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I will deploy local ai with vllm on any cloud or your own server
Software Engineer, Reader and Resercher
I got into tech seven years ago. I built data pipelines first. Then I worked on sensor fusion for autonomous vehicles(ego). After that, I worked for American companies in healthcare and other regulate...
About this Gig
If you use LLMs at scale or you're in a regulated industry or you care about privacy then you must own the AI. I deploy and optimize LLMs/vision-llms/deep learning models entirely on YOUR infrastructure.
WHAT YOU GET
- vLLM, SGLang, Ollama, llama.cpp and MLX serving
- OpenAI/Anthropic compatible API endpoint
- Quantization tuned to your hardware
- P99 TTFT, throughput and batch-size tuning to your SLOs
- KV-cache and prefix caching
- Speculative decoding and chunked prefill for speed
- NVIDIA CUDA, AMD ROCm, Apple Silicon or CPU-only
- AWS, GCP, Azure, RunPod, Lambda or your own server
- Load-tested: P50/P99 latency, tokens/sec, max concurrency
ALSO AVAILABLE
- In-browser AI with WebLLM (MLC)
- On-device iOS/Android and faster-whisper voice
Fast inference is a balance: TTFT, tokens/sec and concurrent users compete for the same VRAM. I tune to your SLO.
FAQ
Which inference engine should I choose?
Depends on your load: Ollama for small teams (~10 users), vLLM for production throughput, SGLang for multi-turn/agent workloads. I assess your case before you order.
Which hardware do you support?
NVIDIA CUDA, AMD ROCm, Vulkan, Apple MLX and CPU-only servers. The generation of the hardware matters. Ampere, Hopper and Blackwell differ.
Can you deploy to mobile or in a browser?
Yes. In-browser via WebLLM. On-device iOS/Android for supported models only.
How fast will my model be?
Speed is a three-way tradeoff: time-to-first-token, tokens per second and how many users fit in memory. You can't max all three. Tell me your constraints and I will configure the engine to match them by finding the right balance.
