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Mistral-7B-Instruct-v0.1

mistralai/Mistral-7B-Instruct-v0.1

Mistral-7B-Instruct-v0.1 at Q4_K_M is exactly 4,368,438,944 bytes (4.07 GiB / 4.37 GB) — an effective 4.826 bits per weight, not the nominal 4.

From the file· summed from 1 file(s)From the file· KV per layer
Parameters
7.2B
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_S1.50 GiB1,612,103,9041.781mradermacher
I1-IQ1_M1.63 GiB1,754,448,0961.938mradermacher
I1-IQ2_XXS1.85 GiB1,991,688,4162.200mradermacher
I1-IQ2_XS2.05 GiB2,198,257,8882.428mradermacher
I1-IQ2_S2.15 GiB2,310,922,4642.553mradermacher
I1-IQ2_M2.33 GiB2,500,714,7202.763mradermacher
Q2_K2.53 GiB2,719,242,4643.004QuantFactory
I1-Q2_K2.53 GiB2,719,244,5123.004mradermacher
I1-IQ3_XXS2.63 GiB2,827,346,1443.123mradermacher
I1-IQ3_XS2.81 GiB3,018,817,7603.335mradermacher
Q2_K2.87 GiB3,083,097,7603.406TheBloke
Q3_K_S2.95 GiB3,164,567,2003.496TheBloke
Q3_K_S2.95 GiB3,164,567,7763.496QuantFactory
I1-Q3_K_S2.95 GiB3,164,569,8243.496mradermacher
I1-IQ3_S2.96 GiB3,182,395,6163.516mradermacher
I1-IQ3_M3.06 GiB3,284,893,9203.629mradermacher
Q3_K_M3.28 GiB3,518,985,8883.888TheBloke
Q3_K_M3.28 GiB3,518,986,4643.888QuantFactory
I1-Q3_K_M3.28 GiB3,518,988,5123.888mradermacher
Q3_K_L3.56 GiB3,822,024,3524.222TheBloke
Q3_K_L3.56 GiB3,822,024,9284.222QuantFactory
I1-Q3_K_L3.56 GiB3,822,026,9764.222mradermacher
I1-IQ4_XS3.64 GiB3,907,690,7204.317mradermacher
Q4_03.83 GiB4,108,916,3844.539TheBloke
Q4_03.83 GiB4,108,916,9604.539QuantFactory
I1-Q4_03.84 GiB4,123,599,0724.555mradermacher
Q4_K_S3.86 GiB4,140,373,6644.574TheBloke
Q4_K_S3.86 GiB4,140,374,2404.574QuantFactory
I1-Q4_K_S3.86 GiB4,140,376,2884.574mradermacher
Q4_K_M4.07 GiB4,368,438,9444.826TheBloke
Q4_K_M4.07 GiB4,368,439,5204.826QuantFactory
I1-Q4_K_M4.07 GiB4,368,441,5684.826mradermacher
Q5_04.65 GiB4,997,715,6165.521TheBloke
Q5_K_S4.65 GiB4,997,715,6165.521TheBloke
Q5_04.65 GiB4,997,716,1925.521QuantFactory
Q5_K_S4.65 GiB4,997,716,1925.521QuantFactory
I1-Q5_K_S4.65 GiB4,997,718,2405.521mradermacher
Q5_K_M4.78 GiB5,131,409,0565.669TheBloke
Q5_K_M4.78 GiB5,131,409,6325.669QuantFactory
I1-Q5_K_M4.78 GiB5,131,411,6805.669mradermacher

KV cache by context

unresolved

This model declares a 4,096-token sliding window, but we could not establish which layers use it. Its architecture publishes the layout as a per-layer array inside the model file rather than as a period in config.json, and we have not yet ingested that array.

A flat context × layers × heads figure would be substantially too high, so we are not showing one. This is tracked as a known gap rather than filled with a guess.

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 3.79 GiB. The real file is 4.07 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
32,000
Sliding window
4096
SWA period
MLA
no
Experts
Experts per token
use_sliding_window

Questions people ask

How much VRAM does Mistral-7B-Instruct-v0.1 need?
Q4_K_M is exactly 4,368,438,944 bytes (4.07 GiB) in weights. Add the KV cache, which depends on your context length, plus roughly half a gigabyte of runtime overhead.
Which quantization of Mistral-7B-Instruct-v0.1 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.