{"v":1,"id":"model:hf:AtomicChat/Qwen3.8-Flash-Next-GGUF","slug":"model-atomicchat-qwen3-8-flash-next-gguf","kind":"model","category":"llm","title":"Qwen3.8-Flash-Next-GGUF","summary":"Built from Qwen's original weights with our own importance matrix. The calibration corpora behind our builds are public.","source":{"provider":"hf","ref":"AtomicChat/Qwen3.8-Flash-Next-GGUF","url":"https://huggingface.co/AtomicChat/Qwen3.8-Flash-Next-GGUF","rev":"142262902a46f7daed19c79d0771534c8106ad59","fetchedAt":"2026-10-02T20:59:53.544Z","etag":"W/\"a1aa-cp8JBzveQdXzNBQ2mZXqLO6UBNg\""},"author":{"name":"AtomicChat","url":"https://huggingface.co/AtomicChat"},"license":{"spdx":null,"raw":"other","url":"https://huggingface.co/Qwen/Qwen3.8-Flash-Next/blob/main/LICENSE","open":null,"note":"custom license: read it at the source before installing"},"metrics":{"downloads":634102,"downloadsWeek":327838,"likes":170,"takenAt":"2026-10-02T20:59:53.544Z"},"tags":["gguf","atomic-chat","qwen","qwen3.8","flash-next","moe","multimodal","imatrix","quantized","llama.cpp","text-generation","endpoints_compatible","conversational"],"pipeline":"text-generation","links":{"github":"QwenLM/Qwen3","npm":"node-llama-cpp"},"updatedAt":"2026-08-27T01:36:22.000Z","collectedAt":"2026-10-02T20:59:53.544Z","review":{"numbers":["634,102 downloads on Hugging Face","170 likes","license other","0.5 GB for imatrix.gguf","327,838 npm downloads a week for node-llama-cpp","latest node-llama-cpp@3.22.1"],"log":null},"trust":"unlabeled","health":{"status":"alive","checkedAt":"2026-10-02T20:59:53.544Z","http":200},"description":"# How to Run Qwen3.8-Flash-Next Locally\n\n  Built from Qwen's original weights with our own importance matrix. The calibration corpora behind our builds are public.\n\n  Qwen3.8-Flash-Next is the first open-weight release of the architecture behind Qwen4.\n  These GGUFs are self-quantized from Qwen's original weights with our own importance matrix, published alongside the quants.\n  The quants are still uploading and need a llama.cpp build with Qwen3.8-Flash-Next support; Atomic Chat runs it as support ships.\n\n## Running a 176B model on a 64 GB MacBook\n\nQwen3.8-Flash-Next has 177B parameters. Our 85 GB quant runs on an M5 Max with\n64 GB of memory, with vision, at 36 tok/s. That is not a typo: the file is\nlarger than the machine's entire RAM.\n\nIt works because 39 GB of that file never enters memory at all.\n\n```\nmemory breakdown [MiB]  |  total    free     self   model  context  compute\n  - MTL0 (Apple M5 Max) |  57344 = 13217 + (44125 = 43720 +   256 +    148)\n  - Host                |                   37279 = 37265 +     0 +     14\n```\n\n### Why this is possible\n\n51B of the model's 177B parameters are not weights in the usual sense. They are\nan **n-gram lookup table**. The model hashes the last three tokens, and that\nhash points at 16 rows of 160 values each. Roughly 2.7 KB per token, read once\nper forward pass, out of a 39 GB table.\n\nThat is a 1-in-13-million read ratio, at a deterministic address. At 36 tok/s it\ncomes to about 3 MB/s of random reads, and NVMe answers in under 100 µs against\na 28 ms per-token budget. Common n-grams stay in page cache anyway.\n\nCompare that with the experts: they touch about 6B parameters per token,\ngigabytes of traffic, and would be hopeless from disk. That is why ordinary\noffloading fails when you run out of memory, and why this table is different.\n\n> [!IMPORTANT]\n> On Apple Silicon this only works if the table sits in **its own GGUF shard**.\n> llama.cpp hands Metal the…\n\nSource: 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