Self-Hosted LLM Upgrade on AMD: Kimi Linear 48B, Qwen3 Coder Next, and Q2_K_XL (site.bhamm-lab.com)

A recent evaluation highlights the capabilities of several self-hosted AI models on AMD hardware, including Kimi Linear 48B, Qwen3 Coder Next, and Q2_K_XL. The Kimi Linear 48B emerged as the best generalist model for diverse tasks, demonstrating impressive speed and consistency, while Qwen3 Coder Next was noted as an exceptional replacement for previous coding models, offering remarkable speed and quality. Another interesting finding was the viability of heavy quantization techniques in Q2_K_XL for long-running, background workflows, although some models struggled with human-in-the-loop tasks due to latency issues. This testing holds significance for the AI/ML community as it illustrates the advancements in model architecture and performance, especially in open-source spaces. The benchmark results provide critical insight into how various models perform under real-world conditions using AMD's latest hardware, emphasizing their potential to compete with proprietary solutions. The exploration of local AI deployments allows for increased understanding of model architecture trade-offs, including latency and context management, ultimately guiding the development of more effective and efficient AI applications tailored for self-hosting environments.