Managed Local AI
Managed Ollama-based local AI on customer-owned GPU infrastructure with Open WebUI as the default interface.
- Ollama standard
- Open WebUI default
- No third-party AI API required by default
- Managed setup and updates
Managed Local AI on your own GPU
We deploy and maintain a local AI stack around Ollama so teams can use modern models in their chosen server context without requiring a third-party AI API by default.
- Managed Local AI with Ollama standard
- Open-source hosting with CyberPanel
- Domains, support, and direct offer paths
Ollama standard. Open WebUI default. External providers only when you choose them.
AI Apps
Private knowledge, team chat, workflow automation, and creative GPU app stacks managed around open-source tools.
- AnythingLLM or LibreChat
- Flowise and n8n options
- ComfyUI for creative GPU workflows
- vLLM only as optional advanced layer
GPU Infrastructure
Right-sized GPU servers for local inference, image workflows, and private automation stacks.
- Dedicated setup
- Storage and backup planning
- Monitoring and maintenance
- Benchmark before performance promises
Open Source Hosting
Managed open-source hosting with CyberPanel, domains, SSL, DNS, and human support.
- CyberPanel control panel
- WordPress, Nextcloud, Matomo and more
- No cPanel or CentOS claims
- Built for long-term maintenance
Domains
Domain registration, renewal, transfer guidance, and DNS support for open-source projects and teams.
- Popular TLDs with USD pricing
- Renewal notes shown clearly
- Transfers reviewed by registry rules
- DNS basics included
Ollama standard, managed for real use
We install, wire, harden, update, and explain the stack. The default path runs models in the selected local system context without requiring a third-party AI API.
Open WebUI
Team-friendly chat interface for Ollama and compatible endpoints.
AnythingLLM
Document workspaces and private knowledge flows where that is the right fit.
vLLM optional
Only for advanced, development, or performance work after benchmarks on the target model and GPU.
Private AI app bridge and access-control benchmark
Many teams do not need a new chatbot first; they need a local endpoint and controlled access that existing tools can call. We benchmark the bridge before exposing it to users.
OpenAI-compatible local endpoint
Ollama can expose OpenAI-compatible chat and responses paths, so internal scripts and app prototypes can be tested against a local model endpoint after readiness checks.
Open WebUI roles and SSO scope
For team deployments, we scope RBAC, SSO/OIDC, API keys, and model or knowledge-base permissions before anyone relies on the system.
Quantized vLLM only after fit checks
AWQ, GPTQ, GGUF, and FP8 paths can be relevant on Ada hardware, but they remain an advanced benchmark track, not the default production promise.
Offer rule: sell integration design and benchmark evidence first; live production traffic waits for GPU driver visibility, Ollama health, access-control review, and a target-workflow smoke test.
Private local agent safety benchmark
Local reasoning models make private agents attractive, but tool use changes the risk profile. Business Secure treats agents as a controlled workflow benchmark, not a generic autonomous assistant promise.
gpt-oss 20B as a benchmark candidate
OpenAI positions gpt-oss models for reasoning and tool-use workflows, while Ollama lists the 20B path around 14 GB with a 128K context window. On this server class, that remains a measured Business Secure trial.
OpenAI gpt-oss-20b model card →Ollama gpt-oss →gpt-oss-safeguard 20B policy benchmark
OpenAI describes gpt-oss-safeguard as open safety reasoning models for custom safety policies. Treat the 20B model as a measured policy-classification benchmark for local agent workflows, not an automatic moderation guarantee.
OpenAI safeguard announcement →Hugging Face safeguard-20b →Tools are treated as privileged access
Open WebUI documents that tool access can execute arbitrary Python code. We scope tool permissions, API keys, and provider credentials before any agent workflow touches customer data or business systems.
Open WebUI permissions →Fixed-task audit before rollout
The benchmark uses a fixed task set, allowed tools, blocked tools, sample data, logging expectations, and rollback notes. Production use waits for GPU/Ollama health and a repeatable target-workflow smoke test.
Sales rule: sell private agent readiness, permission design, and benchmark evidence first. Do not promise autonomous production agents until runtime health, access boundaries, and task-level failure behavior are documented.
Readiness first, then model fit
Modern local AI changes quickly. We turn that uncertainty into a short, practical readiness path before you commit to a larger rollout.
Runtime check
We verify GPU drivers, CUDA visibility, Ollama or container service health, storage, backups, and secure access before model work starts.
20 GB model shortlist
For RTX 4000 Ada class systems, we shortlist realistic small-to-medium models such as Qwen, Gemma 4, or DeepSeek distill variants and document tradeoffs.
Benchmark report
Performance claims are made after testing the target model, quantization, context length, and user workflow. If vLLM is useful, it stays an optional advanced layer.
Current RTX 4000 Ada model-fit shortlist
Checked against current source signals on 2026-06-02. These are benchmark candidates for 20 GB systems, not guaranteed live-throughput promises.
Qwen3 Embedding + Reranker
Qwen3-Embedding and Qwen3-Reranker 0.6B/4B/8B cover multilingual retrieval, code search, and source ranking. Test corpus quality, storage, latency, and answer citations before rollout.
Qwen3 Embedding announcement →Qwen3-Embedding-4B card →Qwen3-VL + Docling baseline
Qwen3-VL 8B is a current OCR and document-structure candidate; Docling/OCR gives a deterministic parsing baseline for PDFs, tables, reading order, and field-level checks.
Qwen3-VL-8B model card →Qwen3-VL Embedding + Reranker trial
Qwen3-VL-Embedding-8B and Qwen3-VL-Reranker-8B are current multimodal retrieval candidates for text, images, screenshots, videos, and mixed documents. On 20 GB systems, scope them as a measured visual-RAG benchmark with corpus size, vector storage, latency, and fallback limits.
