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In an NVIDIA-partnered 2025 short, Matthew Berman describes gaming on an RTX 5090 and tests an 8-billion-parameter DeepSeek R1 distilled model in LM Studio. He reports 78 tokens per second for one run; the short does not establish performance across models or workloads. Berman also discloses a small investment in LM Studio.
- 1Partnered hardwareBerman says NVIDIA provided the RTX 5090, invited him to CES and partnered on the video.
- 2Gaming firstHe says he tried Cyberpunk at maximum settings and enjoyed how Path of Exile 2 looked.
- 3Local AI generationAn 8B distilled DeepSeek R1 model generates Python Snake code in LM Studio, with a reported 78 tokens per second on that run.
Conclusiones
- NVIDIA supplied the RTX 5090 and partnered with Berman on the video.
- Berman runs an 8B DeepSeek R1 distilled model locally in LM Studio.
- He asks for a Python Snake game and reports 78 tokens per second for that run.
- The short does not provide a controlled benchmark across hardware, models or configurations.
Lee el texto editado por capítulos
Games and a local model test
Berman introduces the RTX 5090 and his NVIDIA partnership. He reports impressions from games and the speed of a single local AI generation.
NVIDIA sent me an RTX 5090, its high-end next-generation GPU, and invited me to CES, where Jensen Huang introduced it. NVIDIA also partnered with me on this video. My first test was gaming: I say Cyberpunk ran at maximum settings without a problem, and Path of Exile 2 looked gorgeous.
For AI, I load the 8-billion-parameter DeepSeek R1 distilled model locally in LM Studio and ask it to write Snake in Python. It shows its reasoning and produces code; I report 78 tokens per second for this run. This single example does not establish a general hardware ranking or show whether the game was tested. I disclose a small investment in LM Studio in the video description.
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