Cohere open-sourced a 30B coding model that runs on a single H100. Agentic coding without the frontier bill.

Cohere launched North Mini Code on June 9 under Apache 2.0, its first developer-focused model. The shape is the pitch: 30B parameters, mixture-of-experts, only ~3B active, and it runs on a single H100. It scores 33.4 on the Artificial Analysis Coding Index, competes on SWE-Bench Verified, SWE-Bench Pro, and Terminal-Bench 2.0, and claims up to 2.8x higher throughput with ~30% lower inter-token latency than Devstral Small 2. Cohere says it beats open models up to 4x its size.

Why this matters more than another benchmark post: this is a genuinely deployable local coding agent. Apache 2.0 means no usage restrictions, single-H100 means you can self-host it without a datacenter, and the throughput numbers mean it's fast enough to sit in an actual agent loop rather than a toy demo. Put it next to the NVIDIA RTX Spark Superchip hitting availability with 128GB unified memory and 120B-param local inference, and the "you need frontier infra for agentic coding" assumption is eroding fast.

I haven't run North Mini Code yet, so treat the throughput claims as vendor numbers until you A/B them on your own repo. But the use case writes itself. The work where you don't want to pay $50/M to Fable or ship proprietary code to an API: tight inner-loop edits, test generation, the high-volume agentic grunt work. Run a local 30B for the cheap 80% and reserve frontier calls for the hard 20%. That two-tier split, local model for volume, frontier for the genuinely hard problems, is starting to feel like the default architecture rather than a cost hack. Pull the weights, wire it into your harness, and measure it against whatever you're currently overpaying for.