CES 2025 and Running DeepSeek R1 Distills on an RTX 5090

This was my first CES, and I loved seeing so much technology centered on AI. NVIDIA invited me, paid for my hotel and gave me an RTX 5090, which I put in the machine beside me. NVIDIA partnered with me on this video. After a tour of the show, I will use the card to run DeepSeek R1 distilled reasoning models locally.

The giant farming equipment brought out the kid in me: the wheels on one dump truck were taller than I am. John Deere showed autonomous machines with arrays of cameras and sensors. I was excited to see AI being applied to farming, with NVIDIA technology powering the equipment.

Autonomous vehicles had a large section of CES, including Waymo. I am from the Los Angeles suburbs, so I rarely see its cars at home, although I recently saw them everywhere in San Francisco. Unitree also showed quadruped and humanoid robots, and there were many other robots at the show.

At an NVIDIA editor day, around 100 to 200 invitees got a preview of new technology, including the RTX 5090. NVIDIA also showed Blueprints on build.nvidia.com: application frameworks developers can extend. I saw examples involving CrewAI and a NotebookLM-like application, with options for local execution and services such as ElevenLabs voice.

One demonstration showed a Windows assistant that can answer questions about the screen and perform computer actions. Asked what app was open, it identified Adobe Photoshop and an image called night market.PSD.

Another demonstration combined Blender and ComfyUI for 3D-guided image generation. A demonstrator explained that a simple scene could guide composition while a changed text prompt produced a different setting, such as the canals of Venice. That could let an artist explore a change in creative direction without rebuilding the composition.

Another project reminded me of The Sims, with AI-driven characters living in a 3D world. It brought to mind an earlier research project about simulated humans. I was told the project could be downloaded and run locally.

I also discuss my understanding that generative AI can produce additional frames between conventionally rendered frames on the RTX 5090. I am uncertain about the technical details. NVIDIA describes DLSS Multi Frame Generation as generating additional frames using AI; it is a frame-generation technique, not evidence that ray tracing itself is replaced.

I believe games and software could eventually become more predictive, with AI operating over a CRUD database rather than a conventional game engine. That is my prediction about a possible future, not a demonstration of that architecture at CES.

For the local test, the RTX 5090 has 32 GB of VRAM. I downloaded two GGUF models: DeepSeek-R1-Distill-Llama-8B at about 4.92 GB and DeepSeek-R1-Distill-Qwen-32B at about 18.71 GB. Both use Q4 quantization and, in my setup, are fully offloaded to the GPU.

I ask the 8B model to write Tetris in Python. It reasons through the task before producing code. The displayed run reports 79.15 tokens per second, 2,232 tokens and 0.03 seconds to first token; I say I have seen as much as 90 tokens per second. I show the generated response, but do not run or validate the game in this demonstration.

I load the 32B Qwen distill and give it the same prompt. It is slower, and I estimate 40–50 tokens per second while watching it generate; that is not a displayed completed benchmark. I like being able to download and run models locally. Local processing can reduce reliance on a remote service, though privacy and security depend on the software, connections and configuration.