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Explore AI Billy Bass v1.1 🤖🦜 with 158,065 views, 10,118 likes, and 514 comments. Experience the impact of this video content that has captured audience attention.

This time a Gemma 4 12B instance is embodying in Billy Bass—using a single 32GB Intel Pro B70 GPU. Which pushed the latency down further, while only needing 32GB VRAM instead of the ~80+GB required for the 30B class models like Muse glimmer (what the first billy bass video had inhabiting the animatronic), Qwen 3.8 27B, and Gemma 4 31B. The trancription and TTS are running on a seperate 4070 Ti Super, but could be run on basically any old card or even squezzed onto the primary mode card with a hit to latency (and a bit of context window reduction to squeeze the better tts model on) I did not get around to drafting and testing the full system-prompt/instructions yet, which is partly why this was so ridiculously clunky even compared to the first test (along with being a much smaller model this time around). We'll definitely be adding is along the lines of 'don't speak unless spoken to with specific trigger phrase(s) 😅. For those interested in running something like this, we managed to acquire a second Intel A770 16GB; when it arrives we will have two of those, allowing us to run the exact same 9-12b class instances at full quality entirely in VRAM for ~$600 total in GPU's, the rest of the system nearly irrelevant—though a quirk with intel gpu's necessitates a DDR4 board (even an old ddr3 board with 2 pcie slots would be enough for AMD or Nvidia cards) meaning this can be done for ~$1K all-together. If you accept quantization you can run it with a significantly less even, but I generally refuse to quantize in any scenario, so would rather run uncompressed 9b class than Q6 30b class (both of which can fit in 32GB).

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