How much VRAM does Open_Gpt4_8x7B_v0.2 need?

Q4_K_M is exactly 28,379,301,312 bytes (26.43 GiB) in weights. Add the KV cache, which depends on context length, plus roughly half a gigabyte of runtime overhead.

How large is Open_Gpt4_8x7B_v0.2's KV cache?

4.00 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 Open_Gpt4_8x7B_v0.2 a mixture-of-experts model?

Yes — 8 experts with 2 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.

rombodawg · text · mixture of experts

Open_Gpt4_8x7B_v0.2

rombodawg/Open_Gpt4_8x7B_v0.2

Open_Gpt4_8x7B_v0.2 at Q4_K_M is exactly 28,379,301,312 bytes (26.43 GiB / 28.38 GB) — an effective 4.861 bits per weight, not the nominal 4. Its KV cache at 32K is 4.00 GiB.

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

Parameters

46.7B

total, not active

Architecture

llama

32 layers

Context

32,768

native (config.json)

License

apache-2.0

Shipped quantizations

● exact bytes, summed from published files

Quant	Size●	Exact bytes●	Effective bpw●	Tensors●	Publisher
Q2_K	15.99 GiB	17,170,761,152	2.941	—	TheBloke
Q3_K_M	20.93 GiB	22,477,284,800	3.850	—	TheBloke
Q4_0	24.63 GiB	26,441,532,864	4.529	—	TheBloke
Q4_K_M	26.43 GiB	28,379,301,312	4.861	—	TheBloke
Q5_0	30.02 GiB	32,229,279,168	5.521	—	TheBloke
Q5_K_M	30.95 GiB	33,227,523,520	5.692	—	TheBloke
Q6_K	35.74 GiB	38,378,759,616	6.574	—	TheBloke
Q8_0	46.22 GiB	49,624,262,080	8.500	—	TheBloke

KV cache by context

computed per layer

Context	KV cache (f16)●	Flat formula	Overstated by	Full / windowed / recurrent
4,096	0.50 GiB	0.50 GiB	—	32 / 0 / 0
8,192	1.00 GiB	1.00 GiB	—	32 / 0 / 0
16,384	2.00 GiB	2.00 GiB	—	32 / 0 / 0
32,768	4.00 GiB	4.00 GiB	—	32 / 0 / 0
65,536	8.00 GiB	8.00 GiB	—	32 / 0 / 0
131,072	16.00 GiB	16.00 GiB	—	32 / 0 / 0

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 Q4_K_M at roughly 24.47 GiB. The real file is 26.43 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

128

Hidden size

4096

Vocab

32,000

Sliding window

none

SWA period

—

MLA

Experts

Experts per token

use_sliding_window

—

Questions people ask

How much VRAM does Open_Gpt4_8x7B_v0.2 need?: Q4_K_M is exactly 28,379,301,312 bytes (26.43 GiB) in weights. Add the KV cache, which depends on your context length, plus roughly half a gigabyte of runtime overhead.
How large is Open_Gpt4_8x7B_v0.2's KV cache?: 4.00 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 Open_Gpt4_8x7B_v0.2 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 Open_Gpt4_8x7B_v0.2 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.