LCZero Benchmarks
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ThreadsEngine version/typeSpeed npsNeural Net NameRemark
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RTX 3080 & 30706lc0 v0.2720x25613122911248lc0 -t 6 --backend=multiplexing --backend-opts="backend=cuda-fp16,a(gpu=0),b(gpu=1),c(gpu=0),d(gpu=1)" -w weights_11248.txt --nncache=2000000 --minibatch-size=1024
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RTX 3080 & 2070 Super6lc0 v0.26.320x25612009611248lc0 -t 6 --backend=multiplexing --backend-opts="backend=cuda-fp16,a(gpu=0),b(gpu=1),c(gpu=0),d(gpu=1)" -w weights_11248.txt --nncache=2000000 --minibatch-size=1024
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RTX 30904lc0 v0.26.320x25610100642850-t 4 -b cuda-fp16 --nncache=2000000 --minibatch-size=1024
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3 x RTX 2080 TI2lc0 v0.23.1+git.6837b8320x2568081442850-t 2 --backend=demux "--backend-opts=(backend=cudnn-fp16,gpu=0),(backend=cudnn-fp16,gpu=1),(backend=cudnn-fp16,gpu=2)" --nncache=2000000 --minibatch-size=1024; go nodes 5000000
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2x RTX TITAN4lc0 v0.20 dev (with PR 619)20x25680000--threads=4 --backend=roundrobin --nncache=10000000 --cpuct=3.0 --minibatch-size=256 --max-collision-events=64 --max-prefetch=64 --backend-opts=(backend=cudnn-fp16,gpu=0),(backend=cudnn-fp16,gpu=1) go infinite; NPS checked after 100 seconds (peak was over 100k, then it starts dropping)
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4xV1004lc0 cuda92 cudnn714 ubuntu20x2567870010040./lc0 --backend=multiplexing --backend-opts="x(backend=cudnnhalf,gpu=0,max_batch=512),y(backend=cudnnhalf,gpu=1,max_batch=512),yy(backend=cudnnhalf,gpu=2,max_batch=512),yyy(backend=cudnnhalf,gpu=3,max_batch=512)" --no-smart-pruning --minibatch-size=1024 --threads=4
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RTX 2070 Super & 20704lc0 v0.20 dev (with PR 619)20x2567840132332same as above
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RTX 2070 Super & 207012lc0 v0.22.020x2567605242425ba
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lc0-cudnn Batchsize=256 node-collisions=32
20x25666723kb1-256x20-2100000l
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GTX 1070 @ stock2lc0 v0.20.320x2565415032930-t 2 --backend=cudnn-fp16 --minibatch-size=1024 --nncache=20000000; go nodes 5000000
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mkl3lc0 v0.20.420x2565383811248--threads=3 --backend=roundrobin --nncache=10000000 --cpuct=3.0 --minibatch-size=256 --max-collision-events=64 --max-prefetch=64 --backend-opts=(backend=cudnn-fp16,gpu=0) go infinite; NPS checked at 100 seconds
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2 x RTX 20604lc0 v0.22.020x2565241040685-t 4 -backend=demux -nncache=1000000 -minibatch-size=512 -max-prefetch=32 -backend-opts=(backend=cudnn-fp16,gpu=0),(backend=cudnn-fp16,gpu=1) go nodes 5000000
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2 x RTX 20604lc0 v0.22.020x32052340T40B.2-106-t 4 -backend=demux -nncache=1000000 -minibatch-size=512 -max-prefetch=32 -backend-opts=(backend=cudnn-fp16,gpu=0),(backend=cudnn-fp16,gpu=1) go nodes 5000000
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RTX Titan3lc0 v0.20.1-rc120x2565055832392--minibatch-size=512 -t 3 --backend=cudnn-fp16 --nncache=10000000; go infinite; note down NPS after 1 min
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RTX 2080 Ti @ 338W (~1785 MHz)2
lc0 v0.20.2 (linux: fedora 29, 415.27, cuda 10.0, cudnn 7.4.2.24)
20x2565045632392-t 2 --backend=cudnn-fp16 --minibatch-size=1024 --nncache=20000000; go nodes 5000000
