I Ran DeepSeek V4 Vision Locally For 24 Hours... Local AI is Amazing! 🤯

I Ran DeepSeek V4 Vision Locally For 24 Hours... Local AI is Amazing! 🤯

by xCreate · View the channel on PSI

Applied Sciences & Engineering AI — Models & Research UYQArtificial intelligence (AI)UYQFGenerative artificial intelligence
🎙 xCreate 👥 26K 📅 September 4, 2026 ⏱ 19 min 👁 10K 📄 expert opinion 🧭 2026-09-10
Available in: English (current) Français

Keywords

DeepSeeklocal AIvision modelgame generationbenchmarks

Summary

The video is a practical test of the DeepSeek Flash Vision EXP model, which integrates multimodal capabilities. The creator runs it locally on a Mac Studio and tests various tasks including image-to-game conversion, OCR, 3D generation, and coding benchmarks. The model performs impressively in many areas, such as generating playable games from screenshots, creating photorealistic faces, and handling canvas animations. The video compares different thinking modes (disabled, low, high, max) and notes that max thinking sometimes loops but can be fixed with temperature adjustments. The creator also tests logic and math questions, noting some improvements over previous versions. Overall, the model is praised for its efficiency and speed, though some outputs are basic or require fixes. The video is informative for those interested in running powerful vision-language models locally.

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Critical Evaluation

Value of the Information & Strength of the Argument

The value of the video lies in its real-world demonstrations of the model’s capabilities, providing visual proof of how the model handles complex tasks like creating 3D games from images and understanding visual context. However, the argumentation is largely anecdotal, based on personal impressions and ad-hoc tests rather than systematic evaluation. The creator does not compare with other models under controlled conditions, and some tests have dependencies on parameters like temperature. Nevertheless, the variety of tasks gives a good sense of the model’s strengths and weaknesses.

Scientific Rigor, Source Quality, Title Accuracy

The scientific rigor is low; the video does not provide a detailed methodology, and benchmarks are often reported without precise metrics. The main source cited is the HuggingFace model page, which is legitimate, but the testing procedure is not standardized. The title slightly overstates the duration (‘24 hours’) and misnames the model, but this does not heavily affect the content’s usefulness. The creator does mention that some issues like looping occur and how to mitigate them, showing some transparency.

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Title / Content Match

The title captures the focus on local AI testing, but the '24 hours' duration is not mentioned in the video, and the model is actually called DeepSeek Flash Vision EXP, not V4 Vision.

Quality & Reliability

7/10

The video offers a hands-on evaluation of the model with many examples, but the testing is informal and lacks controlled benchmarks, which somewhat limits reliability.

Chapters

Cited Sources

External References

Contribution & Novelties

This video contributes by demonstrating the practical capabilities of a recently released vision-language model, DeepSeek Flash Vision EXP, when run locally. It shows that the model can generate playable games, OCR text, and produce high-quality images with relatively low token usage compared to other models. The video also highlights the importance of thinking modes and temperature settings for achieving optimal results. This is valuable for practitioners who want to evaluate the model for local deployment.

Pour aller plus loin :

  • DeepSeek — Background on the DeepSeek AI company and models.
  • MLX — Apple’s machine learning framework used for running models on Apple silicon.
  • Procedural generation — Technique used in the generated games and assets.

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Radar Profile

The radar scores indicate a high level of technical depth and information quantity, but moderate reliability and quality. This suggests the video is rich in demonstrations but lacks rigorous scientific backing. The reliability score is lower due to the informal testing methodology.

Reliability 6/10