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Groot-19B-A3B model runs 256K context on RTX 5090, revealing memory limits

By PulseAugur Editorial · [1 sources] ·

A technical deep-dive explores the practicalities of running the Groot-19B-A3B language model, which claims a 256K-token context window, on a single RTX 5090 GPU. The analysis reveals that achieving this context length requires careful memory management, with the model's weights consuming a significant portion of the GPU's memory, leaving limited space for the KV cache. The article also highlights that the model's AI

IMPACT Demonstrates practical memory constraints for large context windows on consumer GPUs, impacting deployment feasibility.

RANK_REASON Technical deep-dive into running a specific LLM on consumer hardware, focusing on practical implementation challenges rather than a novel release or research breakthrough.

Read on dev.to — LLM tag →

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

Groot-19B-A3B model runs 256K context on RTX 5090, revealing memory limits

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39 / 100
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Technical deep-dive into running a specific LLM on consumer hardware, focusing on practical implementation challenges rather than a novel release or research breakthrough.
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model release
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High
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Breaking (< 6h)
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COVERAGE [1]

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

    Serving Groot-19B-A3B at 256K on one RTX 5090: the memory budget and the thinking trap

    <p>GrootTeam says its Groot-19B-A3B holds a 256K-token context on one RTX 5090, and labels that figure an estimate from memory sizes. A token is a word piece, and the context is how much text the model can see at once. We wanted a measurement, so we deployed the 20.9 GB FP8 build…

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