PrismML — Introducing Ternary Bonsai: Top Intelligence at 1.58 Bits
New quantization method slashes model size by 95% while maintaining near-original performance.
PrismML's breakthrough research, dubbed Ternary Bonsai, introduces a radical new approach to model compression. Unlike traditional 4-bit or 8-bit quantization, this method represents each model weight with just 1.58 bits on average, using a ternary system of values (-1, 0, +1). The result is a staggering compression ratio, shrinking a 70-billion-parameter model from roughly 140GB down to just 7GB. Crucially, this isn't just about storage; the technique maintains over 99% of the original model's performance on complex reasoning and coding benchmarks, a feat previously thought impossible at such low bit-depths.
The implications for deployment are profound. Ternary Bonsai effectively democratizes access to frontier models. A Llama 3 70B model, once requiring multiple high-end GPUs, can now run on a single consumer-grade GPU or even a high-end laptop CPU. This drastically reduces the cost and hardware requirements for businesses and developers looking to integrate powerful, private AI into their applications. The method also promises significant energy savings and faster inference times, making advanced AI more sustainable and responsive for real-world use cases.
- Achieves extreme 1.58-bit per weight quantization, compressing models by up to 95%.
- Maintains >99% of original model accuracy on reasoning and coding tasks.
- Enables running 70B-parameter models like Llama 3 on consumer hardware.
Why It Matters
This slashes the cost and hardware barrier to deploying powerful, private AI, enabling local use of frontier models.