Performance of llama.cpp with Vulkan #10879
Replies: 189 comments 288 replies
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AMD FirePro W8100
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With the latest updates:
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With FA:
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AMD RX 470
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With the latest updates:
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With FA:
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i got the mining edition 8 gb ran slightly better
build: e288693 (6242) |
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ubuntu 24.04, vulkan and cuda installed from official APT packages.
build: 4da69d1 (4351) vs CUDA on the same build/setup
build: 4da69d1 (4351) |
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Macbook Air M2 on Asahi Linux ggml_vulkan: Found 1 Vulkan devices:
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For the record I think this is slow on the HoneyKrisp side rather than llama.cpp |
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Can you share how you got vulkan to build on Asahi? I can't seem to get cmake to notice it. |
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Spoke too soon, got it working! |
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Gentoo Linux on ROG Ally (2023) Ryzen Z1 Extreme ggml_vulkan: Found 1 Vulkan devices:
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ggml_vulkan: Found 4 Vulkan devices:
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Cool setup! Could you also post the result of 1, 2 and 3 7900 XTX GPUs? You can use only the first GPU with |
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For this multi GPU case getting Vulkan to support #6017 pipeline parallelism might help improve the prompt processing speed. |
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@netrunnereve I updated the commit id in all my result. |
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build: 0d52a69 (4439) NVIDIA GeForce RTX 3090 (NVIDIA)
AMD Radeon RX 6800 XT (RADV NAVI21) (radv)
AMD Radeon (TM) Pro VII (RADV VEGA20) (radv)
Intel(R) Arc(tm) A770 Graphics (DG2) (Intel open-source Mesa driver)
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@netrunnereve Some of the tg results here are a little low, I think they might be debug builds. The cmake step (at least on Linux) might require |
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I've added My release numbers for the RX 470 are basically identical to the ones I posted earlier without the flag.
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Maybe not in your case, but some other results are suspiciously low in tg (for example the RTX 3080) |
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Build: 8d59d91 (4450)
Lack of proper Xe coopmat support in the ANV driver is a setback honestly.
edit: retested both with the default batch size. |
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I put my A770 into a Windows PC and gave Intel GPA and vtune a shot: GPA just crashes most of the time, I couldn't get it to trace anything useful. vtune works, but does not support Vulkan. It just shows some high-level metrics in that case, not really useful sadly. |
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I did build it with cmake with build type Release. |
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In that case it's something else, cause it should be performing similarly to my A770. I suspect the mesa version, there was something in newer mesa versions that slowed down tg on Intel. |
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A750 has 448 CUs, A770 has 512 CUs I think. Personally, I am not worried about tg. I am worried about pp here. The gemm batch quickly saturates my GPU. |
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@0cc4m https://gitlab.freedesktop.org/mesa/mesa/-/issues/12585 |
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Here's something exotic: An AMD FirePro S10000 dual GPU from 2012 with 2x 3GB GDDR5. build: 914a82d (4452)
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Very interesting, and looks like it's pretty close to the W8100 in tg despite being a dual GPU card. Your backend scales pretty well with layer splitting which is why I find it worthwhile to run my RX470 and W8100 together (I end up getting results that are close to the average of both cards).
