Performance of llama.cpp with Vulkan

[netrunnereve]

This is similar to the Apple Silicon benchmark thread, but for Vulkan! Many improvements have been made to the Vulkan backend in the past month and I think it's good to consolidate and discuss our results here.

We'll be testing the Llama 2 7B model like the other thread to keep things consistent, and use Q4_0 as it's simple to compute and small enough to fit on a 4GB GPU. You can download it here.

Instructions

wget https://huggingface.co/TheBloke/Llama-2-7B-GGUF/resolve/main/llama-2-7b.Q4_0.gguf
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
mkdir build
cd build
cmake .. -DGGML_VULKAN=on
make
llama-bench -m ../../llama-2-7b.Q4_0.gguf -ngl 100 (add any extra options here)

Share your llama-bench results along with the git hash and Vulkan info string in the comments. Feel free to try other models, compare backends, and so forth, but only valid runs will be placed on the scoreboard.

Vulkan Scoreboard for Llama 2 7B, Q4_0

Rank	GPU	pp512 t/s	tg128 t/s	Commit	Comments
1	Nvidia RTX 3080	1706.07 ± 139.33	62.16 ± 1.98	4da69d1
2	AMD RX 470	161.47 ± 0.43	33.45 ± 0.04	4da69d1
3	AMD FirePro W8100	137.10 ± 0.44	28.51 ± 0.12	4da69d1

[netrunnereve]

AMD FirePro W8100

ggml_vulkan: 0 = AMD Radeon FirePro W8100 (RADV HAWAII) (radv) | uma: 0 | fp16: 0 | warp size: 64 | matrix cores: none
build: 4da69d1a (4351)

model	size	params	backend	ngl	threads	sm	test	t/s
llama 7B Q4_0	3.56 GiB	6.74 B	Vulkan	100	8	none	pp512	137.10 ± 0.44
llama 7B Q4_0	3.56 GiB	6.74 B	Vulkan	100	8	none	tg128	28.51 ± 0.12

[netrunnereve]

AMD RX 470

ggml_vulkan: 1 = AMD Radeon RX 470 Graphics (RADV POLARIS10) (radv) | uma: 0 | fp16: 0 | warp size: 64 | matrix cores: none
build: 4da69d1a (4351)

model	size	params	backend	ngl	threads	main_gpu	sm	test	t/s
llama 7B Q4_0	3.56 GiB	6.74 B	Vulkan	100	8	1	none	pp512	161.47 ± 0.43
llama 7B Q4_0	3.56 GiB	6.74 B	Vulkan	100	8	1	none	tg128	33.45 ± 0.04

[max-krasnyansky]

ubuntu 24.04, vulkan and cuda installed from official APT packages.

ggml_vulkan: 0 = NVIDIA GeForce RTX 3080 (NVIDIA) | uma: 0 | fp16: 1 | warp size: 32 | matrix cores: KHR_coopmat

model	size	params	backend	ngl	test	t/s
llama 7B Q4_0	3.56 GiB	6.74 B	Vulkan	99	pp512	1706.07 ± 139.33
llama 7B Q4_0	3.56 GiB	6.74 B	Vulkan	99	tg128	62.16 ± 1.98

build: 4da69d1 (4351)

vs CUDA on the same build/setup

ggml_cuda_init: GGML_CUDA_FORCE_MMQ:    no
ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
ggml_cuda_init: found 1 CUDA devices:
  Device 0: NVIDIA GeForce RTX 3080, compute capability 8.6, VMM: yes

model	size	params	backend	ngl	test	t/s
llama 7B Q4_0	3.56 GiB	6.74 B	CUDA	99	pp512	4499.47 ± 60.66
llama 7B Q4_0	3.56 GiB	6.74 B	CUDA	99	tg128	131.01 ± 0.43

build: 4da69d1 (4351)