Now Available: robolaunch AI Cloud Platform — GPU Workspaces for AI, Robotics & Simulation

Public access is now open

For years, we’ve deployed the robolaunch AI Cloud Platform into GPU-equipped on-prem environments at enterprises across automotive, electronics, and defense. On-prem brings a long list of advantages for these teams: data sovereignty, low-latency access to OT/IT systems, RBAC and auditability, and the ability to pool GPUs across many concurrent projects. When multiple squads are training models, running simulations, and serving inference against sensitive data, a scalable on-prem AI fabric can 10× throughput while remaining cost-efficient versus ad-hoc public compute.

But not every team has the budget or appetite to buy and maintain servers/GPUs—especially startups, research groups, and individual developers. For them, doing development, training, and serving on cloud GPUs is dramatically more efficient: spin up what you need, when you need it, and pay only for usage.

Public access is how we bridge those worlds. By making the robolaunch AI Cloud Platform available in the cloud, we’re giving:

• Budget-constrained teams & indie builders a fast path to GPU workspaces without CapEx.
• Enterprises an easy way to prototype and burst while keeping production on-prem (hybrid by design).
• Distributed teams a consistent, browser-based environment for AI/ML, computer vision, robotics, and simulation—no drivers, no friction.

Why this matters (and why now)

Most teams still pay an infrastructure tax: juggling CUDA versions, GPU provisioning, toolchains, driver mismatches, and laggy remote desktops. The result? Time lost that should be going into model training, evaluation, simulation, and inference deployment—the work that actually creates value.

The pressure to fix this is higher than ever:

• Faster model cycles: CV and LLM stacks evolve weekly; environments must keep up.
• Scarce/pricey GPUs: You can’t afford idle hardware or slow starts.
• Distributed teams: Standardized, browser-based GPU workspaces reduce “it works on my machine.”
• Governance & security: Enterprises need reproducibility, RBAC, and clear data boundaries.
• From demo to production: Moving code from a notebook to a reliable inference endpoint is still too slow.

Our point of view is simple: the competitive edge in AI isn’t just larger models—it’s shorter iteration loops from data to deployment. The robolaunch AI Cloud Platform exists to compress that loop for all AI workloads (computer vision, LLMs/NLP, simulation/robotics, time-series/forecasting). By removing setup friction and unifying develop → simulate → serve in the browser—with templates, one-click endpoints, usage analytics, and hybrid (cloud + on-prem) consistency—we shift your focus from machines to measurable outcomes.

What you can do in minutes

• Launch GPU Workspaces in your browser
  Cloud IDE + Jupyter Notebook + Terminal—no installs, no drivers.
• Run simulation & 3D tools with Cloud Desktop
  Use NVIDIA Isaac Sim, Blender, Unreal, Unity directly in the browser.
• Start from ready-to-use templates
  PyTorch, TensorFlow, YOLO, ollama, Isaac Sim, ComfyUI (growing weekly).
• Expose inference endpoints
  One-click, OpenAI-style APIs (e.g., vLLM/SGLang) to wire into your apps.
• Track cost & performance
  Built-in usage analytics: sessions, GPU type & hours, runtime trends.

Enterprise needs? We also support on-prem for security and low latency—hybrid is in our DNA.

Example workflows

Before we dive into examples, a quick note: the robolaunch AI Cloud Platform is purpose-built to cover the full loop—develop → train/simulate → serve → observe—in standardized, browser-based GPU workspaces that can be promoted to private inference endpoints or taken on-prem unchanged. The flows below are just representative. Teams use the same building blocks for time-series forecasting, speech, multimodal pipelines, synthetic data generation, reinforcement learning, geospatial/remote sensing, cybersecurity analytics, and more—across industries from automotive and electronics to healthcare, retail, and fintech. If your workload needs GPUs and reproducibility, you can model it on these patterns and scale from idea → pilot → production without switching tools.

• Vision AI / Quality
  Fine-tune a defect-detection model, validate on sample sets, then publish an endpoint for inline checks.
• Robotics & Simulation
  Prototype perception + motion in Isaac/Unreal/Unity without driver pain; record runs and iterate quickly.
• LLM & RAG
  Stand up ollama or vLLM, iterate prompts, and serve a private API to your internal tools.
• MLOps fast track
  Standardize environments for new teammates; reduce onboarding from days to minutes.

What’s next on the roadmap

More ready-to-use templates (vision, RL, simulation).
We’re packaging opinionated, production-leaning templates for common patterns—defect detection, pose estimation, synthetic data loops, reinforcement learning for motion/policy training, and simulation-driven evaluation. The goal: hit “Create,” get a best-practice stack (deps, CUDA, configs) in minutes, not hours.

One-click tutorial projects & documentation.
Guided, end-to-end examples that take you from template → training/eval → private endpoint in a single flow, with sample datasets and cost/latency tips. These will double as reproducible starter repos you can fork and extend.

Team spaces & role-based collaboration.
Shared projects with standardized environments, org-level secrets, and RBAC (admin/developer/viewer). Expect workspace sharing, audit trails, and “golden” images so new teammates are productive on day one.

Bring-Your-Own Docker image.
Point to your registry (Docker Hub/GHCR/ECR), validate against our runtime manifest, and run your own images with GPU acceleration. This unlocks true portability: develop in the cloud, then take the same image on-prem.

Deeper observability for inference endpoints.
Built-in traces and metrics (P50/P95/P99 latency, throughput, token/samples per second), structured logs, health checks, and A/B traffic splitting. You’ll see performance and cost signals early—before issues hit users.

Try it (launch offer)

We’re granting evaluation credits to early users who share feedback. Credits are sized to let you:

• launch a GPU workspace,
• run a tutorial project end-to-end,
• and publish a small private inference endpoint for testing.

Start here: www.robolaunch.cloud
Questions or credit requests: info@robolaunch.io or DM us on LinkedIn.

Note: Workspaces are isolated and we don’t train on your data. On-prem deployments are available for regulated environments.

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