Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to.
Previously, you would download Q4_0_4_4/4_8/8_8, and these would have their weights interleaved in memory in order to improve performance on ARM and AVX machines by loading up more data in one pass.
Now, however, there is something called "online repacking" for weights. details in this PR. If you use Q4_0 and your hardware would benefit from repacking weights, it will do it automatically on the fly.
As of llama.cpp build b4282 you will not be able to run the Q4_0_X_X files and will instead need to use Q4_0.
Additionally, if you want to get slightly better quality for , you can use IQ4_NL thanks to this PR which will also repack the weights for ARM, though only the 4_4 for now. The loading time may be slower but it will result in an overall speed incrase.
Click to view Q4_0_X_X information (deprecated
I'm keeping this section to show the potential theoretical uplift in performance from using the Q4_0 with online repacking.
Click to view benchmarks on an AVX2 system (EPYC7702)
model
size
params
backend
threads
test
t/s
% (vs Q4_0)
qwen2 3B Q4_0
1.70 GiB
3.09 B
CPU
64
pp512
204.03 ± 1.03
100%
qwen2 3B Q4_0
1.70 GiB
3.09 B
CPU
64
pp1024
282.92 ± 0.19
100%
qwen2 3B Q4_0
1.70 GiB
3.09 B
CPU
64
pp2048
259.49 ± 0.44
100%
qwen2 3B Q4_0
1.70 GiB
3.09 B
CPU
64
tg128
39.12 ± 0.27
100%
qwen2 3B Q4_0
1.70 GiB
3.09 B
CPU
64
tg256
39.31 ± 0.69
100%
qwen2 3B Q4_0
1.70 GiB
3.09 B
CPU
64
tg512
40.52 ± 0.03
100%
qwen2 3B Q4_K_M
1.79 GiB
3.09 B
CPU
64
pp512
301.02 ± 1.74
147%
qwen2 3B Q4_K_M
1.79 GiB
3.09 B
CPU
64
pp1024
287.23 ± 0.20
101%
qwen2 3B Q4_K_M
1.79 GiB
3.09 B
CPU
64
pp2048
262.77 ± 1.81
101%
qwen2 3B Q4_K_M
1.79 GiB
3.09 B
CPU
64
tg128
18.80 ± 0.99
48%
qwen2 3B Q4_K_M
1.79 GiB
3.09 B
CPU
64
tg256
24.46 ± 3.04
83%
qwen2 3B Q4_K_M
1.79 GiB
3.09 B
CPU
64
tg512
36.32 ± 3.59
90%
qwen2 3B Q4_0_8_8
1.69 GiB
3.09 B
CPU
64
pp512
271.71 ± 3.53
133%
qwen2 3B Q4_0_8_8
1.69 GiB
3.09 B
CPU
64
pp1024
279.86 ± 45.63
100%
qwen2 3B Q4_0_8_8
1.69 GiB
3.09 B
CPU
64
pp2048
320.77 ± 5.00
124%
qwen2 3B Q4_0_8_8
1.69 GiB
3.09 B
CPU
64
tg128
43.51 ± 0.05
111%
qwen2 3B Q4_0_8_8
1.69 GiB
3.09 B
CPU
64
tg256
43.35 ± 0.09
110%
qwen2 3B Q4_0_8_8
1.69 GiB
3.09 B
CPU
64
tg512
42.60 ± 0.31
105%
Q4_0_8_8 offers a nice bump to prompt processing and a small bump to text generation
A great write up with charts showing various performances is provided by Artefact2 here
The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have.
If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.
If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total.
Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'.
If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M.
If you want to get more into the weeds, you can check out this extremely useful feature chart:
But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size.
These I-quants can also be used on CPU and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide.
The I-quants are not compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm.
