Implementation Blueprint

Prerequisites

# Secure a multi-GPU environment
# Check connected GPUs
nvidia-smi -L

# Install vLLM with multi-GPU support
pip install vllm

Deployment with vLLM (Tensor Parallelism)

python -m vllm.entrypoints.openai.api_server \
    --model meta-llama/Llama-2-70b-chat-hf \
    --tensor-parallel-size 2 \
    --host 0.0.0.0 \
    --port 8080

Kubernetes Production Deployment (Helm)

# values.yaml for vLLM helm chart
replicaCount: 1
image:
  repository: vllm/vllm-openai
  tag: latest

env:
  - name: HUGGING_FACE_HUB_TOKEN
    valueFrom:
      secretKeyRef:
        name: hf-token
        key: token

resources:
  limits:
    nvidia.com/gpu: 2 # Number of GPUs per pod
  requests:
    nvidia.com/gpu: 2

extraArgs:
  - "--model=meta-llama/Llama-2-70b-chat-hf"
  - "--tensor-parallel-size=2"

Scaling Strategy

• Pipeline Parallelism: If the model still doesn't fit or you need even higher throughput, use pipeline parallelism to split the model by layers across nodes.
• Quantization (AWQ/GPTQ): Use quantized versions to fit the 70B model into 48GB VRAM cards (like 2x RTX 6000 Ada), significantly reducing hardware costs.
• Pre-fill Cache: Use vLLM's prefix caching if you have large system prompts that are reused across many requests (common in enterprise RAG).

Backup & Safety

• Cold Storage: Maintain copies of the 70B weights in a local high-speed NAS to avoid 140GB downloads during pod restarts.
• Semantic Filtering: Use an LLM-based filter (like Llama Guard) to inspect the 70B outputs for safety and policy compliance.
• Resource Quotas: Implement strict GPU resource quotas to prevent a single service from starving the rest of your AI infrastructure.