A survey on hardware accelerators for large language models (LLMs) explores various hardware solutions to enhance performance and energy efficiency. LLMs, such as GPT-3, are highly complex models that require significant computational resources for training and inference. This paper reviews existing hardware accelerators, including GPUs, FPGAs, ASICs, and in-memory computing, to optimize LLM performance. The survey analyzes different architectures, performance metrics, and energy efficiency, highlighting the trade-offs between speed, energy consumption, and hardware implementation. The computational complexity of LLMs is high due to their large parameter count and deep neural network structures. Training and inference phases both demand substantial computational resources. Energy consumption is a major concern, as training LLMs can consume as much energy as multiple cars. Efforts to reduce energy use include optimizing model architecture, using energy-efficient hardware, and adopting renewable energy sources. FPGA-based accelerators, such as MNNFast, FTRANS, and SpAtten, offer significant speedups and energy efficiency improvements. For example, SpAtten achieves up to 347x speedup over CPUs and 1193x energy savings. ASIC accelerators like A3 and Energon provide even higher performance, with Energon achieving 168x speedup and 10,000x energy reduction. In-memory computing accelerators, such as ATT and X-Former, also show promising results, with X-Former achieving up to 85x latency improvement over GPUs. CPU and GPU-based accelerators, such as SoftMax, LightSeq2, and LLama, also contribute to LLM optimization. LightSeq2 achieves up to 3x speedup on large datasets, while LLama provides up to 3.06x speedup for LLM inference. In-memory accelerators like ReTransformer and iMCAT further enhance performance, with ReTransformer achieving 23.21x speedup and iMCAT achieving 200x speedup for long sequences. The survey concludes that hardware accelerators are essential for optimizing LLM performance and reducing energy consumption. ASICs and in-memory computing offer the highest speedup and energy efficiency, but require significant investment. FPGAs provide a balance between performance and cost, while GPUs remain widely used. As LLMs continue to grow in complexity, hardware accelerators will play a crucial role in enabling efficient and sustainable deployment.A survey on hardware accelerators for large language models (LLMs) explores various hardware solutions to enhance performance and energy efficiency. LLMs, such as GPT-3, are highly complex models that require significant computational resources for training and inference. This paper reviews existing hardware accelerators, including GPUs, FPGAs, ASICs, and in-memory computing, to optimize LLM performance. The survey analyzes different architectures, performance metrics, and energy efficiency, highlighting the trade-offs between speed, energy consumption, and hardware implementation. The computational complexity of LLMs is high due to their large parameter count and deep neural network structures. Training and inference phases both demand substantial computational resources. Energy consumption is a major concern, as training LLMs can consume as much energy as multiple cars. Efforts to reduce energy use include optimizing model architecture, using energy-efficient hardware, and adopting renewable energy sources. FPGA-based accelerators, such as MNNFast, FTRANS, and SpAtten, offer significant speedups and energy efficiency improvements. For example, SpAtten achieves up to 347x speedup over CPUs and 1193x energy savings. ASIC accelerators like A3 and Energon provide even higher performance, with Energon achieving 168x speedup and 10,000x energy reduction. In-memory computing accelerators, such as ATT and X-Former, also show promising results, with X-Former achieving up to 85x latency improvement over GPUs. CPU and GPU-based accelerators, such as SoftMax, LightSeq2, and LLama, also contribute to LLM optimization. LightSeq2 achieves up to 3x speedup on large datasets, while LLama provides up to 3.06x speedup for LLM inference. In-memory accelerators like ReTransformer and iMCAT further enhance performance, with ReTransformer achieving 23.21x speedup and iMCAT achieving 200x speedup for long sequences. The survey concludes that hardware accelerators are essential for optimizing LLM performance and reducing energy consumption. ASICs and in-memory computing offer the highest speedup and energy efficiency, but require significant investment. FPGAs provide a balance between performance and cost, while GPUs remain widely used. As LLMs continue to grow in complexity, hardware accelerators will play a crucial role in enabling efficient and sustainable deployment.