Kimi K2 Instruct
Kimi K2 Instruct 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 Instruct locally
At 1000B parameters (32B active per token), a Q4-class build of Kimi K2 Instruct 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.
1T parameter MoE model (32B active). Cloud/API only, requires ~560GB RAM at Q4, not feasible locally even on Mac Studio M3 Ultra 512GB.
Strong local alternatives
More Kimi models
Frequently asked questions
Can I run Kimi K2 Instruct locally?
Not realistically. Kimi K2 Instruct 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 Instruct?
Through the vendor-hosted API. See the official source linked on this page.
What is the best local alternative to Kimi K2 Instruct?
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 Instruct — specs, memory math and hardware verdicts. https://modelfit.io/models/kimi-k2-instruct/ (dataset updated 2026-09-03, CC BY 4.0).