Let's Run the New #1 Local AI by China's LARGEST Company 🤯 | Hy3 TESTED
Keywords
Summary
120 words
Critical Evaluation
Value of the Information & Strength of the Argument
The video delivers valuable first-hand insights into running a large local AI model, with concrete performance metrics (tokens/sec, memory usage) and qualitative comparisons. The argumentation is solid, based on direct testing, though it occasionally lacks rigor (e.g., single runs, potential variability). The creator clearly explains the reasoning level effects and provides a fair verdict: Hy3 is promising but not universally better than smaller models like Qwen 27B.
Scientific Rigor, Source Quality, Title Accuracy
The video cites official sources via the Hugging Face link for the model and the Inferencer platform. It also references companion videos for context. The title accurately reflects the content. The presentation is informal but transparent about limitations (preview version, single-hardware testing). No formal citations of academic papers are made, but the description provides relevant links.
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Title / Content Match
Title accurately reflects the content: testing a new local AI model (Hy3 preview) from Tencent, with hands-on experiments and comparisons.
Quality & Reliability
8/10
The video provides hands-on testing with quantitative data (token counts, speeds, memory usage) and honest comparisons, but relies on single-run informal benchmarks and some cloud-vs-local inconsistencies.
Key Moments
Markers derived by PSI from the transcript: the creator did not define chapters.
- Introduction to Hy3 preview, its parameters, and comparison plan.
- Math test with reasoning off and low, showing token counts and errors.
- High reasoning math test takes over an hour, cloud version fails.
- 3D solar system demo with different reasoning levels, local vs cloud comparison.
- Flappy Bird comparison: Hy3 vs Qwen, Qwen produces more playable version.
- 3D city simulation: Hy3 high reasoning yields impressive results; Qwen uses instancing for speed.
- Logic tests: trolley problem and car wash, showing reasoning level differences.
- Agentic research task: fetching Wikipedia on Tencent acquisition, both models succeed.
Cited Sources
- Hy3-preview-MLX-9bit on Hugging Face — Official model repository for the quantized version used in local tests.
- Inferencer App — Platform used for running and deploying local AI models.
- Kimi K2.6 companion video — Related model comparison video.
- GLM 5.1 companion video — Related model comparison video.
- Expert Controls companion video — Related video on expert controls.
Concurring Sources
- Hugging Face model card — Provides official details on the model architecture and license (Aether).
Contribution & Novelties
The video provides a practical, hands-on evaluation of a newly released Chinese AI model (Hy3 preview) for local deployment, comparing it with a smaller model (Qwen 27B) across multiple task types. It highlights the trade-offs between model size, reasoning depth, and output quality, and demonstrates the importance of quantization for local feasibility. The findings are useful for AI enthusiasts and researchers considering local deployment of large models.
Pour aller plus loin :
- Mixture of Experts — Explains the architectural basis of MoE models like Hy3.
- Quantization in AI — Discusses how quantization reduces model size and its effects.
- Agentic AI — Overview of AI agents and tool use, which the video tests via web research.
- Tencent AI — Context on Tencent’s AI efforts and the company behind Hy3.
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Radar Profile
The radar profile shows a balanced video with high quantitative information and technical depth, but slightly lower quality due to informal testing methods. The reliability is moderate, reflecting the single-hardware and preview-model context.