SBLA-Pico-45M (Chat)

Sparse-Binary Latent Attention Architecture for Ultra-Efficient Edge Inference

Developed by Sumit Shrivastava (Shri Innovations)

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⚡ Overview

SBLA-Pico-45M is an ultra-efficient foundational language model engineered to circumvent the memory bandwidth and thermodynamic limits of conventional LLMs. It is co-designed to execute natively on the SBLA Production Engine—a custom C++ inference runtime that completely bypasses floating-point matrix multiplications.

By unifying 1.58-bit ternary weight quantization, Multi-Head Latent Attention (MLA), and Auxiliary-Loss-Free Mixture of Experts (MoE), SBLA achieves extreme performance on highly constrained edge hardware (IoT, ARM Cortex, WebAssembly) with zero VRAM decompression overhead.

Model Specifications

• Total Parameters: ~45 Million
• Active Parameters / Token: ~16 Million (Top-4 MoE Routing)
• Precision: 1.58-Bit Ternary ({-1, 0, 1}) weights / INT8 activations
• Architecture: Decoupled Rotary Latent Attention (MLA) + SwiGLU
• Context Window: 2048 Tokens
• Required Engine: SBLA C++ Edge SDK

🚀 Quickstart: C++ Edge Engine

To achieve the intended microsecond latency and zero-copy RAM usage, execute this model using the official C++ SDK.

1. Install the SDK

git clone [https://github.com/ShriInnovations/SBLA_Production_Engine.git](https://github.com/ShriInnovations/SBLA_Production_Engine.git)
cd SBLA_Production_Engine
cmake -B build -DCMAKE_BUILD_TYPE=Release
cmake --build build -j

2. Run Inference

# SBLA uses zero-copy mmap. The model boots in < 0.01 seconds.
./build/sbla_chat models/sbla_pico_40m.bin models/sbla_vocab.txt

🔬 PyTorch Integration (trust_remote_code)

For researchers and developers fine-tuning the model, SBLA supports native PyTorch loading via the provided modeling_sbla.py and custom Triton acceleration kernels (sbla_kernel.py).

from transformers import AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained(
    "SumitShrivastava/SBLA-Pico",
    trust_remote_code=True
)
(Note: High-speed Triton kernel acceleration is reserved for the Enterprise SDK).

Optional: Enable Triton Hardware Acceleration for NVIDIA GPUs

model.set_kernel_mode(use_kernel=True)

⚙️ Hardware Architecture & Custom Silicon Co-Design

SBLA is fundamentally co-designed for next-generation edge chips, NPUs, and specialized integer accelerators.

Layer	Optimization Engine	Physical Mechanism
Memory Bus	Sub-Byte 2-Bit Packing	16 Ternary Weights packed per 32-Bit Memory Word (16x bandwidth reduction)
GPU Acceleration	Custom Triton Kernel	Pure INT8/INT32 integer accumulation bypassing FP16 Tensor Cores
CPU / Edge SIMD	Masked Bitwise Arithmetic	AVX-512 & ARM NEON vector additions; zero multiplication gates
Custom ASIC / NPU	Multiplier-Free Silicon	FPU gates replaced with high-speed integer adders; SRAM expert caching

Thermodynamic & Energy Benchmark

Operation / Metric	Standard FP16 Transformer	SBLA 1.58-Bit Architecture	Hardware Efficiency Gain
8-Bit / Ternary Addition	1.1 pJ (FP16 Multiply)	0.03 pJ (INT8 Add)	~36x to 70x Lower Energy
Active Parameters	40.6 Million (100% Dense)	~14.8 Million (Sparse MoE)	~64% Compute Reduction
KV-Cache Size per Token	Full Multi-Head Vector	Bottleneck dimension (64)	~4x to 8x Less VRAM

⚖️ License & Commercial Deployment

The SBLA architecture, kernel logic, and associated weights are governed by the SBLA Community & Commercial Dual License.

• Open-Source / Academic: 100% Free for evaluation, research, educational, and non-commercial benchmarking.
• Commercial Deployment: Integration into profit-generating software, cloud API serving, edge hardware deployment, or the creation of derivative commercial products is STRICTLY PROHIBITED without an explicit Commercial License Agreement.
• Enterprise Licensing: Commercial licenses require a formal agreement granting either enterprise licensing fees or a negotiated equity share (5%–10%) and gross revenue royalty.

For enterprise SDK access, hardware deployment licenses, or equity partnerships, contact: Sumit Shrivastava (ShriInnovations).

📚 Citation

Any publication, demo, or public evaluation referencing this architecture must prominently cite:

@misc{shrivastava2026sbla,
  title={SBLA: Sparse-Binary Latent Attention Architecture for Ultra-Efficient Edge Foundation Models},
  author={Shrivastava, Sumit},
  year={2026},
  publisher={Hugging Face},
  journal={Shri Innovations Research}
}