RTX 5090 Fuels Blazing-Fast Shogi AI with TensorRT and FP8 Optimization

research #gpu 📝 Blog|Analyzed: Mar 21, 2026 12:45•

Published: Mar 21, 2026 12:41

•

1 min read

Analysis

This article highlights an impressive feat: achieving significant performance gains in a Shogi AI using an RTX 5090 GPU, TensorRT, and FP8 quantization. The implementation demonstrates the power of optimizing deep learning models for faster inference, resulting in a more efficient and responsive AI experience. The focus on VRAM reduction while boosting speed is particularly noteworthy.

Key Takeaways

•The project utilizes TensorRT for optimized inference on an RTX 5090, leading to significant performance gains.
•FP8 quantization reduces VRAM usage while improving inference speed compared to INT4.
•The system achieves impressive node evaluation speeds (90k NPS) with the optimized settings.

Reference / Citation

"FP8 quantization is superior to INT4 in accuracy and exhibits excellent performance in NPS (nodes evaluated per second)."

Q

Qiita DLMar 21, 2026 12:41

* Cited for critical analysis under Article 32.

RTX 5090 LLM Inference Showdown: vLLM vs. TensorRT-LLM vs. Ollama vs. llama.cpp

One RTX 5090, Thirteen AI Projects: A Developer's Innovation Showcase

Related Analysis

AI Music Analyzer: LLM Unveils the Secrets of Sound

Mar 21, 2026 11:16

AI Takes Initiative: Internalized Purpose-Driven Design Revolutionizes AI Agents

Mar 21, 2026 11:16

Mac Studio Outperforms DGX Spark in Local LLM Inference, Revealing Software Optimization Secrets

Mar 21, 2026 10:00

Source: Qiita DL