> SAVE thousands of dollars per month in AI subscriptions
> Your own ChatGPT, on YOUR hardware, with YOUR data
> NO CLOUD. NO COMPROMISE. NO LIMITS.
Configure your AI workstation with real-time pricing
Deploy ChatGPT-quality AI trained on your internal documentation. Process invoices, emails, and customer data without sending sensitive information to third-party servers.
Run everything locally. Zero data transmission to cloud services. Complete control over your AI infrastructure and data processing pipelines.
Fine-tune models, run vLLM inference servers, deploy ComfyUI workflows, or spin up custom Docker containers. Full root access to your AI development environment.
Build AI products without OpenAI rate limits. Deploy autonomous agents, create custom AI services, and scale without recurring subscription costs.
RAG agent framework with sample chatbot, vector database integration, and document processing pipeline.
vLLM, llama.cpp, Ollama, and HuggingFace Transformers for multi-model deployment and inference optimization.
GPU utilization monitoring, automatic load balancing, and performance analytics dashboard.
Web-based management interface for model deployment, fine-tuning, and system configuration.
Optional onboarding assistance, custom model fine-tuning, and integration support.
Locally owned. Locally trained. Your data never leaves your premises. Full ownership of hardware and models.
Enterprise support you can call or visit. Local technicians, remote assistance, and 24/7 monitoring options.
Built to last — watercooled systems, expandable architecture, stress-tested for 24/7 operation.
Tailored to your data — not a generic cloud dashboard. Custom model training and deployment pipelines.
A: Yes, but private, faster, and tailored to your company's specific use cases and data.
A: Only for updates and optional cloud model downloads. Your AI runs completely offline.
A: Yes, and we'll help you set up secure integrations with your existing business systems.
A: Any open-source model: LLaMA, Mistral, Qwen, Mixtral, and custom fine-tuned variations.
Retrieval-Augmented Generation: A technique that enhances large language models by integrating them with external knowledge bases and documents for more accurate, contextual responses.
Any open-source models including LLaMA 2/3, Mistral, Qwen, Claude-style models, and custom fine-tuned variations. Full compatibility with HuggingFace model hub.
| PARAMETER | SAAS_MODEL ($15K/month) | LOCAL_BOX ($15K one-time) |
|---|---|---|
| OWNERSHIP | RENTAL | FULL_OWNERSHIP |
| PRIVACY | CLOUD_SHARED | LOCAL_ONLY |
| LONG_TERM_COST | HIGH_RECURRING | ONE_TIME_PURCHASE |
| CUSTOMIZATION | LIMITED | UNLIMITED |
Deploy AI sales agents trained on your product catalog, pricing, and customer interaction history.
Create an internal chatbot trained on company documentation, SOPs, and institutional knowledge.
Run your own ChatGPT-equivalent for sensitive business communications and document processing.
| Model | Single‑stream | Batched Throughput | Notes |
|---|---|---|---|
| Llama‑3.1‑70B | ~600–800 tok/s | ~2,400–3,600 tok/s | INT4 or FP8 mixed; short prompts |
| Qwen‑2.5‑72B | ~500–750 tok/s | ~2,000–3,300 tok/s | Similar footprint to 70B |
| Mixtral 8×22B (MoE) | ~1,200–1,800 tok/s | ~4,000–7,000 tok/s | 2 experts active; shines with batching |
| Llama‑3‑405B* | ~50–100 tok/s | ~150–300 tok/s | *Aggressively quantized; demo‑only |
| Models | Latency / Realtime Factor | Concurrent Capacity | Notes |
|---|---|---|---|
| XTTS‑v2 / CosyVoice2‑open | ~10–20× realtime (1s audio in 50–100 ms) | 100s of streams | GPU‑light; multiplex many users |
| Piper (CPU‑friendly) | ~1–3× realtime on CPU | Dozens per node | Great fallback / edge |
| Workflow | 1×5090 Latency | 4×5090 Parallel Throughput | Notes |
|---|---|---|---|
| SDXL 1024×1024 · 30 steps | ~1.5–2.2 s / img | ~2–3 img/s | xFormers / TensorRT paths |
| SDXL 1536×1536 · 30 steps | ~2.5–3.5 s / img | ~1–1.6 img/s | Higher‑res scales near‑linearly |
| SD 1.5/2.1 · 768p · 30 steps | ~0.6–0.9 s / img | ~4–6 img/s | Great for bulk assets |
| Pipeline | Single‑clip Latency | 4× Parallel Throughput | Notes |
|---|---|---|---|
| Stable Video Diffusion · 576–720p · 25–49f | ~3–8 s / clip | ~3–6 clips/min | From image conditioning |
| AnimateDiff · 512–768p · 16–24f | ~2–6 s / clip | ~4–10 clips/min | Stylized loops; predictable |
| Open‑Sora‑style / HunyuanVideo‑open (short) | ~6–15 s / clip | ~1–3 clips/min | Quality varies, fast iteration |
Estimates assume optimized kernels, quantization (INT4/FP8 where applicable), PCIe Gen5 x16 per GPU, and healthy airflow/water temps.