How much VRAM does Qwen3.5-35B-A3B-Uncensored-FernflowerAI-safetensors need?

F16 is exactly 69,376,637,568 bytes (64.61 GiB) in weights. Add the KV cache, which depends on context length, plus roughly half a gigabyte of runtime overhead.

LuffyTheFox · text

Qwen3.5-35B-A3B-Uncensored-FernflowerAI-safetensors

LuffyTheFox/Qwen3.5-35B-A3B-Uncensored-FernflowerAI-safetensors

Qwen3.5-35B-A3B-Uncensored-FernflowerAI-safetensors at F16 is exactly 69,376,637,568 bytes (64.61 GiB / 69.38 GB) — an effective 16.013 bits per weight, not the nominal 1.

From the file· summed from 1 file(s)

Parameters

34.7B

Architecture

qwen35moe

Context

—

native (config.json)

License

apache-2.0

Shipped quantizations

● exact bytes, summed from published files

Quant	Size●	Exact bytes●	Effective bpw●	Tensors●	Publisher
F16	64.61 GiB	69,376,637,568	16.013	—	mudler

Compare with

same modality, comparable size

vs Qwen3-Coder-30B-A3B-Instruct vs Qwen3.6-27B vs Qwen3.8-27B vs DeepSeek-V4-Flash vs Qwen3-30B-A3B-Thinking-2507 vs Laguna-XS-2.1 All alternatives →Will it fit on my card? →

Why other calculators give a different number

A parameters × bits ÷ 8 estimate puts F16 at roughly 18.16 GiB. The real file is 64.61 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 Qwen3.5-35B-A3B-Uncensored-FernflowerAI-safetensors need?: F16 is exactly 69,376,637,568 bytes (64.61 GiB) in weights. Add the KV cache, which depends on your context length, plus roughly half a gigabyte of runtime overhead.
Which quantization of Qwen3.5-35B-A3B-Uncensored-FernflowerAI-safetensors 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.