text model · Kimi · macOS
Can I run Kimi K2.7 Code on Apple M1 (8GB)?
No. Kimi K2.7 Code needs ~592.1 GB even at Q4_K_M, but Apple M1 (8GB) only has ~5.5 GB usable.
Needs ~592.1 GB even at Q4_K_M, but only ~5.5 GB is usable.
That figure is at a 4k context and moves about ±15% as context length changes.
The gap is about 586.6 GB: Kimi K2.7 Code needs roughly 592.1 GB at Q4_K_M and Apple M1 (8GB) leaves only about 5.5 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
- ~5.5 GB
- Device memory
- 8 GB
Which quant fits
How to run it
On macOS use LM Studio (Polished GUI, ships MLX on Apple Silicon, one-click model downloads.).
- Parameters
- 1000B (MoE, 32B active)
- Q4_K_M size
- 583.71 GB
- Context
- 256k
- Memory
- 8 GB unified
- Usable for weights
- ~5.5 GB
- Power draw
- ~39 W
- Best runtime
- Ollama (llama.cpp Metal backend)
What you can run instead
FAQ
Can Apple M1 (8GB) run Kimi K2.7 Code?
No. Kimi K2.7 Code needs ~592.1 GB even at Q4_K_M, but Apple M1 (8GB) only has ~5.5 GB usable.
How much memory does Kimi K2.7 Code need?
Apple M1 (8GB) 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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A live badge for your model card or README, updated as the data is. [](https://localmodel.run/can-i-run/kimi-k2.7-code/apple-m1-8gb) 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.