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DeepSeek V4 Flash runs 1M context locally on RTX 5090 with llamacpp patch

A user has developed a patch for the llamacpp library that enables the DeepSeek V4 Flash model to run with a 1 million token context window locally on an RTX 5090 graphics card. This modification addresses issues with the model's indexing and implements a CUDA kernel, significantly reducing VRAM requirements from an estimated 256GB to approximately 3.75GB for the 1M context. The patch has been verified for correctness with a needle-in-a-haystack test and offers improved prefill speeds. AI

IMPACT Enables local execution of large-context models on consumer hardware, potentially broadening access and use cases for advanced AI.

RANK_REASON User-developed patch for an existing tool (llamacpp) to run a specific model (DeepSeek V4) with enhanced capabilities (1M context locally).

Read on r/LocalLLaMA →

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

DeepSeek V4 Flash runs 1M context locally on RTX 5090 with llamacpp patch

COVERAGE [1]

  1. r/LocalLLaMA TIER_1 English(EN) · /u/da_dragon321 ·

    llamacpp patch - DeepSeek V4 Flash running with full 1M token context locally on RTX 5090

    <!-- SC_OFF --><div class="md"><p>Wanted to try running DeepSeek V4 Flash locally but found it asking for absurd amounts of VRAM at higher context lengths (~256GB at 1M). Turned out the DSA lightning indexer lacks proper llamacpp support. Did a bit of digging and there's an upstr…