Unleashing Efficiency: Benchmarking the Power of TensorRT LLMOverview of the benefits of TensorRT

In the high-stakes world of AI, where latency can make or break the utility of an application, Fetch's pioneering use of NVIDIA's TensorRT to optimize Large Language Models (LLMs) has raised the bar. The latest benchmarks clearly illustrate the remarkable strides made possible by TensorRT LLM, particularly when it comes to reducing inference latency for real-time performance.
Figure 1 reveals that TensorRT LLM models significantly outperform traditional models during the prefill phase. Notably, the W4A16_AWQ model, which benefits from adaptive weight quantization (AWQ), shows an almost linear and minimal latency increase, even as the prompt lengthens, underscoring its ability to manage larger contexts with ease.
Key insights:

• Across various prompt sizes, TensorRT LLM variants consistently demonstrate lower latency, allowing for quicker engagement in generating responses.
• The AWQ technique employed in configurations like W4A16_AWQ exemplifies NVIDIA's optimization prowess, delivering top-notch latency performance.

Decoding the Benefits: Latency Benchmarks Unveiled

The second set of benchmarks concentrates on the decoding phase latency, where the model produces responses token by token—a vital measure of its interactive capability.
In Figure 2, the TensorRT LLM models, especially the TensorRT LLM W8A8_SQ, which uses sparse quantization, display a substantial lead over baseline models, achieving lower mean latencies and higher rates of token generation, particularly noticeable as the token count increases.
Key observations:

• TensorRT LLM W4A16 exhibits stellar low-latency performance, a testament to the optimization of 4-bit weight quantization paired with 16-bit activation quantization.
• With state-of-the-art quantization approaches like those seen in TensorRT LLM W8A16_AWQ, the models enhance speed while maintaining output integrity, propelling token per second rates to new highs.

Quantization: The Technical Edge

Quantization is a technique that reduces the precision of the model's computations. Weight quantization refers to reducing the number of bits that represent each weight in the model's architecture, while activation quantization applies the same principle to the activation outputs from each layer. Adaptive Weight Quantization (AWQ) is an advanced method that intelligently adapts the quantization levels to the distribution of weights, further optimizing the balance between model size, speed, and accuracy.

Conclusion: The Competitive Edge of TensorRT LLM

These benchmarks convincingly show that TensorRT LLM significantly reduces inference latency, enabling faster and more responsive AI applications. By employing sophisticated quantization techniques for both weights and activations, TensorRT LLM accelerates the inference process and democratizes the use of advanced LLMs for developers and companies facing strict latency requirements.
Fetch's commitment to the adoption of TensorRT LLM is a testament to our pursuit of excellence in AI performance. These benchmarks substantiate our strategy and emphasize the definitive edge provided by TensorRT LLM, allowing us to deliver AI solutions that are not just visionary but also viable in the fast-paced technology landscape of today.
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