text model · Kimi · macOS

Can I run Kimi K2.7 Code on Apple M3 Pro (18GB)?

Compatibility verdict VRAM threshold engine

No, not enough memory would not load

No. Kimi K2.7 Code needs ~592.1 GB even at Q4_K_M, but Apple M3 Pro (18GB) only has ~12 GB usable.

Needs ~592.1 GB Device usable ~12 GB

Needs ~592.1 GB even at Q4_K_M, but only ~12 GB is usable.

That figure is at a 4k context and moves about ±15% as context length changes.

The gap is about 580.1 GB: Kimi K2.7 Code needs roughly 592.1 GB at Q4_K_M and Apple M3 Pro (18GB) leaves only about 12 GB usable for a model. No single tracked device has enough memory; Kimi K2.7 Code needs a multi-GPU or high-memory rig.

Q4_K_M needed: ~592.1 GB
Usable on device: ~12 GB
Device memory: 18 GB

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Which quant fits

Quant ladder vs ~12 GB usable

Q2_K

~427.2 GB

Q3_K_M

~497.2 GB

Q4_K_M

~592.1 GB

Q5_K_M

~720.9 GB

Q6_K

~828.4 GB

Q8_0

~1070.9 GB

FP16

~2008.4 GB

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

How to run it

On macOS use LM Studio (Polished GUI, ships MLX on Apple Silicon, one-click model downloads.).

Model Kimi

Parameters: 1000B (MoE, 32B active)
Q4_K_M size: 583.71 GB
Context: 256k

Full Kimi K2.7 Code 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) →

What you can run instead

FAQ

Can Apple M3 Pro (18GB) run Kimi K2.7 Code?

No. Kimi K2.7 Code needs ~592.1 GB even at Q4_K_M, but Apple M3 Pro (18GB) only has ~12 GB usable.

How much memory does Kimi K2.7 Code need?

Apple M3 Pro (18GB) does not have enough memory. At Q4_K_M the weights are ~583.71 GB; with KV cache and runtime overhead, budget ~592.1 GB at a 4k context. It is a Mixture-of-Experts model (1000B total / 32B active), so all experts must stay in memory; memory tracks total params, not active params.

What is the best tool to run Kimi K2.7 Code 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

[![Kimi K2.7 Code on Apple M3 Pro (18GB)](https://localmodel.run/badge/kimi-k2.7-code/apple-m3-18gb.svg)](https://localmodel.run/can-i-run/kimi-k2.7-code/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.