AI / ML HOSTING
From your first self-hosted language model to GPU-powered applications. Find the right compute for inference, experimentation and training with LilabaliCloud.
An architecture shaped around your workload.
CHOOSE YOUR STARTING POINT
A lightweight assistant and a training pipeline need different resources. Start with your workload, then size the environment.
Configurations are quoted individually. Hardware, region, availability and management scope are confirmed with your proposal.
START WITH WHAT YOU WANT TO BUILD
Select a workload for a starting point. Final sizing depends on your model and a representative performance test.
Explore a self-hosted chat interface for drafting, summarising and everyday questions. Start with a model that fits your memory budget and expected number of users.
BRING YOUR PREFERRED TOOLS
Choose the model, serving layer and application experience together. We can discuss the software requirements of your proposed environment before you commit.
Tell us about your stackLlama · Qwen · Mistral · Your own model
Ollama · vLLM · Open WebUI
PyTorch · Transformers · Jupyter
Vector storage · APIs · Containers
Tools shown are examples for compatibility review. Model licenses and hardware support vary; no third-party affiliation is implied.
| What matters | LLM VPS / CPU | GPU compute |
|---|---|---|
| Typical starting use | Small models, development and occasional requests | Accelerated inference, media generation and training |
| Memory planning | System RAM holds model and runtime data | GPU VRAM plus system RAM; leave room for context and concurrency |
| Performance | Practical when slower responses are acceptable | Parallel compute for suitable models and frameworks |
| Cost planning | CPU, RAM, storage, traffic and administration | GPU type and count, active time, storage, traffic and administration |
| Before you decide | Test that your model fits and response time is acceptable | Benchmark throughput, latency and memory at expected load |
A CLEAR PATH TO YOUR ENVIRONMENT
Compute is one part of the setup. Define the software, data and day-to-day responsibilities before launch.
Share the model, task, expected users and response-time goals. Include a sample request or dataset description.
Review memory, compute, storage and region. Confirm availability, billing terms and management responsibilities.
Check the runtime, access controls and model performance with a representative workload before production use.
Agree monitoring, updates, backups and recovery. Revisit capacity as your model or usage changes.
Endpoint authentication · Data location & retention · Model licensing · Backup ownership · Support scope
Not always. A small, quantized language model can run on a CPU if there is enough RAM, though responses may be slower. GPU acceleration is worth considering for larger models, image generation, higher concurrency or lower latency. Benchmark the actual model and workload before choosing hardware.
Inference runs an existing model to generate an answer, prediction or image. Fine-tuning adapts a model using your examples; training from scratch creates a model from a dataset. Training typically needs additional memory, storage and compute time, so it needs its own configuration.
Include them in your enquiry. Ollama can run supported language models, while Open WebUI provides a browser interface. The operating system, model, memory requirements and access controls need to be checked for the proposed environment.
Bring your preferred model and runtime, such as Llama, Qwen, Mistral, PyTorch or Hugging Face Transformers. Compatibility depends on the specific model version, license, hardware and software stack. These are examples to discuss, not a pre-installed catalogue.
Model weights are only part of the requirement. Precision, context length, concurrent requests, runtime overhead and the attention cache also consume memory. Fine-tuning adds training state and activations. Share these details so the configuration can include suitable headroom.
Request a quote with your model, expected usage, preferred region and budget. The proposed hardware, billing period, storage, network allowances and support scope are confirmed in the quote. This page does not show live GPU inventory or offer instant provisioning.
Agree responsibilities before deployment. A self-managed environment requires someone to maintain the operating system, runtime, access rules and backups. If you need assistance, include monitoring, updates, recovery and support expectations in your request.
Discuss where data is stored and processed, who can access it, what is logged and how long it is retained. Self-hosting gives you more configuration control, but privacy still depends on the deployment, software integrations and operating practices.
LET'S FIND YOUR NEXT STEP
Tell us what you’re building. We’ll discuss a configuration, availability and a quote for your requirements.
Request a quote
Get started in minutes. No coding. No hassle. Just growth.
