# b11618: HIP: use 64-row MMQ tiles for some quant types on RDNA3.5 (#28195)

> **Key Architectural Takeaway:** The RDNA3.5 config table selects a 64-row / 128-thread tile up to J=32 and a 128-row / 256-thread tile above that.

**Published:** 2026-10-14T06:10:29+00:00 
**Source:** Llama.cpp Tensor Acceleration 
**Category:** ai-local-edge 
**Canonical URL:** https://fosswire.org/news/b11618-hip-use-64-row-mmq-tiles-for-some-quant-types-on-rdna.html 

## Executive Summary
The RDNA3.5 config table selects a 64-row / 128-thread tile up to J=32 and a 128-row / 256-thread tile above that. On gfx115x the smaller tile leaves enough LDS free for a second workgroup per CU, while the larger one does not. Extend the 64-row tile to the (type, J) pairs where it measured faster on gfx1151, and leave every other entry untouched. J=96 is deliberately excluded because it did not benefit.

## Architectural & Systems Analysis
From an artificial intelligence architecture, model weights governance, and inference efficiency perspective:

- **Weights Accessibility & Sovereignty:** Evaluates whether weights are open for private self-hosting or locked behind centralized cloud APIs.
- **Quantization & Edge Performance:** Kernel optimizations (4-bit/8-bit GGUF, AWQ, EXL2) allow high tokens-per-second on consumer GPUs and Apple Silicon.
- **Reasoning & Architectural Scaling:** Scrutinizes mixture-of-experts (MoE), attention mechanisms, and fine-tuning datasets against open community benchmarks.

## Impact on the Open Ecosystem
Protects developers and enterprises from proprietary black-box entrapment, fostering auditable, sovereign AI infrastructure.