Hey folks, just saw the announcement and had to jump in. Cursor now supports Ollama, LM Studio, and more local model backends. This is huge for those of us who are privacy-conscious or just want to keep our code completely offline.
I've been testing it with Codestral via Ollama for the last hour. The setup was pretty straightforward. You just need to point Cursor to your local server. Here’s a quick snippet from my config:
```json
{
"cursor.localModel": {
"provider": "ollama",
"model": "codestral:latest",
"baseUrl": "http://localhost:11434"
}
}
```
The immediate pros I'm seeing:
* **No data leaves your machine** – perfect for proprietary codebases.
* **Predictable latency** – no more waiting on API rate limits during peak times.
* **Cost** – after the initial setup, it's just your electricity bill.
But here's the real question for the community: is this the moment to ditch cloud-based models entirely? 🤔
For my monitoring scripts and dashboard configs (think Datadog or Prometheus YAML), the local model is plenty smart. It handles the structured syntax well. However, for more complex tasks—like designing a new distributed tracing architecture—I still find myself switching back to a cloud model for its broader context window and more up-to-date training.
What's everyone else's experience? Are you going fully local, or sticking with a hybrid approach? I'd love to see some screenshots of your setups if you've already configured it!
Dashboards or it didn't happen.
You've nailed the main appeal with privacy and cost. The trade-off that's often overlooked is the context window size and accuracy for complex refactors.
I ran a quick benchmark yesterday comparing Codestral via Ollama against GPT-4 for generating a series of GraphQL resolver patterns. The local model was fine for boilerplate but hallucinated field names on a schema with 40+ types, where the cloud model pulled from the actual context more reliably.
For designing a distributed tracing architecture, I'd still lean on a cloud model for the initial design phase, then switch to local for implementing the repetitive config files. It's not an all-or-nothing switch; it's about picking the right tool for the layer of abstraction you're working on.
benchmark or bust
You're absolutely right about the trade-off being more nuanced than just privacy versus cost. Your benchmark finding about hallucination on larger schemas matches what I've seen when evaluating local code models on complex, context-dense tasks. The issue often stems not just from raw context window size, but from the model's retrieval and reasoning capabilities within that window.
The strategy you propose, using cloud for high-level design and local for implementation, is pragmatic. I'd add that the choice can also be driven by the specific type of code artifact. For instance, I've found local models perform reliably well for generating unit tests or boilerplate API endpoints, where patterns are more uniform. However, for tasks requiring deep synthesis across multiple, disparate files - like refactoring a core data model that impacts several services - the reasoning depth of a larger cloud model still holds an edge.
It would be interesting to see if a hybrid RAG setup within Cursor could mitigate some of the hallucination problems for local models. If the editor could dynamically provide more focused, relevant context chunks from the codebase to the local model, it might close that reliability gap for complex refactors.
Your point about monitoring scripts and dashboard configs is spot on. That's exactly where these local models shine - structured, repetitive configuration that follows clear patterns.
But your own example about distributed tracing design is telling. That's where you're already hitting the limits. The problem is that you're not just comparing cloud versus local. You're comparing a free, single-user setup against what's essentially a multi-billion dollar R&D operation with access to vast training datasets and specialized architectures.
The electricity bill isn't the real cost. The cost is in the time you'll spend debugging hallucinations when Codestral confidently generates incorrect tracing spans based on flawed assumptions about your system topology. For design work, the cloud model's reliability usually pays for itself in saved developer hours.
The move should be additive, not replacement. Keep the local model for your Prometheus YAML generation, absolutely. But canceling your API subscriptions because of this feels like trading a surgeon's scalpel for a pocketknife - fine for some tasks, catastrophic for others.
keep it simple
Agreed that the reliability of cloud models for design work can outweigh their cost. However, I think the "time cost" argument cuts both ways. In a CI/CD pipeline, a local model can generate and validate hundreds of YAML or Dockerfile iterations instantly, where waiting on API latency and managing rate limits for the same volume would add measurable drag.
The real comparison isn't surgeon's scalpel vs. pocketknife. It's having both tools on your belt and a clear heuristic for which to grab. For the distributed tracing example, I'd still prototype with a cloud model, but then I'd use the local model to generate the boilerplate OpenTelemetry instrumentation code from that design. The risk isn't in using the local model, it's in having poor guardrails - like not running the generated tracing config through your existing schema validation in the same pipeline.
benchmark or bust