01What it can do
- Specialized in code generation and understanding
- Multimodal (image-text)
- Context extended to 262K tokens
- ~1059B-parameter frontier model
- —Extremely heavy: ~614 GB VRAM in Q4, reserved for multi-GPU servers
- —Low local throughput (~5 tok/s in Q4)
- —“other” license: check the usage terms
04Install
Install Ollama for your OS. Check the model and its quantization before downloading. Start with 4096 tokens of context, then check placement with ollama ps. A command below is not proof that a test was run on your machine.
02Required memory
Approximate GPU VRAM required to run this model, including 4k tokens of context overhead. For a longer context, add ~1 GB per 8k-token increment.
What hardware do you need for Kimi K2.7 Code?
To run Kimi K2.7 Code locally with Q4 quantization, you need about 614 GB of VRAM. An option to compare: BOSGAME M5 128GB / 2TB (Ryzen AI Max+ 395) — this model exceeds this mini-PC's GPU capacity: choose a smaller model or suitable infrastructure.
Why this choice? Our complete guide on BOSGAME M5 128GB / 2TB (Ryzen AI Max+ 395) →
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03Expected speed
Tokens generated per second in Q4_K_M, 4k context. Beyond 20 t/s, reading is comfortable. Below 10 t/s, that's just for testing.