AI tool comparison
Bonsai-8B vs Depot
Which one should you ship with? Here is the side-by-side panel verdict, pricing read, reviewer split, and community vote comparison.
Infrastructure
Bonsai-8B
A true 1-bit 8B LLM that fits in 1.15 GB — runs on your iPhone
75%
Panel ship
—
Community
Free
Entry
Bonsai-8B is PrismML's latest model in their BitNet-inspired lineage — an 8.2B parameter language model that has been quantized end-to-end to true 1-bit precision (weights stored as -1 or +1), compressing the entire model to just 1.15 GB. That's roughly 12-14x smaller than a standard FP16 equivalent. Unlike post-training quantization hacks that lose substantial quality, PrismML trained Bonsai-8B with 1-bit arithmetic baked into the forward pass from the start. Benchmark results are competitive for the size class: 63.8 on MMLU, 72.1 on HellaSwag, and 54.2 on GSM8K — while running at 131 tokens/sec on an M4 Pro MacBook and 44 tokens/sec on an iPhone 17 Pro Max. That makes it the fastest locally-runnable 8B model in its weight class on Apple Silicon. The MLX-optimized weights are available on Hugging Face today under Apache 2.0. The significance goes beyond benchmarks. Getting a capable open-weight model to run at interactive speeds on consumer hardware — with no API key, no GPU, no cloud dependency — is a meaningful step toward truly private, offline AI. This follows PrismML's earlier "Ternary Bonsai" (1.58-bit) but represents a cleaner binary architecture that's easier to accelerate on custom silicon.
Infrastructure
Depot
Remote container builds for CI
100%
Panel ship
—
Community
Free
Entry
Depot provides remote Docker builds that are 5-20x faster than CI runners. Persistent caching, native multi-platform builds, and zero configuration.
Reviewer scorecard
“131 tokens/sec on M4 Pro at 1.15 GB is genuinely impressive — I can embed this in a macOS app without any cloud dependency, no rate limits, no privacy concerns. The Apache 2.0 license means I can ship commercial products on top of it. This is the edge AI story I've been waiting for.”
“Docker builds that take 10 minutes in CI complete in 30 seconds on Depot. The speed improvement is dramatic.”
“63.8 on MMLU is respectable but it's still noticeably behind mid-range cloud models on reasoning tasks. The GSM8K score of 54.2 means it'll fumble multi-step math that users expect to just work. Until 1-bit gets to 70B scale, it's a neat demo that falls short in production use cases where quality matters.”
“If Docker builds are your CI bottleneck, Depot eliminates it. Drop-in replacement with massive time savings.”
“The trajectory here is what matters: 1-bit models are getting faster to train and competitive faster than expected. When custom Apple Neural Engine kernels land for BitNet-style weights, we'll see 200+ tokens/sec on a phone. Bonsai-8B is the proof-of-concept that makes that future feel real.”
“Remote build infrastructure will become standard. Local or CI builds on underpowered machines make no sense.”
“I've been looking for something I can embed in a creative writing or brainstorming app that doesn't require an internet connection. At 44 tokens/sec on iPhone, Bonsai-8B is finally fast enough to not break the creative flow. The 'no account required' angle is a genuine selling point for privacy-conscious users.”
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