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I will deploy and host your llm or ai app on the cloud
Senior Software Engineer POS, ERP and AI Powered Web Mobile Solutions
Senior Software Engineer building scalable POS, ERP, MVP, web & mobile apps with a focus on performance, security & maintainability. Skilled in microservices, API development, database optimization, s...
About this Gig
Need your LLM deployed to production, fast, optimized, and cost-efficient?
You're in the right place!
I deploy and configure open-source LLMs on GPU cloud servers and Kubernetes, with tuned inference engines for fast responses and lower GPU costs.
What I Offer:
- LLM deployment (Llama, Mistral, Qwen, DeepSeek, Gemma)
- Inference engine configuration (vLLM, TGI, Ollama, Triton)
- Model quantization (AWQ, GPTQ, GGUF)
- GPU optimization & cost reduction
- OpenAI-compatible LLM API setup
- Private & self-hosted LLM deployment
- RAG & AI app backend deployment
- Kubernetes LLM cluster with auto-scaling
- Docker & CI/CD for LLM apps
- Monitoring & performance tuning
Tech Stack:
Inference: vLLM || TGI || Ollama || NVIDIA Triton || TensorRT-LLM || llama.cpp || SGLang
Models: Hugging Face || Llama || Mistral || Qwen || DeepSeek
Cloud & GPU: AWS || Google Cloud || Azure || RunPod || Lambda Labs
Containers: Docker || Kubernetes || Helm
Monitoring: Prometheus || Grafana
Why Choose Me?
- Fast delivery
- Free consultation
- Optimized for speed & cost
- Full documentation
- Post-deployment support
Let's get your LLM live today!
FAQ
Which LLMs can you deploy?
Any open-source model from Hugging Face, including Llama, Mistral, Qwen, DeepSeek, Gemma, and Phi, as well as fine-tuned or custom models you provide.
What does inference engine configuration include?
Setting up and tuning vLLM, TGI, Triton, or Ollama for your model: GPU memory, batching, context length, KV cache, tensor parallelism, and quantization, so you get faster responses at lower cost.
Which inference engine should I use?
vLLM for high-throughput APIs, TGI for Hugging Face models, Triton or TensorRT-LLM for maximum NVIDIA GPU performance, and Ollama or llama.cpp for smaller or CPU setups. I'll recommend the best fit.
Can you reduce my GPU costs?
Yes. Through quantization (AWQ, GPTQ, GGUF), batching, and right-sizing GPUs, I can often run the same model on cheaper hardware with similar quality.
Will the API work like OpenAI's?
Yes. I can set up an OpenAI-compatible API endpoint, so you can switch your existing app from OpenAI to your own LLM by changing just the base URL and API key.
Who pays for GPU and cloud costs?
GPU and hosting costs are billed by the provider (AWS, GCP, Azure, RunPod, etc.) to your own account. I'll help you choose the most cost-effective GPU for your model and traffic.
Is my model and data private?
Yes. Your LLM runs on your own servers, so prompts and data never go to third-party APIs. I also secure the endpoint with API keys, SSL, and firewall rules. I can sign an NDA if needed.
What do I need to provide before we start?
The model you want (or your requirements), access to your cloud or GPU account, expected traffic, and any domain you want to use. If you're unsure, I offer a free consultation.
Can you deploy a model I fine-tuned?
Yes. Share your model weights or Hugging Face repo, and I'll deploy and optimize it with the right inference engine.
Not sure which package fits your project?
Message me before ordering. I'll review your model and traffic needs and recommend the right package or send a custom offer.
