A developer's experiment revealed that the DDR5 bandwidth on AMD APUs significantly limits the performance of running multiple large language models simultaneously. Despite a 35-billion-parameter model like Qwen 3.6:35B appearing to use only a fraction of its parameters per token, its actual inference speed is bottlenecked by the shared memory bandwidth, making it comparable to smaller models. This discovery led to the abandonment of a multi-model agent architecture due to performance degradation when attempting to run two models concurrently on the same hardware. AI
IMPACT Highlights critical hardware bottlenecks for running multiple LLMs on consumer-grade hardware, impacting agent architectures.
RANK_REASON Developer benchmarks and analysis of hardware limitations for LLM inference. [lever_c_demoted from research: ic=1 ai=0.7]
- AMD Radeon 780M
- AMD Ryzen 9 7940HS
- DDR5
- Gemma 4:2B-abliterated
- LLM
- Minisforum UM790Pro
- Ollama
- Qwen 2.5:1.5B
- Qwen 3:4B-instruct
- Qwen 3.6:35B
AI-generated summary · Google Gemini · from 1 sources. How we write summaries →
