Healthcare, defense, financial, and high-volume SaaS organizations deploying open-weight LLMs (Llama 3.3, Qwen 2.5, DeepSeek, Mistral) inside private VPCs frequently search to hire a freelance vLLM, TGI, Triton & private GPU inference programmer. I am Aldian Fazrihady, a Senior Freelance AI Infrastructure & Application Engineer.
Core Self-Hosted GPU Inference & Fine-Tuning Capabilities
- High-Throughput vLLM & SGLang Serving: Configuring PagedAttention, continuous batching, speculative decoding, prefix caching, and AWQ/FP8 quantization on NVIDIA A100/H100/L40S GPUs across AWS, GCP, and RunPod.
- LoRA / QLoRA Fine-Tuning & Multi-LoRA Serving: Training domain-specific adapters with Unsloth / Axolotl / Hugging Face TRL and serving dozens of tenant-specific LoRA adapters dynamically from a single base model in vLLM.
- OpenAI-Compatible Gateway & Auto-Scaling: Exposing private vLLM endpoints behind LiteLLM / Envoy gateways with scale-to-zero GPU autoscaling.
Frequently Asked Questions
When does self-hosting open-weight models on vLLM beat commercial LLM APIs on cost?
When sustained token volume exceeds ~20–30 million tokens per day or when strict data residency/HIPAA rules prohibit sending sensitive payloads to external API providers.
How do I hire you for private GPU LLM deployment?
Hire me via Upwork.