dphn · text · mixture of experts

dolphin-2.6-mixtral-8x7b

dphn/dolphin-2.6-mixtral-8x7b

dolphin-2.6-mixtral-8x7b at Q4_K_M is exactly 26,441,545,664 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
QuantSizeExact bytesEffective bpwTensorsPublisher
I1-IQ1_S9.15 GiB9,821,678,3681.682mradermacher
I1-IQ1_M10.10 GiB10,847,185,6961.858mradermacher
I1-IQ2_XXS11.69 GiB12,556,364,5762.151mradermacher
I1-IQ2_XS12.97 GiB13,923,707,6802.385mradermacher
I1-IQ2_S13.16 GiB14,127,861,3442.420mradermacher
I1-IQ2_M14.43 GiB15,495,204,4482.654mradermacher
Q2_K14.57 GiB15,644,045,3762.680TheBloke
Q2_K16.12 GiB17,311,239,7762.965mradermacher
I1-Q2_K16.12 GiB17,311,240,0322.965mradermacher
I1-IQ3_XXS16.99 GiB18,242,473,5683.125mradermacher
IQ3_XS18.02 GiB19,350,401,4403.315mradermacher
I1-IQ3_XS18.02 GiB19,350,401,6963.315mradermacher
Q3_K_M18.96 GiB20,363,367,2963.488TheBloke
Q3_K_S19.03 GiB20,432,531,8723.500mradermacher
IQ3_S19.03 GiB20,432,531,8723.500mradermacher
I1-IQ3_S19.03 GiB20,432,532,1283.500mradermacher
I1-Q3_K_S19.03 GiB20,432,532,1283.500mradermacher
IQ3_M19.96 GiB21,430,776,2243.671mradermacher
I1-IQ3_M19.96 GiB21,430,776,4803.671mradermacher
Q3_K_M21.00 GiB22,546,461,0883.862mradermacher
I1-Q3_K_M21.00 GiB22,546,461,3443.862mradermacher
Q3_K_L22.51 GiB24,169,656,7364.140mradermacher
I1-Q3_K_L22.51 GiB24,169,656,9924.140mradermacher
I1-IQ4_XS23.36 GiB25,080,550,8804.296mradermacher
IQ4_XS23.63 GiB25,374,151,9044.346mradermacher
Q4_K_M24.63 GiB26,441,545,6644.529TheBloke
Q4_024.63 GiB26,441,545,6644.529TheBloke
I1-Q4_024.74 GiB26,561,042,1444.550mradermacher
Q4_K_S24.91 GiB26,745,591,2644.581mradermacher
I1-Q4_K_S24.91 GiB26,745,591,5204.581mradermacher
Q4_K_M26.49 GiB28,448,478,6884.873mradermacher
I1-Q4_K_M26.49 GiB28,448,478,9444.873mradermacher
Q5_030.02 GiB32,229,292,9925.521TheBloke
Q5_K_M30.02 GiB32,229,292,9925.521TheBloke
Q5_K_S30.02 GiB32,231,348,7045.521mradermacher
I1-Q5_K_S30.02 GiB32,231,348,9605.521mradermacher
Q5_K_M30.95 GiB33,229,593,0565.692mradermacher
I1-Q5_K_M30.95 GiB33,229,593,3125.692mradermacher
Q6_K35.74 GiB38,378,774,5286.574TheBloke
Q6_K35.74 GiB38,380,830,2406.574mradermacher

KV cache by context

computed per layer
ContextKV cache (f16)Flat formulaOverstated byFull / windowed / recurrent
4,0960.50 GiB0.50 GiB32 / 0 / 0
8,1921.00 GiB1.00 GiB32 / 0 / 0
16,3842.00 GiB2.00 GiB32 / 0 / 0
32,7684.00 GiB4.00 GiB32 / 0 / 0
65,5368.00 GiB8.00 GiB32 / 0 / 0
131,07216.00 GiB16.00 GiB32 / 0 / 0

Compare with

same modality, comparable size

Will it run on your card?

full quant x context sweep

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-2.6-mixtral-8x7b need?
Q4_K_M is exactly 26,441,545,664 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-2.6-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-2.6-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-2.6-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.