🔲 LLM Inference Hardware 🔲
As the applications of Large Language Models (LLMs) continue to expand, the demand for efficient, rapid, and cost-effective inference solutions is escalating. Traditionally,GPUs have dominated the LLM inference landscape, but 2024 is set to witness a significant shift towards alternative hardware solutions.
▶ AMD's Instinct accelerators, including the MI300X and MI300A, paired with AMD's ROCm open software platform, offer exceptional throughput on AI workloads. This combination addresses previous challenges like long lead times and resource intensiveness.
▶ Companies like Neural Magic and Intel are leading the way with CPU-based solutions for LLM inference. Neural Magic's approach to LLM inference focuses on balancing model size, speed, and accuracy through sparsification and quantization, making CPUs a practical alternative for teams with limited GPU access. Intel's BigDL-LLM and Intel® Extension for Transformers enhance CPU processing capabilities, making them more suitable for handling LLM demands.
▶ Meta's foray into custom-designed silicon based on RISC-V technology, purpose-built for AI workloads
▶ The recent research paper on "Efficient LLM Inference on CPUs" by Intel introduces an automatic INT4 quantization flow using Intel Neural Compressor, significantly reducing model size and accuracy loss, with minimal differences from FP32 baselines. This approach is complemented by an efficient LLM runtime designed to accelerate inference on CPUs, using a CPU tensor library that supports mainstream instruction sets like AVX2, AVX512, AVX512_VNNI, and AMX.
▶ It shows effectiveness across various LLMs, with per-token generation latencies ranging from 20ms to 80ms for models ranging from 3B to 20B parameters, surpassing the average human reading speed of approximately 200ms per token.
▶ Advancements in CPU-based LLM inference, this shift from vendor lock-in in tech ecosystem offers teams the flexibility to select the most fitting option for their specific needs and represents a significant step towards democratizing generative AI.
📌 Efficient LLM Inference on CPUs paper link : https://lnkd.in/eji5sdWk
#LLM #InferenceHardware #AI #GenAI
Semiconductor Innovation @ ipXchange | Content, Social Media, Digital Marketing
1moWatch the full interview about how Groq’s LPUs overtake GPUs for fastest LLM AI here: https://ipxchange.tech/news/how-groqs-lpus-overtake-gpus-for-fastest-llm-ai/