Kimi K2.7 Code
Kimi K2.7 code-specialized variant. Strong on long-horizon coding agent workflows and multi-file refactors.
Best for Kimi K2.7 code-specialized variant (June 2026)
Sizes MoE
Context 128K
License Modified MIT
Min VRAM (default size, Q4) 256 GB
Rec VRAM 512 GB
What is Kimi K2.7 Code?
Kimi K2.7 Code is Moonshot AI's MoE open-weights language model. Released in 2026-06, it ships in 1 size (MoE) and is licensed Modified MIT. The most popular use case is Kimi K2.7 code-specialized variant (June 2026).
VRAM and hardware
The MoE size fits in 256 GB of VRAM.
| Size | Min VRAM (Q4_K_M) | Recommended VRAM | Tokens/sec on 3090 |
|---|---|---|---|
| MoE | 256 GB | 384 GB | ~10 tok/s |
How to run Kimi K2.7 Code locally
Option 1: Ollama (simplest)
ollama pull kimi-k2-7-code
ollama run kimi-k2-7-code Option 2: Mullama (production)
mullama pull kimi-k2-7-code
mullama run kimi-k2-7-code Option 3: llama.cpp (CLI)
# Download a GGUF from Hugging Face (search "kimi-k2-7-code gguf")
./llama-cli -m kimi-k2-7-code.Q4_K_M.gguf -p "Hello, AI!" Option 4: Python with Mullama or llama-cpp-python
from mullama import Model, Context
model = Model.load("kimi-k2-7-code.Q4_K_M.gguf", n_gpu_layers=99)
ctx = Context(model, n_ctx=4096)
print(ctx.generate("Hello, AI!", 256)) What you can build with Kimi K2.7 Code
- coding agents
- multi-file refactoring
- long-horizon code tasks
Hardware it fits on
- Apple Silicon (8GB): MoE in MLX
- Apple Silicon (16GB): MoE in MLX
- Apple Silicon (32GB): MoE in MLX or GGUF
- Apple Silicon (64GB+): MoE in MLX
- RTX 3090 (24GB): MoE at Q4_K_M
- RTX 4090 (24GB): same as 3090, ~30% faster
- RTX 5090 (32GB): MoE at Q8_0
- 2× RTX 6000 Ada (96GB total): MoE at Q4_K_M
Related models in the Kimi family
- Kimi K2 — 1T (32B active MoE) · frontier agentic with 1T total / 32B active MoE
- Kimi K2.5 — MoE · Kimi K2.5 — January 2026 frontier update
- Kimi K2.6 — MoE · Kimi K2.6 — March 2026 update
Other models you might consider
- Llama 3.1 (Meta) — 8B, general-purpose chat, agentic workflows, code
- Llama 3.2 (Meta) — 1B, low-end hardware, mobile, edge deployment
- Llama 3.2 Vision (Meta) — 11B, vision + chat on desktop GPUs
- Llama 3.3 (Meta) — 70B, best-in-class 70B for desktops
- Llama 4 (Meta) — Scout 17B (109B MoE), vision + long context (10M tokens)
- Qwen 2.5 (Alibaba) — 0.5B, strongest 7B-32B on consumer hardware