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

A parameters × bits ÷ 8 estimate puts Q4_K_M at roughly 9.64 GiB. The real file is 10.54 GiB, because a quantization is a mixture and some tensors are always kept at higher precision.

How much VRAM does Llama-3.2-8X3B-MOE-Dark-Champion-Instruct-uncensored-abliterated-18.4B need?

Q4_K_M is exactly 11,312,942,112 bytes (10.54 GiB) in weights. Add the KV cache, which depends on your context length, plus roughly half a gigabyte of runtime overhead.

How large is Llama-3.2-8X3B-MOE-Dark-Champion-Instruct-uncensored-abliterated-18.4B's KV cache?

3.50 GiB at 32K context with an f16 cache, computed per layer. Quantizing the cache to q8_0 roughly halves it, which is often the difference between a context length fitting and not.

Is Llama-3.2-8X3B-MOE-Dark-Champion-Instruct-uncensored-abliterated-18.4B a mixture-of-experts model?

Yes — 8 experts, 2 routed per token. Every expert must be resident, but only the routed ones are read per token, which is why its memory requirement and its speed behave very differently.

Which quantization of Llama-3.2-8X3B-MOE-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.