Kimi K2.7-Code
Kimi K2.7-Code publishes its weights, but at 1000B parameters (32B active per token) no consumer machine holds the checkpoint. This page covers the access paths that work and the open models that actually run locally.
Why you can'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. That figure is arithmetic, not opinion: 0.6 GB per billion parameters is the standard Q4 rule we apply across the whole catalog. The weights themselves are public (official source linked below); capacity, not licensing, is the wall.
What works instead: the hosted API. For most workloads, the open models below deliver the same job on hardware that fits under a desk.
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.
What to run locally instead of Kimi K2.7-Code
More Kimi models
Frequently asked questions
Can I run Kimi K2.7-Code locally?
Not on hardware you can buy. The weights are public, but Kimi K2.7-Code is a 1000B-parameter model (32B active), and a Q4-class build would need roughly 600 GB — beyond any consumer machine. Until the ecosystem ships a smaller official build, the API or a smaller open model is the practical path.
How do I access Kimi K2.7-Code?
Through the vendor's hosted API. The official source is 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, from 192 GB machines), with Qwen3.5 122B-A10B Instruct close behind. Both run on a single high-memory Mac or GPU — see their pages 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).