DavidAU · text

Llama-3.2-8X3B-GATED-MOE-Reasoning-Dark-Champion-Instruct-uncensored-abliterated-18.4B

DavidAU/Llama-3.2-8X3B-GATED-MOE-Reasoning-Dark-Champion-Instruct-uncensored-abliterated-18.4B

Llama-3.2-8X3B-GATED-MOE-Reasoning-Dark-Champion-Instruct-uncensored-abliterated-18.4B at Q4_K_M is exactly 11,408,360,768 bytes (10.62 GiB / 11.41 GB) — an effective 4.959 bits per weight, not the nominal 4.

From the file· summed from 1 file(s)
Parameters
18.4B
Architecture
llama
Context
native (config.json)
License
apache-2.0

Shipped quantizations

exact bytes, summed from published files
QuantSizeExact bytesEffective bpwTensorsPublisher
Q2_K6.65 GiB7,136,326,9763.102DavidAU
Q3_K_S7.77 GiB8,348,224,8323.629DavidAU
Q3_K_M8.50 GiB9,122,073,9203.965DavidAU
Q3_K_L9.06 GiB9,733,426,4964.231DavidAU
Q4_K_S10.02 GiB10,757,195,0724.676DavidAU
Q4_K_M10.62 GiB11,408,360,7684.959DavidAU
Q5_K_S11.99 GiB12,877,907,2645.598DavidAU
Q5_K_M12.34 GiB13,252,248,8965.760DavidAU
Q6_K14.54 GiB15,615,851,8406.788DavidAU
IQ4_XS2 shards19.39 GiB20,823,411,3289.051DavidAU
Q8_02 shards37.87 GiB40,658,012,80017.673DavidAU

Compare with

same modality, comparable size

Will it run on your card?

full quant x context sweep

Why other calculators give a different number

A parameters × bits ÷ 8 estimate puts Q4_K_M at roughly 9.64 GiB. The real file is 10.62 GiB, because a quantization is a mixture and some tensors are always kept at higher precision.

Architecture

Architecture unavailable — this repository is gated and no ungated mirror was found. Exact file sizes above are still authoritative; only the KV math needs the config.

Questions people ask

How much VRAM does Llama-3.2-8X3B-GATED-MOE-Reasoning-Dark-Champion-Instruct-uncensored-abliterated-18.4B need?
Q4_K_M is exactly 11,408,360,768 bytes (10.62 GiB) in weights. Add the KV cache, which depends on your context length, plus roughly half a gigabyte of runtime overhead.
Which quantization of Llama-3.2-8X3B-GATED-MOE-Reasoning-Dark-Champion-Instruct-uncensored-abliterated-18.4B should I use?
Q4_K_M is the usual default. Pick the largest quantization that fits your card at the context you actually need — the table above gives exact sizes for every one published.