LLiMa is an automated on‑device AI framework developed by SiMa.ai for deploying Large Language Models, Large Multimodal Models, and Vision‑Language Models on Modalix MLSoC hardware. It eliminates manual optimization by automatically importing, quantizing, and compiling models into edge‑ready binaries that run under 10 watts. The framework integrates model orchestration, quantization strategies, and deterministic scheduling to ensure stable, predictable performance. Its ecosystem includes a curated model zoo, retrieval‑augmented generation support, and agent‑to‑agent communication for building complete on‑premise AI systems without relying on cloud services. LLiMa supports multiple architectures, automated runtime coordination, and enterprise data integration through MCP, providing a comprehensive infrastructure for real‑time Physical AI applications.

Key Features

Automated Model Compilation Transforms LLMs, LMMs, and VLMs into optimized Modalix‑ready binaries.

• Automated ONNX generation, quantization, and compile steps
• Multi‑process compilation for large models to reduce time

Curated Model Zoo & Seamless Import Supports direct import of models from Hugging Face.

• Precompiled model availability
• One‑click import for supported architectures

Sub‑10W Real‑Time Execution Ensures deterministic low‑latency performance on edge hardware.

• 6–17 TPS sustained throughput
• 0.12–1.38s time‑to‑first‑token metrics

Advanced Quantization Pipeline Reduces memory and power use while preserving accuracy.

• INT8 and INT4 weight compression
• Dynamic activation quantization on‑chip

Full On‑Device Enterprise Integration Connects models directly to enterprise systems without cloud.

• Retrieval‑augmented generation
• Model Context Protocol and agent‑to‑agent workflows

Benefits

Zero Cloud Dependency All inference and data processing runs locally.

• Eliminates data‑egress risks
• Improves privacy and compliance for regulated sectors

Predictable Performance Static scheduling ensures consistent, repeatable inference.

• No thermal throttling
• Deterministic execution paths for safety‑critical tasks

Lower Power and Total Cost Edge execution reduces operational and hardware overhead.

• Sub‑10W consumption
• Avoids cloud runtime fees and cooling demands

Fast Deployment Cycle Automated processes reduce engineering time.

• No manual optimization needed
• Hours instead of months for custom model deploymen