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Latest arch with For the sake of consistency I run every bit in a script and also build every target from scratch (for some reason kill -STOP -1
timeout 240s $COMMAND
kill -CONT -1
ggml_vulkan: Found 1 Vulkan devices:
ggml_vulkan: 0 = Intel(R) Iris(R) Xe Graphics (TGL GT2) (Intel open-source Mesa driver) | uma: 1 | fp16: 1 | warp size: 32 | matrix cores: none
build: ff3fcab (4459)
This bit seems to underutilise both GPU and CPU in real conditions based on
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Unless you reduce the number of GPU layers, threads and openblas/non-openblas is not gonna make any difference. Try it with ngl 0, then only prompt processing is accelerated using Vulkan, the rest runs on CPU. This is often a good setting for integrated GPUs. |
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That's something I didn't think about, with
build: ba8a1f9 (4460)
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It seems latest patches has improved the results a bit: ggml_vulkan: Found 1 Vulkan devices:
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A few months later and I get:
build: f3a4b16 (5568) I run it on Linux (Arch with lama.cpp-vulkan-git package compiled by GCC 15). From my tests, only Vulkan backend ( I'm curious why I cannot go over 6 t/s. Is this an issue with the newer llama.cpp version or with my OS configuration? |
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Intel ARC A770 on Windows:
build: ba8a1f9 (4460) |
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Single GPU VulkanRadeon Instinct MI25 ggml_vulkan: 0 = AMD Radeon Instinct MI25 (RADV VEGA10) (radv) | uma: 0 | fp16: 1 | warp size: 64 | matrix cores: none
build: 2739a71 (4461) Radeon PRO VII ggml_vulkan: 0 = AMD Radeon Pro VII (RADV VEGA20) (radv) | uma: 0 | fp16: 1 | warp size: 64 | matrix cores: none
build: 2739a71 (4461) Multi GPU Vulkanggml_vulkan: 0 = AMD Radeon Pro VII (RADV VEGA20) (radv) | uma: 0 | fp16: 1 | warp size: 64 | matrix cores: none
build: 2739a71 (4461) ggml_vulkan: 0 = AMD Radeon Pro VII (RADV VEGA20) (radv) | uma: 0 | fp16: 1 | warp size: 64 | matrix cores: none
build: 2739a71 (4461) Single GPU RocmDevice 0: AMD Radeon Instinct MI25, compute capability 9.0, VMM: no
build: 2739a71 (4461) Device 0: AMD Radeon Pro VII, compute capability 9.0, VMM: no
build: 2739a71 (4461) Multi GPU RocmDevice 0: AMD Radeon Pro VII, compute capability 9.0, VMM: no
build: 2739a71 (4461) Layer split
build: 2739a71 (4461) Row split
build: 2739a71 (4461) Single GPU speed is decent, but multi GPU trails Rocm by a wide margin, especially with large models due to the lack of row split. |
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What is the power profile for this MI25? Mine is 110W but its running slower than yours on git from today. |
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Mine defaults to 220w. |
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AMD Radeon RX 5700 XT on Arch using mesa-git and setting a higher GPU power limit compared to the stock card.
I also think it could be interesting adding the flash attention results to the scoreboard (even if the support for it still isn't as mature as CUDA's).
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There is no Vulkan flash attention support (except with coopmat2 on very new nvidia drivers). What you're measuring here is a CPU fallback. |
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I see, I was sure about the CPU fallback but didn't know there was no flash attention support at all. |
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I tried but there's nothing after 1 hrs , ok, might be 40 mins... Anyway I run the llama_cli for a sample eval...
Meanwhile OpenBLAS |
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Even at below 1t/s |
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Truth is ... (0.79 tokens per second), 3788.63 ms / 3 tokens So it's not even...it just slower... |
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Newer builds seem to have better tg performance than pp performance or I'm confused as to what I am doing wrong to get lower pp performance on my XTX.
