dphn · text · mixture of experts

dolphin-2.5-mixtral-8x7b

dphn/dolphin-2.5-mixtral-8x7b

dolphin-2.5-mixtral-8x7b at Q4_K_M is exactly 26,441,544,544 bytes (24.63 GiB / 26.44 GB) — an effective 4.529 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
QuantSize●Exact bytes●Effective bpw●Tensors●Publisher
Q2_K14.57 GiB15,644,044,2562.680—TheBloke
Q2_K16.12 GiB17,309,182,8482.965—MaziyarPanahi
Q3_K_M18.96 GiB20,363,366,1763.488—TheBloke
Q3_K_S19.03 GiB20,430,474,9443.500—MaziyarPanahi
Q3_K_M21.00 GiB22,544,404,1603.862—MaziyarPanahi
Q3_K_L22.51 GiB24,167,599,8084.140—MaziyarPanahi
Q4_K_M24.63 GiB26,441,544,5444.529—TheBloke
Q4_024.63 GiB26,441,544,5444.529—TheBloke
Q4_K_S24.91 GiB26,743,534,3364.581—MaziyarPanahi
Q4_K_M26.49 GiB28,446,421,7604.873—MaziyarPanahi
Q5_K_S30.02 GiB32,229,291,7765.521—MaziyarPanahi
Q5_030.02 GiB32,229,291,8725.521—TheBloke
Q5_K_M30.02 GiB32,229,291,8725.521—TheBloke
Q5_K_M30.95 GiB33,227,536,1285.692—MaziyarPanahi
Q6_K35.74 GiB38,378,773,3126.574—MaziyarPanahi
Q6_K35.74 GiB38,378,773,4086.574—TheBloke
Q8_046.22 GiB49,624,279,7448.500—MaziyarPanahi
Q8_046.22 GiB49,624,279,8408.500—TheBloke

KV cache by context

computed per layer
ContextKV cache (f16)●Flat formulaOverstated byFull / windowed / recurrent
4,0960.50 GiB0.50 GiB—32 / 0 / 0
8,1921.00 GiB1.00 GiB—32 / 0 / 0
16,3842.00 GiB2.00 GiB—32 / 0 / 0
32,7684.00 GiB4.00 GiB—32 / 0 / 0
65,5368.00 GiB8.00 GiB—32 / 0 / 0
131,07216.00 GiB16.00 GiB—32 / 0 / 0

Compare with

same modality, comparable size
vs Qwen3-Coder-30B-A3B-Instructvs Qwen3.6-27Bvs Qwen3.8-27Bvs gemma-4-12B-it-qat-q4_0-unquantizedvs gemma-4-E4B-itvs Qwen3-30B-A3B-Thinking-2507All alternatives →Will it fit on my card? →

Will it run on your card?

full quant x context sweep
By VRAM:8GB12GB16GB24GB32GB48GB
Radeon RX 6500 XT 4GBGeForce RTX 3050 6GBGeForce RTX 5050 8GBGeForce RTX 3080 10GBGeForce RTX 2080 Ti 11GBGeForce RTX 5070 12GBGeForce RTX 5060 Ti 16GBApple M3 Pro 18GBGeForce RTX 3080 Ti 20GBGeForce RTX 5090 D V2 24GBGeForce RTX 5090 D 32GBApple M5 Max 36GBApple M5 Pro 48GBApple M5 Max 64GBApple M3 Ultra 96GBApple M5 Max 128GBApple M2 Ultra 192GBApple M3 Ultra 256GBApple M3 Ultra 512GB

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 24.63 GiB, because a quantization is a mixture and some tensors are always kept at higher precision.

Architecture

from config.json
Layers
32
Attention heads
32
KV heads
8
Head dim
128
Hidden size
4096
Vocab
32,002
Sliding window
none
SWA period
—
MLA
no
Experts
8
Experts per token
2
use_sliding_window
—

Questions people ask

How much VRAM does dolphin-2.5-mixtral-8x7b need?
Q4_K_M is exactly 26,441,544,544 bytes (24.63 GiB) in weights. Add the KV cache, which depends on your context length, plus roughly half a gigabyte of runtime overhead.
How large is dolphin-2.5-mixtral-8x7b'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 dolphin-2.5-mixtral-8x7b 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 dolphin-2.5-mixtral-8x7b 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.