Kimi K2.7 Code — VRAM, speed & local setup
Written by Jakub Rusinowski · Last updated
Moonshot's coding-specialised build, layered directly on top of K2.6: 1 trillion total parameters with 32B active per token across 384 experts (8 selected plus 1 shared), 61 layers, MLA attention, and a 400M-parameter MoonViT encoder for image and video input. Reported ~30% fewer reasoning tokens than K2.6 at higher coding scores. Weights ship in native INT4 via quantization-aware training. 256K context. At ~340 GB even in INT4 this is a datacenter deployment, not a desktop one — most people reach it through the API.
Kimi K2.7 Code needs about 605 GB of VRAM at INT4 (native) — quantized weights plus framework overhead, before any KV cache. On Apple Silicon that figure comes out of unified memory.
VRAM and speed by quantization
Quoted against NVIDIA RTX 4090 (24 GB). Weights plus overhead only: this model's architecture is unpublished, so no KV cache is added.
| Quant | Memory | VRAM | Speed (est.) | Fit |
|---|---|---|---|---|
| Q2_K 2.63 bpw | 329.6 GB | — | Too big | |
| Q3_K_M 3.41 bpw | 427.1 GB | — | Too big | |
| Q4_K_M 4.83 bpw | 604.6 GB | — | Too big | |
| Q5_K_M 5.67 bpw | 709.6 GB | — | Too big | |
| Q6_K 6.56 bpw | 820.8 GB | — | Too big | |
| Q8_0 8.50 bpw | 1063.3 GB | — | Too big | |
| F16 16.00 bpw | 2000.8 GB | — | Too big |
Black marker = usable memory on the NVIDIA RTX 4090 (24 GB). Estimates from the memory-bandwidth roofline on the methodology page. Kimi K2.7 Code VRAM calculator →
Get Kimi K2.7 Code running
Kimi K2.7 Code is not practical on a single consumer GPU: it needs about 604.6 GB at Q4_K_M, more than any single device in our catalogue holds with headroom. How multi-GPU inference works, or rent a multi-GPU server by the hour:
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How to run Kimi K2.7 Code
Install Ollama, then run:
ollama run kimi-k2Specifications
Preview — The model is released, but these specs are thin or rest on a single source. Individual fields may be wrong.
Preview — The model is released, but these specs are thin or rest on a single source. Individual fields may be wrong.
- Parameters
- 1 Trillion (32B active)
- Context window
- 262,144
- Architecture
- MoE (384 experts, MLA) + MoonViT vision
- Provider
- Moonshot AI
- Licence
- Modified MIT (attribution above ~100M MAU / ~$20M monthly revenue)
- Practical deployment
- Private cloud / datacenter cluster
- Specified at
- INT4 (native)
- System RAM
- 768 GB
- Record updated
- 2026-08-15
Commercial use permitted. No usage restrictions beyond attribution.
Quality and use cases
Scores as published by the model’s authors or an independent evaluator — quality, not throughput, and not measured by us.
Other Kimi K2.5 / K2.6 / K2.7 sizes
Kimi K2.7 Code — frequently asked questions
How much VRAM does Kimi K2.7 Code need?
About 605 GB at INT4 (native) — quantized weights plus framework overhead, before any KV cache. The cache grows with context length and is added on top; the table above folds it in. Apple Silicon counts unified memory toward the same figure.
Does Kimi K2.7 Code run on an RTX 4090 (24 GB)?
No. Kimi K2.7 Code needs about 605 GB at INT4 (native), more than a single RTX 4090's 24 GB. It needs a larger card, several GPUs, or Apple Silicon with enough unified memory — or it runs with part of the weights offloaded to system RAM, which is much slower.
How do I run Kimi K2.7 Code locally?
Install Ollama and run `ollama run kimi-k2`. That pulls the weights and starts a local OpenAI-compatible endpoint; after the download nothing leaves the machine.
What other sizes does Kimi K2.5 / K2.6 / K2.7 come in?
Kimi K2.5 (605 GB), Kimi K2.6 (605 GB), Kimi K2.5 1T (32B Active) (605 GB), Kimi K2.7 Code (605 GB). Every size shares the family's training and licence; the larger ones score higher and need proportionally more memory.