I'll have to do an upgrade later. |
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Titan V (12 GB / HBM2 / 3072 bit) ggml_vulkan: Found 1 Vulkan devices:
build: e56abd2 (6794) I wonder if there's a way to actually utilize the matrix cores. |
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NVIDIA GeForce RTX 4080 SUPEROS: NixOS / Linux 6.16.11-xanmod1
build: 81086cd (6729) |
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Radeon 660M (Ryzen 5 6600H), dual channel DDR5 4800MT/s
build: ee09828 (6795) |
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Radeon 680M (Ryzen 5 6800H), LPDDR5 6400 MT/s
build: ee09828 (6795) |
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RTX 3070 Laptop GPU (8 GB / GDDR6 / 256 bit) Driver Version: 580.76.05
build: ceff6bb (6783) |
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RX Vega 56
build: 66b0dbc (6791) |
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I know that's an old run but interestingly this is beating the Vega 64 by quite a bit. |
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Another Strix Halo 395+ (128GB). Also with the optimizations alluded to in the Strix Halo benchmarking guide. radv, mesa 26.0.0 amdvulkan |
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AMD Radeon RX 480 $ llama-bench --device vulkan2 -ngl 100 -m llama-2-7b.Q4_0.gguf -fa 0,1
build: 0bcb40b (6833) |
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AMD Ryzen 5 5600H with Vega 7 iGPU. The package is capped at 35W. 2x 32GB DDR4 3200MHz (generic). Slightly more surprising is this: |
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@pt13762104 Yes, most likely. Memory bandwidth is a limiting factor here I think. However those models make local LLMs feasible on platforms that were never thought to be sufficient for it. Here's another example, 7B MoE, running completely on the iGPU and staying relatively cool. |
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You can uncap TDP for package / iGPU. https://github.com/JamesCJ60/Universal-x86-Tuning-Utility/releases I use this, and on my humble R5 4650U I GPU runs so fast you can play BG3 in full HD with decent FPS. |
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I know, but (a) this is a tiny mini PC that fits in my palm - it simply doesn't provide the cooling or voltage regulators; (b) the wall wart delivers 55W max; and (c) this isn't Windows (but POR can be edited in the BIOS). |
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Wall wart hahahahahaha made me laugh aloud |
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I get ~80 tps on a MX150 with 13 layers offloaded on my old laptop, it's quite simmilar to this, but tg is only 3 tps due to the laptop having only 1 channels of DDR4 |
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b6840, macOS Sequoia AMD Radeon RX 6900 XT, eGPU, TB3, iMac Pro ./llama.cpp/build/bin/llama-bench -m ./GGUF/llama-2-7b.Q4_0.gguf -ngl 100 -fa 0,1 -sm none -mg 0
AMD Radeon Pro Vega 64, internal, iMac Pro ./llama.cpp/build/bin/llama-bench -m ./GGUF/llama-2-7b.Q4_0.gguf -ngl 100 -fa 0,1 -sm none -mg 1
FA ALL: ./llama/llama-bench -m ./GGUF/llama-2-7b.Q4_0.gguf -ngl 100 -fa all -sm none -mg 0
./llama/llama-bench -m ./GGUF/llama-2-7b.Q4_0.gguf -ngl 100 -fa all -sm none -mg 1
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Radeon Vega 6 APU
(AMD Ryzen 5 PRO 4650U) .\llama-bench.exe -m llama-2-7b.Q4_0.gguf -ngl 100 -fa 0,1
FA ALL .\llama-bench.exe -m llama-2-7b.Q4_0.gguf -ngl 100 -fa all
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AMD Radeon 880M (Ryzen AI 9 365) Tested with RADV and AMDVLK. % AMD_VULKAN_ICD=AMDVLK ./build-vk/bin/llama-bench -m llama-2-7b.Q4_0.gguf -fa 0,1
% AMD_VULKAN_ICD=RADV ./build-vk/bin/llama-bench -m llama-2-7b.Q4_0.gguf -fa 0,1
build: c55d53a (6854) Edit: it's reported as 890M but it's 880M. |
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This is similar to the Apple Silicon benchmark thread, but for Vulkan! 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
Either run the commands below or download one of our Vulkan releases. If you have multiple GPUs please run the test on a single GPU using
-sm none -mg YOUR_GPU_NUMBERunless the model is too big to fit in VRAM.Share your llama-bench results along with the git hash and Vulkan info string in the comments. Feel free to try other models and compare backends, but only valid runs will be placed on the scoreboard.
If multiple entries are posted for the same setup I'll prioritize newer commits with substantial Vulkan updates, otherwise I'll pick the one with the highest overall score at my discretion. Performance may vary depending on driver, operating system, board manufacturer, etc. even if the chip is the same. For integrated graphics note that the memory speed and number of channels will greatly affect your inference speed!
Vulkan Scoreboard (Click on the headings to expand the section)
Llama 2 7B, Q4_0, no FA
Llama 2 7B, Q4_0, FA enabled
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