Kimi K2.7-Code
Kimi K2.7-Code is a 1000B-parameter model you reach through an API, with 32B parameters active per token — too large to self-host, so this page covers what it does, how to access it, and what to run locally instead.
You don't run Kimi K2.7-Code locally
At 1000B parameters (32B active per token), a Q4-class build of Kimi K2.7-Code would need roughly 600 GB — beyond any consumer machine (0.6 GB per billion parameters, our standard Q4 rule). Access is through the hosted API.
Jun 12, 2026 Moonshot AI release. 1T total / 32B active MoE built on K2.6. Moonshot reports it cuts thinking-token usage ~30%, a self-reported figure. 256K context, Modified MIT license. Open weights, but 1T scale means cloud/API for nearly all users; Ollama exposes a cloud-only tag.
Strong local alternatives
More Kimi models
Frequently asked questions
Can I run Kimi K2.7-Code locally?
Not realistically. Kimi K2.7-Code is a 1000B-parameter model (32B active); a Q4-class build would need roughly 600 GB — beyond any consumer machine. The hosted API or a smaller open model is the practical path.
How do I access Kimi K2.7-Code?
Through the vendor-hosted API. See the official source linked on this page.
What is the best local alternative to Kimi K2.7-Code?
Qwen3 235B A22B is the strongest local model we track (235B). It runs on a single high-memory Mac or GPU; see its page for exact hardware.
Cite this page
ModelFit: Kimi K2.7-Code — specs, memory math and hardware verdicts. https://modelfit.io/models/kimi-k2.7-code/ (dataset updated 2026-09-03, CC BY 4.0).