 | Just got a DGX Spark set up today and starting to configure it for local LLM inference. Plan is to run: as a local API backend for an application I’m building (education / analytics use case, trying to keep everything local/private). I’ve mostly been working with cloud GPUs up to now, so this is my first time running something like this fully on-prem. A few things I’m curious about: Would appreciate any insights from people running similar setups. [link] [comments] |
DGX Spark just arrived — planning to run vLLM + local models, looking for advice
Reddit r/LocalLLaMA / 4/15/2026
💬 OpinionDeveloper Stack & InfrastructureSignals & Early TrendsTools & Practical Usage
Key Points
- A user recently set up a DGX Spark for on-prem local LLM inference and plans to use vLLM with PyTorch and Hugging Face models as a private API backend.
- They are seeking community guidance on which models run efficiently on this specific unified-memory hardware configuration.
- The user asks for vLLM tuning advice tailored to unified memory systems, including practical configuration considerations.
- They want realistic expectations for throughput and performance compared with what they previously saw on cloud GPUs.
- The post is effectively a request for field-tested best practices for deploying and scaling local model inference on DGX Spark.
