~16.7 GB of the Apple M4's 24 GB usable memory, at ~10 tok/s (est.), with room for up to 256K context.

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Apple Mac mini M4 (16GB)
16 GB unified memory · 120 GB/s
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Apple M4 Specs

VRAM32 GB unified memory
Memory Bandwidth120 GB/s

Ternary Bonsai 2 27B on the Apple M4: VRAM by quantization

QuantVRAM neededFits 32 GB?Max contextWhole PC
PQ2_0 ternary8.5 GB✓ Yes128K—
PTQ1_0 ternary7.3 GB✓ Yes256K—

Assumes an 8K-token context with an f16 KV cache. A longer window needs more; a quantized KV cache needs less. “Max context” is the largest window that still fits in 32 GB of unified memory. Figures are estimates from parameter count, quantization and memory bandwidth — the analyzer lets you tune KV-cache quant and context.

Context costs VRAM too. At PTQ1_0 ternary on the Apple M4: 8K 7.3 GB · 32K 8.9 GB · 128K 15.3 GB. Every window this model publishes still fits 32 GB.

Ternary Bonsai 2 Sizes That Fit the Apple M4 (at 8K context)

Ternary Bonsai 2 27BPTQ1_0 ternary · 7.3 GB at 8K context · ~10 tok/s (est.)
Ternary Bonsai 2 9BPTQ1_0 ternary · 3.1 GB at 8K context · ~28 tok/s (est.)
Ternary Bonsai 2 4BPTQ1_0 ternary · 2 GB at 8K context · ~52 tok/s (est.)
Ternary Bonsai 2 2BPTQ1_0 ternary · 1.3 GB at 8K context · ~99 tok/s (est.)
Won't fit — rent Ternary Bonsai 2 instead

Ternary Bonsai 2 needs ~7 GB but this GPU has 32 GB. Rent a RTX 4090 (24 GB)-class GPU by the hour instead of buying one:

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FAQ

Will Ternary Bonsai 2 run on the Apple M4?

Yes, comfortably — Ternary Bonsai 2 27B at PTQ1_0 ternary needs 7.3 GB at 8K context (5.9 GB weights + 1.4 GB KV cache/overhead), leaving ~16.7 GB of the Apple M4's 24 GB usable memory, at ~10 tok/s (est.), with room for up to 256K context.

Which Ternary Bonsai 2 variant fits best on the Apple M4?

Ternary Bonsai 2 27B at PTQ1_0 ternary quantization needs 7.3 GB at 8K context (5.9 GB weights + 1.4 GB KV cache/overhead), estimated ~10 tokens/sec, up to 256K context.

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