onpremisegpukubernetesvllminfrastructurellmai

On-Premise LLM Infrastructure Guide - From GPU Servers to Kubernetes Deployment

A guide for building LLM infrastructure on-premise. Covers GPU server configuration, network design, Kubernetes + vLLM deployment, monitoring, security, and cost analysis.

Data DynamicsApril 16, 20264 min read

Growing numbers of enterprises need to run LLMs on-premise for data security, regulatory compliance, and cost efficiency. This post covers GPU server configuration through production Kubernetes deployment.

1. Why On-Premise LLM?

Reason	Description	Industries
Data security	Enterprise data never leaves premises	Finance, healthcare, defense
Regulatory compliance	Data sovereignty, GDPR, privacy laws	Public sector, finance, healthcare
Cost efficiency	Cheaper than API at scale	Large-scale services
Low latency	Local network for minimal delay	Real-time services
Customization	Free deployment of Fine-Tuned models	All industries

2. GPU Server Configuration

GPU Selection Guide

GPU	VRAM	FP16 Performance	Price (approx.)	Suitable Models
RTX 4090	24 GB	82.6 TFLOPS	~$1,600	7-8B (Q4), development
A100 40GB	40 GB	77.97 TFLOPS	~$10,000	7-13B (FP16)
A100 80GB	80 GB	77.97 TFLOPS	~$15,000	70B (Q4), 13B (FP16)
H100 80GB	80 GB	267 TFLOPS	~$30,000	70B (FP16), best perf
L40S	48 GB	91.6 TFLOPS	~$7,000	7-13B (FP16), cost-efficient

Server Configurations

[Dev/Test Server]
CPU: AMD EPYC 7543 (32-core), RAM: 256 GB
GPU: RTX 4090 × 2, Storage: NVMe 2TB

[Production Server (small)]
CPU: AMD EPYC 9354 × 2, RAM: 512 GB DDR5
GPU: A100 80GB × 4, Storage: NVMe 4TB RAID, Network: 25GbE

[Production Server (large)]
CPU: Intel Xeon × 2, RAM: 1 TB DDR5
GPU: H100 80GB × 8 (NVLink), Storage: NVMe 8TB, Network: 100GbE + InfiniBand

3. Kubernetes + vLLM Deployment

Helm Chart

# values.yaml
replicaCount: 2
image:
  repository: vllm/vllm-openai
  tag: latest
model:
  name: meta-llama/Llama-3.1-8B-Instruct
  maxModelLen: 4096
  gpuMemoryUtilization: 0.9
resources:
  limits:
    nvidia.com/gpu: 1
  requests:
    memory: 32Gi
    cpu: 8
autoscaling:
  enabled: true
  minReplicas: 2
  maxReplicas: 8
  targetGPUUtilization: 70

helm install vllm ./charts/vllm -f values.yaml -n llm-serving
kubectl get pods -n llm-serving

4. Monitoring

Key Metrics

Category	Metric	Alert Threshold
GPU	GPU utilization	> 95% (5min sustained)
GPU	GPU memory usage	> 90%
GPU	GPU temperature	> 85°C
Serving	Request latency (P95)	> 3s
Serving	Error rate	> 1%
System	CPU usage	> 80%

5. Security

Area	Measure	Implementation
Network isolation	GPU servers in separate VLAN	Firewall rules
API authentication	JWT/API Key	API Gateway
TLS encryption	Encrypt all communication	cert-manager
Access control	RBAC-based model/tool access	K8s RBAC
Audit logging	Log all requests/responses	ELK Stack
I/O filtering	Apply guardrails	Proxy service

6. Cost Analysis

On-Premise vs Cloud vs API

[Monthly cost for 1M inferences (7B model)]

Cloud API (GPT-4o-mini):     ~$750/month
Cloud GPU (A100 × 1):        ~$2,600/month
On-Premise GPU (A100 × 1):   ~$1,300/month (3-year amortization)

→ On-premise cost-efficient above 3M inferences/month
→ Choose on-premise when data security is mandatory

Break-Even Point

Monthly Inferences	API	Cloud GPU	On-Premise
100K	$75	$2,600	$1,300
1M	$750	$2,600	$1,300
5M	$3,750	$5,200	$1,300
10M	$7,500	$10,400	$2,600

Note: Consider on-premise investment when expecting 3M+ monthly inferences. However, initial investment (server purchase) and operations staff are required.

References

NVIDIA GPU Operator — https://docs.nvidia.com/datacenter/cloud-native/gpu-operator/
vLLM Documentation — https://docs.vllm.ai/
Kubernetes Documentation — https://kubernetes.io/docs/

— Data Dynamics Engineering Team