mradermacher/Meta-Llama-3.1-405B-Instruct-GGUF

About

static quants of https://huggingface.co/meta-llama/Meta-Llama-3.1-405B-Instruct

weighted/imatrix quants are available at https://huggingface.co/mradermacher/Meta-Llama-3.1-405B-Instruct-i1-GGUF

Usage

If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files.

Provided Quants

(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

Link	Type	Size/GB	Notes
PART 1 PART 2 PART 3 PART 4	Q2_K	151.3
PART 1 PART 2 PART 3 PART 4	IQ3_XS	168.2
PART 1 PART 2 PART 3 PART 4	Q3_K_S	177.2
PART 1 PART 2 PART 3 PART 4	IQ3_S	177.7	beats Q3_K*
PART 1 PART 2 PART 3 PART 4	IQ3_M	183.9
PART 1 PART 2 PART 3 PART 4	Q3_K_M	197.6	lower quality
P1 P2 P3 P4 P5	Q3_K_L	215.3
P1 P2 P3 P4 P5	IQ4_XS	221.3
P1 P2 P3 P4 P5	Q4_K_S	233.0	fast, recommended
P1 P2 P3 P4 P5	Q4_K_M	245.8	fast, recommended
P1 P2 P3 P4 P5 P6	Q5_K_S	282.3
P1 P2 P3 P4 P5 P6	Q5_K_M	289.8
P1 P2 P3 P4 P5 P6 P7	Q6_K	336.5	very good quality
P1 P2 P3 P4 P5 P6 P7 P8 P9	Q8_0	435.8	fast, best quality

Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):

And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9

FAQ / Model Request

See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized.

Thanks

I thank my company, nethype GmbH, for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.