How much VRAM does Ornith-Agents-A1-3.7-35B-A3B-dare_ties_v4 need?

Q4_K_M is exactly 22,064,715,488 bytes (20.55 GiB) in weights. Add the KV cache, which depends on context length, plus roughly half a gigabyte of runtime overhead.

How large is Ornith-Agents-A1-3.7-35B-A3B-dare_ties_v4's KV cache?

0.63 GiB at 32K context with an f16 cache, computed per layer from the model's architecture. Quantizing the cache to q8_0 roughly halves it.

Is Ornith-Agents-A1-3.7-35B-A3B-dare_ties_v4 a mixture-of-experts model?

Yes — 256 experts with 8 routed per token. Every expert must be resident in memory, but only the routed ones are read per token, so its memory requirement and its speed behave very differently.

tepirale · text · mixture of experts

Ornith-Agents-A1-3.7-35B-A3B-dare_ties_v4

tepirale/Ornith-Agents-A1-3.7-35B-A3B-dare_ties_v4

Ornith-Agents-A1-3.7-35B-A3B-dare_ties_v4 at Q4_K_M is exactly 22,064,715,488 bytes (20.55 GiB / 22.06 GB) — an effective 5.093 bits per weight, not the nominal 4. Its KV cache at 32K is 0.63 GiB.

From the file· summed from 1 file(s)From the file· KV per layer

Parameters

34.7B

total, not active

Architecture

qwen35moe

40 layers

Context

262,144

native (config.json)

License

apache-2.0

Shipped quantizations

● exact bytes, summed from published files

Quant	Size●	Exact bytes●	Effective bpw●	Tensors●	Publisher
Q4_K_M	20.55 GiB	22,064,715,488	5.093	—	tepirale
Q5_K_M	23.87 GiB	25,627,088,608	5.915	—	tepirale
Q6_K	27.39 GiB	29,412,110,048	6.789	—	tepirale
Q8_02 shards	35.60 GiB	38,222,149,888	8.822	—	tepirale
F162 shards	65.34 GiB	70,159,449,376	16.194	—	tepirale

KV cache by context

computed per layer

Context	KV cache (f16)●	Flat formula	Overstated by	Full / windowed / recurrent
4,096	0.08 GiB	0.31 GiB	4.00×	10 / 0 / 30
8,192	0.16 GiB	0.63 GiB	4.00×	10 / 0 / 30
16,384	0.31 GiB	1.25 GiB	4.00×	10 / 0 / 30
32,768	0.63 GiB	2.50 GiB	4.00×	10 / 0 / 30
65,536	1.25 GiB	5.00 GiB	4.00×	10 / 0 / 30
131,072	2.50 GiB	10.00 GiB	4.00×	10 / 0 / 30

30 of 40 layers use linear attention, which keeps a fixed-size recurrent state instead of a per-token cache. Those layers do not grow with context at all — treating them as ordinary attention, as a flat formula does, overstates this model's cache by roughly 4.0× at long context.

Compare with

same modality, comparable size

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

Why other calculators give a different number

A parameters × bits ÷ 8 estimate puts Q4_K_M at roughly 18.16 GiB. The real file is 20.55 GiB, because a quantization is a mixture and some tensors are always kept at higher precision.

Architecture

from config.json

Layers

Attention heads

KV heads

Head dim

256

Hidden size

2048

Vocab

248,320

Sliding window

none

SWA period

—

MLA

Experts

256

Experts per token

use_sliding_window

—

Questions people ask

How much VRAM does Ornith-Agents-A1-3.7-35B-A3B-dare_ties_v4 need?: Q4_K_M is exactly 22,064,715,488 bytes (20.55 GiB) in weights. Add the KV cache, which depends on your context length, plus roughly half a gigabyte of runtime overhead.
How large is Ornith-Agents-A1-3.7-35B-A3B-dare_ties_v4's KV cache?: 0.63 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 Ornith-Agents-A1-3.7-35B-A3B-dare_ties_v4 a mixture-of-experts model?: Yes — 256 experts, 8 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 Ornith-Agents-A1-3.7-35B-A3B-dare_ties_v4 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.