New Tool for LLM Inference Performance Tuning: Finding the Optimal Balance Between TTFT and TPOT

This article introduces the open-source tool llm-inference-sla-tuner, which provides a hardware-aware automatic tuning solution for LLM inference configurations, helping developers achieve the optimal trade-off between first-token latency (TTFT) and generation speed (TPOT). The tool integrates Service Level Objectives (SLO) into its optimization framework, addressing the limitations of traditional manual parameter tuning. It is applicable to various scenarios and offers advantages such as versatility and interpretability.

With the widespread deployment of LLMs, inference performance optimization has become a core challenge: users need both low TTFT (seeing the first character as soon as possible) and low TPOT (fast generation of subsequent content), but these two are mutually constrained. Traditional solutions rely on manual parameter tuning based on experience, which is time-consuming and labor-intensive, and difficult to adapt to different hardware (such as NVIDIA GPU, AMD GPU, TPU), failing to fully tap into hardware potential.

This tool is a hardware-aware automatic tuning prototype, with its core innovation being the explicit integration of SLO. Its technical architecture consists of three main components:

1. Hardware Performance Modeler: Analyzes hardware parameters (such as memory bandwidth, computing units) to build prediction models and adapt to new hardware;
2. Configuration Space Searcher: Uses intelligent algorithms (like Bayesian optimization) to efficiently search for optimal solutions in multi-dimensional parameter spaces (batch processing, parallelism, etc.);
3. SLO-Aware Optimizer: Users define TTFT/TPOT targets, and the optimizer maximizes throughput or minimizes resource consumption under constraints.

Applicable to three types of scenarios:

• Pre-research for new model deployment: Evaluate performance under different hardware to support procurement and architecture decisions;
• Dynamic tuning for online services: Adjust configurations based on real-time load (increase batch processing during low peaks to improve throughput, reduce batch processing during high peaks to optimize latency);
• Multi-tenant resource allocation: Allocate appropriate configurations to tenants with different sensitivity requirements, balancing service quality and resource efficiency.

Unlike inference engines such as vLLM and TensorRT-LLM, this tool is positioned as a "configuration advisor" rather than an execution engine. Its advantages include:

• Versatility: Can work with any inference backend;
• Interpretability: Provides basis for configuration choices and performance predictions;
• Flexibility: Supports custom optimization goals and constraints.

The project welcomes community contributions. Key future directions include:

• Expanding hardware platform coverage;
• Integrating more advanced optimization algorithms;
• Providing visual performance analysis tools;
• Establishing a community-shared hardware performance database. Automatic configuration optimization will become a standard part of infrastructure, and the tool's SLO-driven concept is expected to influence the development of subsequent tools.

LLM inference optimization is a multi-dimensional and complex problem, and llm-inference-sla-tuner provides a new solution through SLO-driven approaches. For teams facing deployment performance challenges, this lightweight tool is worth trying—finding the optimal balance between TTFT and TPOT can both improve user experience and reduce infrastructure costs, achieving a win-win situation.