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DeepSeek-V4.1-Flash-1017 runs locally on single RTX 5090 via Project Maya

By PulseAugur Editorial · [1 sources] ·

A guide details how to run the 748B parameter DeepSeek-V4.1-Flash-1017 large language model locally on a single RTX 5090 GPU using Project Maya. The setup requires approximately 350-400 GB of NVMe storage and leverages Maya's engine to manage model experts across GPU, RAM, and disk. Measurements indicate speeds of around 21-25 tokens per second with a 262K context window. AI

IMPACT Enables local execution of large models on consumer hardware, potentially increasing accessibility for developers and researchers.

RANK_REASON Guide on running a specific LLM with a specific tool on consumer hardware.

Read on dev.to — LLM tag →

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

DeepSeek-V4.1-Flash-1017 runs locally on single RTX 5090 via Project Maya

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0 / 100
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Tool
Guide on running a specific LLM with a specific tool on consumer hardware.
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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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model release, infra
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High
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3 days old
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COVERAGE [1]

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

    Run DeepSeek-V4.1-Flash (748B) locally with Project Maya on one RTX 5090

    <p><strong>DeepSeek-V4.1-Flash-1017 has 748B parameters. Even Project Maya's own 2-3 bit quant is 340 GB with its Engram tables, and my workstation has 128 GB of RAM.</strong> It still runs here: on one RTX 5090, with a 262K context, at about 21 tokens per second. This guide show…

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