JiRack Dense is a high-performance transformer architecture designed to bridge the gap between 100B and 500B+ parameter models. Unlike traditional architectures that suffer from memory bottlenecks, JiRack utilizes proprietary
SWA Fusion
and
BRE Routing
to maximize throughput on AMD ROCm (Instinct MI300/400) and NVIDIA Hopper/Blackwell hardware.
This repository contains the core logic for the
Dense
(Non-Ternary) versions of JiRack, optimized for high-fidelity reasoning and stable training on massive datasets like
The Pile
.
🛠 Key Innovations
1. SwiGLU-Attention (SWA) Fusion
A unified compute kernel that merges the Feed-Forward Network (FFN) and Multi-Head Attention (MHA) passes.
Impact:
30% reduction in VRAM I/O and faster training steps.
2. Buffered Routing Embedding (BRE)
A predictive HBM management system that pre-fetches embedding weights into high-speed buffers.
Impact:
Eliminates GPU "starvation" during inference and allows for context windows up to 128k.
3. Frontier Scaling
Optimized configurations for extreme scales:
140B:
The efficiency leader for enterprise clusters.
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