Running LLMs locally: Practical LLM Performance on DGX Spark — Mozhgan Kabiri chimeh, NVIDIA
Apr 10, 2026 · 10:16
NVIDIA’s Mozhgan Kabiri Chimeh demonstrates that running LLMs locally on the DGX Spark workstation, powered by the GB10 Grace Blackwell superchip and 128GB unified memory, achieves practical performance for models up to 14B parameters. Using a reproducible vLLM benchmarking methodology, she shows that the 14B NVFP4 quantized model delivers 20.19 tokens per second and a time-to-first-token 3.4× faster than the unoptimized 14B base model. The DGX Spark supports the same NVIDIA AI software stack as production environments, enabling local development, fine-tuning, and privacy-sensitive workloads before scaling to the cloud. NVFP4 quantization is highlighted as critical for balancing intelligence and throughput on single-system setups.
