Host Llama-3 70B
on your own GPU VMs.
Deploy Llama-3 70B directly to your own cloud instance with active scaling and OpenAI-compatible API layers.
Llama-3 70B Overview & Capabilities
Meta's flagship open-weights large language model, optimized for highly complex dialogue, reasoning, coding, and instruction-following tasks.
Optimized GPU Kernels
vxcloud packages models with highly optimized vLLM or Ollama layers, yielding maximum token-generation speed.
100% Data Privacy
Run inference within your own VPC boundary. No queries, training prompts, or logs flow to third-party endpoints.
OpenAI-Compatible APIs
Exposes standardized endpoints, allowing you to drop your private backend straight into existing LangChain/LlamaIndex code.
Quantized Weights support
Supports AWQ and GPTQ quantization out of the box, reducing GPU VRAM memory targets by up to 75%.
Dynamic Scaling
Spin up additional replica VMs behind load balancers to distribute high-volume client completions.
Flat Compute Costs
Ditch per-token query markups. You only pay for the VM run hours directly, cutting API expenses at scale.
Deployment Hardware Specifications
AI models demand heavy GPU memory profiles. Review these suggested minimum configurations to prevent Out-Of-Memory compilation failures.
- Model Parameters: 70 Billion
- Minimum recommended VRAM: 140 GB
- Recommended GPU Layout: 4x NVIDIA A10G (24GB each) or 2x A100 (80GB each)
- Suggested Cloud Instance: g5.12xlarge (AWS) or a2-ultragpu-2g (GCP) (Meta AI optimized)
One-Command GPU Deployment Setup
Run this command inside your terminal session to provision GPU resources and deploy the OpenAI-compatible vLLM API layer.
vxcli deploy model llama-3-70b \
--cloud aws --region us-east-1 \
--instance-type g5.12xlarge \
--gpu-count 4 \
--engine vllm \
--api-key $(vault:get-openai-key) \
--enable-ssl --domain llama.example.comLlama-3 70B Hosting FAQ
Deploy Llama-3 70B now
Experience secure, private, and fast text generation on your own cloud nodes.