Hire a vLLM Specialist in Salt Lake City

**The Problem: The VRAM Bottleneck.** When multiple users query a language model simultaneously, the 'KV Cache' (the memory storing the context of the conversation) fragments and exhausts the GPU's VRAM. The server crashes, or you are forced to rent an astronomically expensive secondary GPU. For Salt Lake City-based companies competing with Qualtrics for talent, this dynamic is especially acute.

**The Agitation: Prohibitive Unit Economics.** Running a default open-source model in production is often more expensive than just using OpenAI's API, defeating the entire purpose of owning your own model. The unit economics of AI SaaS die at the inference layer. In the Salt Lake City market specifically, utah's silicon slopes ecosystem has produced multiple unicorns (qualtrics, pluralsight, domo).

**The Solution: High-Throughput Inference (vLLM).** Slickrock.dev deploys inference specialists. We use vLLM, a state-of-the-art inference engine that treats the GPU's memory like a modern operating system handles RAM. By using 'PagedAttention', we eliminate memory fragmentation, allowing the exact same piece of hardware to serve 5x to 10x as many concurrent users.