text model · Kimi · macOS
Can I run Kimi K2 Instruct on Apple M5 (16GB)?
No. Kimi K2 Instruct needs ~586.6 GB even at Q4_K_M, but Apple M5 (16GB) only has ~10.5 GB usable.
Needs ~586.6 GB even at Q4_K_M, but only ~10.5 GB is usable.
That figure is at a 4k context and moves about ±15% as context length changes.
The gap is about 576.1 GB: Kimi K2 Instruct needs roughly 586.6 GB at Q4_K_M and Apple M5 (16GB) leaves only about 10.5 GB usable for a model. No single tracked device has enough memory; Kimi K2 Instruct needs a multi-GPU or high-memory rig.
- Q4_K_M needed
- ~586.6 GB
- Usable on device
- ~10.5 GB
- Device memory
- 16 GB
Which quant fits
- Parameters
- 1000B (MoE, 32B active)
- Q4_K_M size
- 578.15 GB
- Q8_0 size
- 1016.12 GB
- Context
- 128k
- Ollama tag
- kimi-k2
- Memory
- 16 GB unified
- Usable for weights
- ~10.5 GB
- Best runtime
- MLX direct / Ollama (MLX backend)
What you can run instead
FAQ
Can Apple M5 (16GB) run Kimi K2 Instruct?
No. Kimi K2 Instruct needs ~586.6 GB even at Q4_K_M, but Apple M5 (16GB) only has ~10.5 GB usable.
How much memory does Kimi K2 Instruct need?
Apple M5 (16GB) does not have enough memory. At Q4_K_M the weights are ~578.15 GB; with KV cache and runtime overhead, budget ~586.6 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 Instruct 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/apple-m5-16gb) 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.