Qwen3-VL-Embedding-8B card →Qwen3-VL-Reranker-8B card →Qwen3-Coder 30B benchmark
Ollama lists the 30B path around 19 GB with 256K context, so on a 20 GB GPU it is benchmark-only with strict context, concurrency, and fallback limits.
Ollama Qwen3-Coder listing →Gemma 4 E4B/26B trial
Gemma 4 E4B is the low-memory assistant and multimodal candidate. Gemma 4 26B and 31B are benchmark-only on 20 GB because Ollama lists 18 GB and 20 GB footprints; concurrency, context, and MTP latency gains stay gated by local smoke tests.
Google Gemma 4 announcement →Gemma 4 MTP drafters →gpt-oss 20B private reasoning benchmark
OpenAI positions gpt-oss-20b for local and specialized reasoning use, and Ollama lists the 20B path around 14 GB with 128K context. Treat it as a strong Business Secure benchmark candidate with strict context, tool-use, safety, and latency checks before rollout.
OpenAI gpt-oss-20b card →OpenAI model card PDF →Operational rule: publish no live local-inference claim until the driver stack, Ollama health, and a target-model smoke test pass on the actual server. Current public offers sell benchmark evidence, setup, and managed operating discipline first.
Four benchmarkable local-AI offer tracks
These are practical packages for the RTX 4000 Ada 20 GB class. Each one starts with a measured smoke test before any production performance claim is made.
Private RAG with sources
For internal documents, support archives, policies, and project knowledge where every useful answer should point back to a source.
- Open WebUI or lightweight team UI
- Qwen3 or Gemma 4 chat candidate
- EmbeddingGemma or Qwen embedding trial
Smoke test: ingest a representative corpus, ask fixed benchmark questions, require cited answers, and record VRAM, latency, and miss behavior.
Start Team RAG benchmark →Local PDF and image extraction
For invoices, forms, screenshots, and operational documents that need local extraction support without sending files to a cloud AI API by default.
- Qwen3-VL/Qwen2.5-VL benchmark
- Docling/OCR fallback for hard scans
- Field-level error notes
Smoke test: run real sample pages against expected fields, measure false positives, unsupported layouts, throughput, and VRAM peak.
Read document intake benchmark →Request document workflow trial →Visual RAG and evidence search
For teams that need to retrieve answers from screenshots, diagrams, scanned pages, product images, or short video captures with evidence links instead of plain text-only RAG.
- Qwen3-VL-Embedding 8B recall trial
- Qwen3-VL-Reranker 8B ranking trial
- Source thumbnails and miss analysis
Smoke test: index a small mixed-media corpus, ask fixed visual-search questions, record top-k misses, reranker gains, storage size, VRAM, and latency before rollout.
Start visual RAG benchmark →Local transcription and meeting notes
For interviews, internal meetings, and support recordings where private audio handling and predictable operations matter more than a generic SaaS workflow.
- Whisper or faster-whisper benchmark
- English and German sample set
- Optional local summary pass
Smoke test: transcribe representative 5 to 30 minute files, record runtime factor, language quality, segmentation limits, and GPU use.
Scope transcription benchmark →Positioning rule: these tracks are sold as benchmarked setup and managed operations. Live local-inference claims wait for driver visibility, Ollama or serving health, and a target-workflow smoke test on the actual host.
RAG Retrieval Quality Audit
A smaller paid first step for teams that already know they need private knowledge search, but do not yet know whether their documents, questions, and permissions are ready for a managed monthly rollout.
Representative corpus first
We start with a limited set of real documents, screenshots, policies, tickets, or manuals and turn them into a fixed retrieval benchmark instead of ingesting everything blindly.
Qwen3 small-to-medium retrieval path
Qwen3-Embedding 0.6B/4B and Qwen3-Reranker 0.6B/4B are the default audit candidates. The 8B path stays optional when corpus size, latency, and the 20 GB VRAM budget justify it.
Answer quality before rollout
The result is a short report with top-k misses, citation quality, reranker gains, storage notes, privacy boundaries, and a clear decision: fix sources, run a pilot, or move to Team RAG.
Commercial rule: this audit sells retrieval evidence and rollout clarity. It does not claim production local inference until the GPU, Ollama, selected models, and the target workflow pass smoke tests on the actual server.
Plans that map to real operating work
Use the public page to choose the right starting point, then confirm the exact setup, renewal terms, and any benchmark notes in the HostBill checkout.
BYO Server Management
from $299.18/mo
For teams that already have a suitable GPU server or rented instance and need managed Ollama, Open WebUI, updates, and monitoring.
- Driver and Ollama readiness check
- Open WebUI installation
- Hosting costs paid directly by you
Local AI Managed
from $699.42/mo
For private local model hosting with a managed operating layer and no third-party AI API required by default.
- 20 GB model-fit shortlist
- Benchmark report before promises
- Managed updates, monitoring, backups
Team RAG
from $999.60/mo
For teams that need document-assisted local AI, curated embeddings, user roles, and clearer knowledge workflows.
- Knowledge/RAG setup
- Embedding and model guidance
- Prioritized support
Business Secure
from $1,499.90/mo
For production business use where security, audit preparation, update windows, and support scope matter.
- Security hardening
- OIDC/SSO preparation
- Monthly review and change windows
Current runtime gate: GPU driver visibility and Ollama service health are verified before live inference claims. RTX 4000 Ada class systems are treated as small-to-medium model hosts, not universal frontier-model servers.