# Assessing Tenstorrent's RISC-V MatMul Acceleration Capabilities

arXiv: https://arxiv.org/abs/2505.06085 · HTML: https://mlsanity.com/paper/2505.06085/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 5 references that are in the map.
- First posted on arXiv 9 May 2025.

## Builds on (most cited first, 4 of 4)

- [Cerebras-GPT: Open Compute-Optimal Language Models Trained on the Cerebras Wafer-Scale Cluster](https://mlsanity.com/paper/2304.03208/) (2023)
- [A Modern Primer on Processing in Memory](https://mlsanity.com/paper/2012.03112/) (2020)
- [SambaNova SN40L: Scaling the AI Memory Wall with Dataflow and Composition of Experts](https://mlsanity.com/paper/2405.07518/) (2024)
- [Accelerating stencils on the Tenstorrent Grayskull RISC-V accelerator](https://mlsanity.com/paper/2409.18835/) (2024)

## Built on by (most cited first, 1 of 1)

- [Operator Fusion for LLM Inference on the Tensix Architecture](https://mlsanity.com/paper/2606.09879/) (2026)

## Abstract (arXiv)

The increasing demand for generative AI as Large Language Models (LLMs) services has driven the need for specialized hardware architectures that optimize computational efficiency and energy consumption. This paper evaluates the performance of the Tenstorrent Grayskull e75 RISC-V accelerator for basic linear algebra kernels at reduced numerical precision, a fundamental operation in LLM computations. We present a detailed characterization of Grayskull's execution model, gridsize, matrix dimensions, data formats, and numerical precision impact computational efficiency. Furthermore, we compare Grayskull's performance against state-of-the-art architectures with tensor acceleration, including Intel Sapphire Rapids processors and two NVIDIA GPUs (V100 and A100). Whilst NVIDIA GPUs dominate raw performance, Grayskull demonstrates a competitive trade-off between power consumption and computational throughput, reaching a peak of 1.55 TFLOPs/Watt with BF16.