The High-Performance
AI Engine & Model Hub
Discover, benchmark, and deploy 1,000,000+ open models with automated VRAM profiling, microsecond TTFT, and 1-click TensorRT-LLM & vLLM acceleration.
Can Your GPU Run This Model?
Eliminate Out-Of-Memory (OOM) crashes. Calculate precise weights, KV Cache memory, and generate 1-click vLLM / TensorRT runtimes.
docker run --gpus all -p 8000:8000 --ipc=host vllm/vllm-openai:latest \ --model meta-llama/Meta-Llama-3.1-8B-Instruct \ --max-model-len 8192 \ --quantization fp8 \ --gpu-memory-utilization 0.95
Trending Foundation Models
Live weights verified for NVIDIA TensorRT-LLM and vLLM deployment.
DeepSeek-R1
deepseek-ai
State-of-the-art open reasoning model matching OpenAI o1 on math, code, and logical deduction benchmarks.
Meta-Llama-3.1-70B-Instruct
meta-llama
Premier open-weights foundation model optimized for enterprise conversational AI, coding, and synthesis.
Meta-Llama-3.1-8B-Instruct
meta-llama
High-speed 8B instruction tuned model, fits easily on single consumer GPUs (RTX 4090 / 3090).
Mistral-Large-Instruct
mistralai
Flagship high-capacity multilingual model from Mistral AI with advanced reasoning and 128k context window.
FLUX.1-dev
black-forest-labs
Next-generation 12B parameter flow transformer delivering state-of-the-art visual generation and typography.
whisper-large-v3-turbo
openai
Ultra-fast speech transcription and translation model fine-tuned for real-time streaming audio pipelines.
Qwen2.5-72B-Instruct
Qwen
Top-tier coding, math, and STEM reasoning model competing neck-and-neck with proprietary frontier models.
stable-diffusion-3.5-large
stabilityai
Multimodal Diffusion Transformer model for photorealistic image generation and complex prompt following.
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.
How NEXUS AI Compares to Hugging Face
Why modern AI engineers and enterprise teams are migrating to hardware-optimized infrastructure.
| Capability | Hugging Face | NEXUS AI (High-Performance) |
|---|---|---|
| Model Catalog & Registry | Standard Git LFS repository | Global Catalog (1M+) + Hardware Profiled |
| GPU & VRAM Estimator | None (Trial and error / OOM crashes) | Built-in Real-time VRAM & KV Cache Engine |
| High-Throughput Engines | Generic Python serving (slow cold starts) | Native TensorRT-LLM & vLLM (5.1x faster) |
| NVIDIA Hardware Alignment | Generic hardware support | Optimized for Blackwell, Hopper, Ada Lovelace |
| Quantization Matrix | Community files scattered manually | Automated FP8 / 4-bit AWQ / GGUF pipeline |
| Private Enterprise Model Vault | $20/user/mo + high cloud egress | Zero Egress Fees (Cloudflare R2 + Private VPC) |
Ready to Cut GPU Latency & Cloud Bills by 68%?
Join thousands of AI researchers, foundation labs, and enterprise engineering teams deploying with NEXUS AI.