V-Seek: Accelerating LLM Reasoning on Open-hardware Server-class RISC-V Platforms

• URL: http://arxiv.org/abs/2503.17422v1
• Date: Fri, 21 Mar 2025 09:00:19 GMT
• Title: V-Seek: Accelerating LLM Reasoning on Open-hardware Server-class RISC-V Platforms
• Abstract summary: This paper focuses on optimizing Large Language Models (LLMs) inference on the Sophon SG2042, the first commercially available many-core RISC-V CPU with vector processing capabilities.<n>On two recent state-of-the-art LLMs optimized for reasoning, DeepSeek R1 Distill Llama 8B and DeepSeek R1 Distill QWEN 14B, we achieve 4.32/2.29 token/s for token generation and 6.54/3.68 token/s for prompt processing, with a speed up of up 2.9x/3.0x compared to our baseline.
• Score: 13.87105456715319
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The recent exponential growth of Large Language Models (LLMs) has relied on GPU-based systems. However, CPUs are emerging as a flexible and lower-cost alternative, especially when targeting inference and reasoning workloads. RISC-V is rapidly gaining traction in this area, given its open and vendor-neutral ISA. However, the RISC-V hardware for LLM workloads and the corresponding software ecosystem are not fully mature and streamlined, given the requirement of domain-specific tuning. This paper aims at filling this gap, focusing on optimizing LLM inference on the Sophon SG2042, the first commercially available many-core RISC-V CPU with vector processing capabilities. On two recent state-of-the-art LLMs optimized for reasoning, DeepSeek R1 Distill Llama 8B and DeepSeek R1 Distill QWEN 14B, we achieve 4.32/2.29 token/s for token generation and 6.54/3.68 token/s for prompt processing, with a speed up of up 2.9x/3.0x compared to our baseline.

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