Cactus Compute has introduced Needle, a foundation model specifically engineered for resource-constrained environments like microcontrollers, wearables, and robotics. The flagship Needle 3 architecture utilizes a Laddered Simple Attention Network, which replaces standard Feed-Forward Networks (FFN) with Monarch Hadamard MLPs and integrates Grouped Query Attention (GQA) with causal convolutional taps. A distinctive feature is the "engram" n-gram memory read via gather operations and multi-lane hyper-connections. This design allows the 121M parameter model to perform with the arithmetic efficiency of a 50M parameter model. Needle is optimized for local execution, offering binary sizes between 8-29 MB and supporting quantization down to 2.125 bits per weight via the .cact format. Technically, the model excels at tool calling and structured JSON extraction through a byte-level grammar that constrains token generation to ensure valid parsing. Performance benchmarks indicate that when fine-tuned on DroidCall, subnetwork depths from 4 layers upward can outperform DeepSeek V4 Flash, even when starting from only 29M parameters. The system supports tiered deployment, allowing users to extract specific depths (from 2 to 20 layers) as standalone, deployable models, and provides both local LoRA fine-tuning and platform-based full-model training.