PrismML introduces 1-bit Bonsai for edge-ready LLM deployment

A March 31, 2026 post in r/LocalLLaMA brought PrismML’s new 1-bit Bonsai models into the mainstream local-inference conversation, picking up 102 points and 43 comments. The linked announcement is ambitious: PrismML says it has built the first commercially viable end-to-end 1-bit LLM family, aimed at phones, laptops, robots, and secure edge environments rather than large clusters.

In PrismML’s official write-up, 1-bit Bonsai 8B uses 1-bit weights across embeddings, attention, MLP layers, and the LM head with no higher-precision escape hatches. The company says the model has 8.2 billion parameters but occupies only 1.15GB, roughly 12x to 14x smaller than comparable 16-bit 8B models. PrismML reports 136 tokens per second on an M4 Pro Mac, 440 tokens per second on an RTX 4090, and about 44 tokens per second on an iPhone 17 Pro Max.

Key claims from the launch

The model family is presented as a native end-to-end 1-bit design, not a later-stage quantization pass.

PrismML’s intelligence-density metric puts Bonsai 8B at 1.06 per GB versus 0.10 per GB for Qwen3 8B.

The company claims much better memory efficiency for on-device inference and longer-running agent workloads.

Weights are available under Apache 2.0, with a whitepaper and MLX plus llama.cpp CUDA support.

The LocalLLaMA interest makes sense. The subreddit has spent the past year chasing better quantization, lower latency, and workable on-device agent setups, and Bonsai is framed as a jump from “can it fit” to “can it do serious work.” PrismML also argues that the smaller memory footprint translates into 4x to 5x better energy efficiency and opens room for persistent local agents, secure enterprise copilots, and offline AI products.

Still, this is launch-day data from the vendor. The new intelligence-density metric is defined by PrismML itself, and the real test will be whether outside users can reproduce the speed, quality, and tool-use claims on shipping hardware. Even with that caveat, the release is notable because it moves the conversation beyond post-training quantization and toward models designed as 1-bit systems from the start.

Community source: Reddit discussion. Primary source: PrismML announcement.