FastTPS: An Optimized Method for LLM Token Phase for AI accelerators

• URL: http://arxiv.org/abs/2607.11211v1
• Date: Mon, 13 Jul 2026 08:02:26 GMT
• Title: FastTPS: An Optimized Method for LLM Token Phase for AI accelerators
• Abstract summary: FastTPS is a high performance and low-precision loss method for accelerating the token-phase in LLM inference on general AI accelerators.<n>Our results confirm that FastTPS significantly alleviates memory bottlenecks in the token phase, delivering a 6x speed improvement.
• Score: 11.379739631959822
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The popularity of large language models (LLMs) escalates an ongoing demand for effective inference. However, due to the sequential processing of tokens during the token phase in decoder-only LLMs inference, the inherent low parallelism leads to reduced throughput and suboptimal utilization of the computing units on artificial intelligence (AI) accelerators, particularly when handling long-sequence inputs that impose significant memory overhead. Recently, many reported methods have been developed as potential solutions, since they emerge with numeric deviation. This paper presents FastTPS, a high performance and low-precision loss method for accelerating the token-phase in LLM inference on general AI accelerators which includes three key components: (1) AI accelerator-enabled reloading-free KV Cache concatenation which decreases memory access overhead as well as enables full fusion of Attention, (2) high-efficiency and high-accuracy 'RoPE' attention based on the tiling optimized FLAT, and (3) highly-fused MLP with fine-grain pipeline scheduling. Our results confirm that FastTPS significantly alleviates memory bottlenecks in the token phase, delivering a 6x speed improvement (compared to none-fusion) on an AMD Ryzen AI 300 series NPU with BF16 precision while sustaining 93% peak memory bandwidth utilization during Phi3-mini-4k-instruct inference.

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