RTX 5060 Ti 16GB for Development Sandbox

Every engineering team building AI features needs a dev GPU that isn’t prod. A RTX 5060 Ti 16GB on our hosting is the right tier for this.

Contents

• The sandbox role
• Workloads
• Good practices

The Sandbox Role

A development sandbox does three things:

1. Lets developers iterate on model configs without touching prod
2. Runs integration and regression tests for AI features
3. Hosts experimental models being evaluated for future prod rollout

Typical Workloads

• “Try this new model” – download weights, launch vLLM, hit endpoint
• CI: nightly runs of an eval suite against your prod model candidate
• Load testing – run the benchmark script to validate after config changes
• LoRA experiments – fine-tune candidates before rolling out
• Prompt A/B testing against eval datasets
• New integration work – building against a staging API endpoint

Good Practices

• Identical to prod config where possible. If prod is vLLM + FP8 + FP8 KV, sandbox matches. Reduces “works on dev, fails on prod” bugs.
• Per-developer namespaces. systemd user services or tmux sessions so multiple engineers don’t trample each other. See systemctl user services.
• Synthetic test data. No PII, no customer data – scrubbed fixtures only.
• Snapshot weights to fast storage. Gen4 NVMe or Gen5 means model swaps are fast.
• VSCode Remote SSH / Jupyter for seamless development from laptops – see VSCode Remote setup.
• Keep one stable model loaded. Run the current-prod model continuously as the “source of truth” for comparative testing.

For a 3-10 person team, one 5060 Ti sandbox handles experimentation without bottlenecks. Scale to a second when you outgrow it.

See also: internal tooling, research lab, VSCode Remote, benchmark script.