Data framework connecting LLMs to private documents, providing indexing, retrieval and query engines in Python with common loader and vector store integrations. Built and maintained by WIEWAVE for Azure Marketplace, AWS Marketplace and Google Cloud Marketplace, on Ubuntu and Debian.
| Built & maintained by | WIEWAVE |
|---|---|
| Category | AI & Machine Learning |
| Operating systems | Ubuntu, Debian |
| Marketplaces | Azure Marketplace, AWS Marketplace and Google Cloud Marketplace |
| Offer types | Public listing, with private offers on request |
| Marketplace | Status |
|---|---|
| Azure Marketplace | Published |
| AWS Marketplace | Published |
| Google Cloud Marketplace | Published |
Need it on another marketplace, or as a private offer for your organisation? cloud@wiewave.com
Each image follows its distribution's own provisioning model, package manager and security tooling — not one build relabelled several times.
LTS and interim releases, Minimal and Pro variants, built to Canonical's cloud-image conventions.
Stable and oldstable, with backports where a workload needs a newer runtime than the release ships.
The same four steps behind every offer we've published, including the hardening and CIS Benchmark checks every build goes through.
The distribution, licensing model and target marketplaces are agreed before anything is built.
Packer templates, Ansible provisioning and a pinned package set — then hardened, scanned and checked against the CIS Benchmark for its distribution.
Taken through each cloud's own certification pipeline before it goes live on the marketplace.
Rebuilt on the upstream security cadence and re-published, with old versions retired without breaking deployments.
If yours isn't here, ask our marketplace team directly.
GPU-ready training and inference images with drivers, CUDA and frameworks already matched to each other.
Tooling image aimed at inspecting and evaluating large language model behaviour; the packaged application's exact feature set should be confirmed before use.
Drop-in OpenAI-compatible API server that runs local models for text, embeddings, images and audio without calling external inference services.
Transformer architecture using sliding-window attention to process long documents, supplied with pretrained checkpoints and Hugging Face inference dependencies.
Declarative machine learning framework where models are defined in YAML configuration, wrapping PyTorch for training text, tabular and image tasks.
Fast C++ toolkit for training and serving neural machine translation models, compiled with CPU and GPU decoding support and example configurations.
Image providing a large language model runtime for text generation workloads, with GPU inference dependencies installed and a serving entry point.
Tell us the distribution, the marketplace and the commercial model — we'll build, certify and publish it as a public listing or a private offer.
