Hey folks, I've been digging into the new local model support in Windsurf for the past couple of weeks, primarily for automating infrastructure tasks. While the privacy and offline potential is fantastic, I'm hitting some real performance snags that make me question its daily-driver viability for larger projects.
My typical workflow involves generating Ansible playbooks or Terraform modules based on high-level prompts. With the cloud models, this is nearly instantaneous. After switching to a local `Llama 3.1 70B` model (quantized, running via Ollama), the generation time for a similar task jumped from ~2 seconds to over 90 seconds on my M2 Max with 64GB RAM. The quality of the output is still quite good, but the wait is palpable and breaks my flow.
Here's a concrete example. I prompted for an Ansible playbook to harden a base Ubuntu server. The cloud model gave me a complete, structured response in a blink. The local model produced a similarly good playbook, but the thinking time was substantial.
```yaml
# Example of the *type* of output from the local model. Quality is fine.
- name: Harden Ubuntu Server
hosts: all
become: yes
tasks:
- name: Ensure automatic updates are enabled
ansible.builtin.apt:
name: unattended-upgrades
state: present
- name: Configure unattended-upgrades
ansible.builtin.copy:
src: 50-unattended-upgrades.j2
dest: /etc/apt/apt.conf.d/50-unattended-upgrades
```
The real cost, beyond time, is the resource consumption. To get usable speeds, I had to drop down to a `CodeLlama 13B` model. The performance was better (~15-20 second generation), but the output quality suffered noticeably for complex tasks—more hallucinations in module options, less understanding of best practices.
**So, my current take:**
* **Worth it for:** Sensitive data (obviously), learning/experimentation, simple boilerplate generation when completely offline.
* **Not worth it (yet) for:** Rapid iteration, large or complex infrastructure code generation, daily automation where speed matters.
I'm curious if others have found a sweet spot. Have you tuned your local inference settings (like `num_ctx`, `num_thread`) significantly? Are there smaller, code-specific models that deliver both decent speed and reliable accuracy for our kind of work? The feature is a huge step forward philosophically, but practically, I'm still leaning on the cloud for most of my heavy lifting.
—John
Keep it simple.
I'm a principal platform engineer at a fintech with around 300 microservices in production. We use a mix of cloud and local models for different tasks, with most of our CI/CD automation still on cloud providers for speed.
**Core comparison for local vs. cloud model support:**
1. **Latency & developer flow:** Cloud models give you ~1-3 second responses, which feels interactive. Local 70B-class models, even quantized, introduce a 45-90 second thinking time on high-end hardware. This is the single biggest blocker for iterative, in-flow work.
2. **Infrastructure & hidden cost:** Cloud model cost is predictable SaaS (~$20-50/user/mo depending on provider). Local model cost is hardware (your M2 Max is a ~$3k+ machine) and operational overhead (managing Ollama, GPU memory, updates). For a team, you quickly need dedicated inference servers, which changes the TCO.
3. **Privacy boundary & fit:** Local is mandatory for air-gapped environments or strict data sovereignty requirements - this is its clear win. For all other use, cloud models are operationally simpler. If you're not in a regulated industry (healthcare, gov, certain fintech), the privacy argument weakens.
4. **Output quality for infra tasks:** For structured outputs like Terraform/Ansible, I've found the quality delta between a top-tier cloud model (like GPT-4) and a local 70B model to be about 10-15% less "correct on first try" in my testing. The local model needs more precise prompting and occasional re-runs.
**My pick:** I recommend sticking with cloud models for your primary daily driver. The latency hit is a real productivity killer. Reserve the local model for specific, sensitive tasks where you cannot ship the prompt externally - run those jobs in batch, not interactively.
To make a cleaner call, tell us: 1) Is any of your infrastructure code handling true PII/secrets in the prompts? 2) What's the size of your team that would need access?
I've seen this exact line of thinking trip up a half dozen clients in the last year. You're correct on the latency and hardware points, but that third point about privacy boundaries is where I've watched teams make a critical error in their cost-benefit.
> If you're not in a regulated industry... the privacy argument weakens.
This assumes privacy is purely a compliance checkbox. It's not. It's a liability and a negotiation point. We had a client in ad-tech, not heavily regulated, push everything local for "IP protection." They spent six figures on inference hardware and dedicated ops time. Then their legal team reviewed the standard cloud provider DPA and indemnification clauses and realized the actual risk was negligible compared to their new capital expenditure. They'd conflated a vague sense of secrecy with a quantifiable contractual obligation.
The real question teams should ask is: what specific data categories, under which contracts or regulations, cannot physically touch a vendor's log for *any* period? If you can't point to the clause, you're buying insurance for a fender bender with a gold-plated tank.
Test the migration.