User achieves 1M context window on 17GB model using KVarN quantization

By PulseAugur Editorial · [1 sources] · 2026-08-10 11:38

A user on Reddit's r/LocalLLaMA forum reported successfully loading a large language model with a 1 million token context window, utilizing approximately 17 GB of VRAM on a 24 GB VRAM graphics card. This was achieved using a 35 billion parameter model based on Qwen, with a KVarN 4-bit quantization method for the KV cache. The user was able to extract information from various parts of the text, indicating that the context window was functional and not corrupted. AI

IMPACT Demonstrates potential for running larger context windows on consumer hardware, improving efficiency for complex tasks.

RANK_REASON User report on achieving a technical feat with existing models and quantization techniques, not a direct release from a frontier lab.

Read on r/LocalLLaMA →

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

User achieves 1M context window on 17GB model using KVarN quantization

COVERAGE [1]

r/LocalLLaMA TIER_1 English(EN) · /u/Anbeeld · 2026-08-10 11:38

1M context with 17 GB model in 24 GB VRAM: "for the first time I was able to load a context of almost 1M tokens and extract 7 needles from various parts of the text"

<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1vkicyd/1m_context_with_17_gb_model_in_24_gb_vram_for_the/"> <img alt="1M context with 17 GB model in 24 GB VRAM: "for the first time I was able to load a context of almost 1M tokens and extract 7 needles…

COVERAGE [1]

1M context with 17 GB model in 24 GB VRAM: "for the first time I was able to load a context of almost 1M tokens and extract 7 needles from various parts of the text"

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