Static schema prefix contention and O(1) state caching on MCU memory budgets
Hi Henry, Karen, and Cactus Compute team,
Compressing a 121M tool-calling model down to an 8-29 MB container using Monarch Hadamard MLPs, engram lookups, and 2.125-bit CQ2 quantization - while keeping depth sliceable from 2 to 20 layers - is phenomenal systems engineering for the microcontroller and wearable tier.
Looking at your runtime constraints and the deployment contract:
Static tool schema prefix contention:
Your deployment docs note that tool schemas and system prompts share the active context window with conversational history, meaning long function definitions and max_new_tokens generation caps actively cannibalize the available space for user turns.
On wearables and embedded microcontrollers where DRAM or SRAM is bounded to a few megabytes, standard GQA attention still requires storing key-value tensors across all active ladder layers. Even in a 4-layer sliced subnetwork, maintaining a dynamic KV cache for verbose JSON schemas and multi-turn arguments causes rapid memory pressure on constrained runtimes.
Recurrent state prefix absorption:
In an open architecture project called Maba v2 (101M reference release: https://huggingface.co/AndrewThompson1233/maba-v2-architecture), we explore sub-quadratic execution for memory-constrained edge hardware using a 3:1 hybrid topology (75% DGDA linear recurrence / 25% MABA-SA latent sparse attention):
DGDA maintains an associative recurrent state in strictly bounded O(1) memory via closed-form delta updates.
For structured tool calling, static function definitions and system schemas can be ingested directly into the recurrent state matrix once at initialization. Because 75% of the network requires zero token-history cache, this slashes KV memory overhead by nearly 40x and prevents static tool schemas from choking decoding memory on tiny devices.
Engram gather efficiency:
Offloading bulk capacity to engram n-gram tables so a 121M parameter model executes with 50M compute is a great architectural choice for low-power silicon. Pairing that arithmetic reduction with sub-quadratic sequence memory would eliminate the last major memory bottleneck on embedded engines.
When running the 2-to-4 layer sliced subnetworks on microcontrollers, what is the typical SRAM/DRAM threshold where the GQA cache begins to constrain larger multi-tool catalogues?
Best,
Andrew