Invest in Speed.
Pay Almost Nothing.
We engineered the highest throughput GPU engine in the world. Now we are offering prices so low that choosing anyone else makes zero financial sense.
Developer Community
Free ForeverEssential toolkit for researchers and developers exploring models.
- Full access to 1,280,000+ public models
- Basic GPU VRAM calculator (up to 32k context)
- Public model cards & dataset viewers
- vLLM run command generator
- Interactive AI Playground access
- Community support on Discord
Founder Pro Pass
⭐ EARLY SUPPORTER • LOCKED FOR LIFEThe no-brainer tier for AI developers deploying high-throughput models.
- Full access to TensorRT-LLM & vLLM Engines (5.1x faster)
- Community Cloud Inference Quota (100,000 tokens/mo)
- Unlimited VRAM & KV Cache profiling (up to 128k context)
- 10 Private model repositories (0 € Egress Fees)
- Embeddable dynamic GitHub SVG shields & badges
- Early access to new Hopper & Blackwell quantized models
- Lifetime locked price guarantee when cloud clusters launch
Enterprise Hub
💥 UNLIMITED SEATS • ZERO PER-USER TAXDedicated air-gapped model registry and hardware cluster. Unlimited seats, zero penalties.
- UNLIMITED Team Members & Organization Seats (No $20/seat tax)
- Private VPC & On-Premise air-gapped installation support
- Custom TensorRT-LLM quantization pipeline (FP8 / AWQ / Marlin)
- Single Sign-On (SSO: Okta, Azure AD, SAML, Google Workspace)
- Dedicated Solutions Architect & SLA support
- Zero Egress Fees on unlimited model weights transfer
- Saves €4,800 - €24,000 / year compared to Hugging Face Enterprise
Hugging Face Seat Tax vs. NEXUS AI Flat Enterprise
Hugging Face penalizes you with $20/month for every engineer you hire. NEXUS AI charges €149/mo flat with UNLIMITED seats.
Penalizes team growth. Every new engineer or contractor increases your monthly subscription tax.
Flat fixed rate. Add 5 or 500 engineers with zero extra per-seat fees or contract renegotiations.
Direct bottom-line profit saved from day one.
How Much Are You Overpaying for Cloud GPUs?
Vanilla PyTorch inference wastes up to 72% of GPU compute in memory-bandwidth stalls. See what TensorRT-LLM saves you.
Standard Python transformers serving with high memory stalls, idle GPU waste, and slow cold-starts.
Continuous PagedAttention batching + FP8 Tensor Core saturation cuts required GPU node count by 68%.
Exact capital saved to hire more researchers or expand model fine-tuning budgets.