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Kimi K3 LLM analysis shows diminishing returns on RAM for speed

By PulseAugur Editorial · [1 sources] ·

An analysis of the Kimi K3 large language model, running on an open-source C engine called kimi-k3-in-c, reveals that increasing RAM significantly does not proportionally improve speed. Despite a 28-fold increase in memory from 8 GB to 224 GB, the model's speed only improved by a factor of 1.7. The analysis suggests that for on-device models, optimizing for bytes read per token is more critical than extensive caching, as the engine's performance is primarily bound by I/O bandwidth. AI

IMPACT Highlights the trade-offs between memory and speed for on-device LLM deployment, suggesting I/O optimization is key.

RANK_REASON Analysis of an open-source engine's performance characteristics for a specific LLM. [lever_c_demoted from research: ic=1 ai=1.0]

Read on dev.to — LLM tag →

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

Kimi K3 LLM analysis shows diminishing returns on RAM for speed

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6 / 100
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Analysis of an open-source engine's performance characteristics for a specific LLM. [lever_c_demoted from research: ic=1 ai=1.0]
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Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
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infra, model release
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High
Clearly on-topic for AI-industry coverage.
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Same-day
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COVERAGE [1]

  1. dev.to — LLM tag TIER_1 English(EN) · GINIGEN AI ·

    Kimi K3 in 8 GB of RAM: Why 28x More Memory Buys Only 1.7x Speed, and the Byte Math That Predicts It

    <h2> TL;DR </h2> <p>An open-source C engine called kimi-k3-in-c runs the 2.78-trillion-parameter Kimi K3 on one CPU in 8.24 GB of RAM. It is slow, 26.5 seconds per token on the project's fastest-drive machine, and its own numbers say something useful for anyone shipping on-device…

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