16 community quantized versions of the official V4.1-Flash-1017 weights, covering GGUF (llama.cpp and several specialized engines), EXL3 (multi-GPU vLLM / exllamav3), MLX, and Apple Silicon-specific formats; this edition reviewed each model card individually, with no uncensored or fine-tuned versions included.
Total downloads: 1515552 · Total likes: 843
Reasons
- ISTA-DASLab's GSQ-RCO GGUF offers three tiers: IQ1_M, IQ2_XXS, and IQ2_XS (totaling approximately 231–266 GB). After the 10-27 update, it can be loaded using llama.cpp mainline b11870 or later, supporting image input. The author claims that IQ2_XXS performs on par with the original on AIME25, GPQA-Diamond, and LiveCodeBench v6 (author's self-tests, reasoning effort 50).
- For machines with 128 GB of RAM, there are several options: the author states that Maya-XS (Project Maya engine) has a resident memory footprint of 115.3 GB; the ds4 version of the Q2 GGUF is designed for SSD streaming reads on 128 GB Macs; unsloth claims that the smallest 1-bit version requires more than 128 GB of RAM.
- For multi-GPU setups, there are EXL3 solutions: the diffbot version targets 2 RTX PRO 6000 cards, while the franken-v100 version targets 8 V100 32GB cards; LibertAI's 2.9 bpw version includes a chat_template.jinja ported from the official encoder, which the author claims renders identically to the reference implementation across 8,455 test cases.
- Total download counts and likes only reflect attention levels.
Usage
- llama.cpp: hf download ISTA-DASLab/DeepSeek-V4.1-Flash-1017-GSQ-RCO-GGUF --include "*IQ2_XXS*" "*Engram*" --local-dir ., symlink the Engram files as the 6th shard according to the model card, then load using llama-cli -m …-00001-of-00006.gguf.
- Apple Silicon: mlx-community's oQ4e / oQ2.5e (oMLX) or beamster's Sushi-STQ 1.75bpw (requires sushi v1.4.0+, macOS 26.2+).
- Most EXL3 packages do not include the two Engram tables; you need to fetch the 47th and 48th shards separately from the official repository (each approximately 95 GiB).
Risks
- Even the smallest version, including the Engram tables, is several hundred GB; Engram files must be placed on a fast SSD. Different repositories require different engines and branches (the mrfakename version still requires a specific llama.cpp branch, and the unsloth version requires its PR or Unsloth Desktop); check the corresponding model card before downloading.
- Quality metrics (KLD, top-1 agreement rate, benchmark scores) are all self-tested by the respective authors, with varying methodologies; the lossless-CSF version uses a custom LIL license and requires a specialized loader.