Enterprise AI & Private LLM Infrastructure

Select the hardware tier that matches your parameter size and inference/training requirements.

Server Configurations

Dell C4130 (SXM2)

$4,000+

Best For: Private LLMs (Llama-3 8B/70B), Research

The ideal entry point for high-performance private AI. Utilizes the SXM2 socket for high-bandwidth NVLink communication (300 GB/s). Perfect for quantized 70B models.

GPU Socket: NVIDIA SXM2
Configuration: 4x Tesla V100
Total VRAM: 64GB - 128GB

Configure C4130

NVIDIA DGX-1

$12,000+

Best For: Heavy Training, Multi-User Service

The gold standard for deep learning. Doubles the density of the C4130 using SXM2 architecture with a "Cube Mesh" NVLink topology for massive parallel throughput.

GPU Socket: NVIDIA SXM2
Configuration: 8x Tesla V100
Total VRAM: 128GB - 256GB

View DGX-1

NVIDIA DGX-2

$40,000+

Best For: "Unsharded" Huge Models, Science

A massive leap in AI architecture. Uses the SXM3 socket and NVSwitch to let 16 GPUs communicate simultaneously at 2.4 TB/s, creating a unified 512GB memory pool.

GPU Socket: NVIDIA SXM3
Configuration: 16x Tesla V100
Total VRAM: 512GB (Unified)

View DGX-2

HGX A100 Platform

$85,000+

Best For: Commercial Production, BF16 Training

Commercial-grade production capability using the SXM4 socket. Features Ampere architecture with TensorFloat-32 and BF16 support for unprecedented speed.

GPU Socket: NVIDIA SXM4
Configuration: 8x A100
Total VRAM: 320GB - 640GB

View HGX A100

Supermicro SYS-4029GP

From $7,000+

Best For: High-Density DIY Builds (Bring Your Own GPU)

The ultimate DIY host. This 4U chassis supports 8x Double-Width PCIe GPUs. Features Dual Xeon Scalable processors, up to 6TB RAM, and massive airflow for running RTX 3090/4090 or Tesla cards.

Form Factor: 4U Rackmount
Capacity: 8x PCIe 3.0 x16 GPUs
Processor: Dual Xeon Scalable

Configure Supermicro

Dell PowerEdge R740

$4,000+

Best For: Edge Inference, RAG Pipelines

Standard enterprise infrastructure for inference. Accepts standard PCIe accelerators (A10, T4, A2). A cost-effective solution for deploying tasks like RAG pipelines or chatbots.

Type: Standard PCIe 3.0
Capacity: Up to 3x Double-Width
Cooling: Standard Air

Configure R740

Setup & Demonstration Tutorials

Advanced Proxmox GPU Passthrough (16x V100 SXM3)

A deep dive into configuring Proxmox VE for extreme GPU density. This tutorial demonstrates the complex IOMMU and PCIe passthrough setup required to assign 16 individual NVIDIA V100 SXM3 GPUs to virtual machines on a DGX-2 class server.

Live Coding: Qwen3 Coder & Cline on Dell C4130

Experience a real-world private LLM coding workflow. We demonstrate editing code directly on the server using the Cline extension in VS Code, powered privately by the Qwen3 Coder model running on a Dell C4130 with 4x V100 GPUs.

Local RAG Demo: PDF Analysis with OpenWebUI

See how to perform secure, local document analysis. This demo runs OpenWebUI locally to ingest and analyze a PDF file, using the Qwen3 model hosted on a Dell C4130 (4x16GB V100 SXM2) to answer questions based on the document content.

Visual Analysis & Code Generation (VLM)

A two-part demonstration of private VLM capabilities on the Dell C4130. Part 1 uses Qwen 8B VL on 16GB V100s for image analysis. Part 2 showcases heavy code generation using Qwen3-Coder running on 32GB V100 SXM2 variants.

vLLM Launch Command Reference

A Configuration for 4x 16GB V100 Cards

Reasoning Model (Qwen3-14B) Efficient 4-bit quantization using bitsandbytes. Ideal for general logic tasks.

vllm serve unsloth/Qwen3-14B-unsloth-bnb-4bit --port 8000 --served-model-name "qwen3-14b" --quantization bitsandbytes --gpu_memory_utilization 0.9 --pipeline_parallel_size 4

Coding Model (Qwen3-Coder-30B) GPTQ Int8 Quantization with Expert Parallelism enabled for MoE.

vllm serve QuantTrio/Qwen3-Coder-30B-A3B-Instruct-GPTQ-Int8 --port 8000 --served-model-name Qwen3-Coder-30B-A3B-Instruct-GPTQ-Int8 --enable-expert-parallel --gpu_memory_utilization 0.8 --tensor_parallel_size 4 --tokenizer "Qwen/Qwen3-Coder-30B-A3B-Instruct" --trust-remote-code --max-model-len 64000 --max-num-seqs 512 --swap-space 16

Visual LLM (Qwen3-VL-8B) Standard Instruct model for image analysis tasks.

vllm serve Qwen/Qwen3-VL-8B-Instruct --port 8000 --served-model-name Qwen3-VL-8B --gpu_memory_utilization 0.9 --tensor_parallel_size 4 --trust-remote-code --max-model-len 32000 --max-num-seqs 512

B Configuration for 4x 32GB V100 Cards

Huge Model (DeepSeek R1 Distill 70B) Running a massive 70B parameter model spread across 4x 32GB cards using 4-bit quantization.

vllm serve unsloth/DeepSeek-R1-Distill-Llama-70B-bnb-4bit --port 8000 --served-model-name DeepSeek-R1-Distill-Llama-70B-bnb-4bit --gpu_memory_utilization 0.9 --pipeline_parallel_size 4 --trust-remote-code --quantization bitsandbytes

Reasoning (Qwen3-30B-A3B) Larger context handling capabilities utilizing higher VRAM pool.

vllm serve Qwen/Qwen3-30B-A3B --port 8000 --served-model-name "qwen3-30b" --gpu_memory_utilization 0.9 --pipeline_parallel_size 4 --enable-expert-parallel

Coding (Qwen3-Coder-30B) Full performance configuration with larger context window and swap space.

vllm serve QuantTrio/Qwen3-Coder-30B-A3B-Instruct-GPTQ-Int8 --port 8000 --served-model-name Qwen3-Coder-30B-A3B-Instruct-GPTQ-Int8 --enable-expert-parallel --gpu_memory_utilization 0.8 --tensor_parallel_size 4 --tokenizer "Qwen/Qwen3-Coder-30B-A3B-Instruct" --trust-remote-code --max-num-seqs 512 --swap-space 16