How much VRAM does Llama-3.1-8B-Instruct-abliterated need?

Q5_K_M is exactly 5,732,992,160 bytes (5.34 GiB) in weights. Add the KV cache, which depends on context length, plus roughly half a gigabyte of runtime overhead.

huihui-ai · text

Llama-3.1-8B-Instruct-abliterated

huihui-ai/Llama-3.1-8B-Instruct-abliterated

Llama-3.1-8B-Instruct-abliterated at Q5_K_M is exactly 5,732,992,160 bytes (5.34 GiB / 5.73 GB) — an effective 5.711 bits per weight, not the nominal 5.

From the file· summed from 1 file(s)

Parameters

8.0B

Architecture

llama

Context

—

native (config.json)

License

other

Shipped quantizations

● exact bytes, summed from published files

Quant	Size●	Exact bytes●	Effective bpw●	Tensors●	Publisher
Q5_K_M	5.34 GiB	5,732,992,160	5.711	—	c4tdr0ut
Q8_0	7.95 GiB	8,540,775,584	8.509	—	c4tdr0ut

Compare with

same modality, comparable size

vs Qwen3-Coder-30B-A3B-Instruct vs Qwen3.6-27B vs Qwen3.8-27B vs gemma-4-12B-it-qat-q4_0-unquantized vs gemma-4-E4B-it vs Qwen3-30B-A3B-Thinking-2507 All alternatives →Will it fit on my card? →

Why other calculators give a different number

A parameters × bits ÷ 8 estimate puts Q5_K_M at roughly 4.21 GiB. The real file is 5.34 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-3.1-8B-Instruct-abliterated need?: Q5_K_M is exactly 5,732,992,160 bytes (5.34 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-3.1-8B-Instruct-abliterated 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.