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Optimized GLM-5.3-Flash for single GPU, boosting speed 1.5x

By PulseAugur Editorial · [1 sources] ·

A technical blog post details optimizations for running the large GLM-5.3-Flash model on a single 96 GiB GPU, achieving up to 16.5 tokens per second. The author implemented a "hot-expert cache" that keeps frequently used model components on the GPU, significantly speeding up token generation and prompt reading. This approach, combined with a specialized Triton kernel for efficient data loading and grouping experts for long prompts, improved performance by up to 1.5x for writing and 2.9x for prompt reading, while maintaining output quality. AI

IMPACT Demonstrates techniques for efficient LLM deployment on limited hardware, potentially enabling wider access to large models.

RANK_REASON Technical blog post detailing optimizations for running a specific LLM on hardware, not a new model release or major industry event.

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Optimized GLM-5.3-Flash for single GPU, boosting speed 1.5x

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Technical blog post detailing optimizations for running a specific LLM on hardware, not a new model release or major industry event.
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COVERAGE [1]

  1. dev.to — LLM tag TIER_1 English(EN) · Rahul Kumar ·

    A hot-expert cache made a 181 GiB model faster on one 96 GiB GPU

    <p>The build of GLM-5.3-Flash I run is 181 GiB, and my GPU has 96 GiB. It still writes up to 16.5 tokens a second on one desktop. Offloading more on its own made it slower; keeping each layer's hot experts on the card made it faster.</p> <p>One of the eight bugs on the way gave n…

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