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2lc0 v0.20.1-rc120x25646446--backend=cudnn-fp16 --nncache=10000000; go infinite; note down NPS after 1 min
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RTX Titan3
lc0 v0.20.2 (linux: fedora 29, 415.27, cuda 10.0, cudnn 7.4.2.24)
20x2563939032930--minibatch-size=512 -t 3 --backend=cudnn-fp16 --nncache=2000000; go nodes 5000000
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RTX 2070 (slight OC, +66Mhz core)lc0-v0.19.1.1-windows-cuda.zip20x2563172332085--minibatch-size=1024 -t 2 --backend=multiplexing --backend-opts="x(backend=""cudnn-fp16"",gpu=0)" ; go nodes 1000000
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2lc0 v0.18.13143311250
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TITAN V2lc0 v0.20.1-rc220x2563100410048--minibatch-size=512 -t 2 --backend=cudnn-fp16 --nncache=2000000; go nodes 1000000
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6lc0-v0.19.020x2562932931748lc0 -t 6 -w weights_31748.txt --backend=multiplexing "--backend-opts="a(backend=cudnn-fp16,gpu=0,minibatch-size=512,nncache=2000000),b(backend=cudnn,gpu=1)"
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lc0-v0.18.1-windows-cuda10.0-cudnn7.3-for-2080.zip
2613511250?--futile-search-aversion=0 --minibatch-size=1024 -t 2 --backend-opts="x(backend=""cudnn-fp16"",gpu=0)"
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RTX 2070lc0 v0.26.320x2562548042850lc0 benchmark default settings
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RTX2080(laptop)2lc0 v0.22.020x25624142T40B.2-106lc0.exe -b cudnn-fp16 -w T40B.4-160;go nodes 100000
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2lc0-v0.20.2?2179732742--minibatch-size=512 -t 2 --backend=cudnn-fp16 --nncache=2000000; go nodes 1000000
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RTX 20604
lc0-v0.18.1-windows-cuda10.0-cudnn7.3-for-2080.zip
20x2562141311250.\lc0 --weights=weights_run1_11248.pb.gz --threads=4 --minibatch-size=256 --allowed-node-collisions=256 --cpuct=2.8 --nncache=10000000 --backend=multiplexing --backend-opts="(backend=cudnn,gpu=0),(backend=cudnn,gpu=1)"
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4lc0-v0.18.120x2562141311248
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Radeon R9 390X2lc0-v0.26.3-windows10-gpu-dx1210x12819897703810lc0 benchmark
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RTX 2060 (laptop)lc0 v0.26.320x2561752242850lc0 benchmark default settings
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GeForce GTX 780 Ti4lc0-v0.26.020x256110001541000Default Settings, backend = cudnn-fp16, NNCacheSize = 20000000, MiniBatchSize = 1024, MaxCollisionsEvents = 1024, Threads = 4
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RX 5700 XT2lc0-v0.25.1-windows10-gpu-dx1220x2561098142850./lc0 benchmark
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GTX 1080 TiLC0 V17.2 dev20x256920810954GPU load 98-99%, GPU Temp <= 82°C, Fanspeed 95%, go movetime 130 000
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GTX 1650 SUPER2lc0-v0.24.1-windows-gpu-nvidia-cuda20x256877342850./lc0 benchmark --threads=2 --backend=cudnn-fp16 --minibatch-size=512 --movetime=50000
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GTX 10802?800011250
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2x GTX 1060 (6GB)1lc0-win-20180526 (cuda 9.2)20x2567596kb1-256x20-2000000-t 12 --backend=multiplexing "--backend-opts=(backend=cudnn-fp16,gpu=0),(backend=cudnn-fp16,gpu=1)" --nncache=2000000 --minibatch-size=1024; go nodes 5000000
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4LC0 Ver 17 RC2 (Windows)700510970-t 4 --minibatch-size=512 --backend=multiplexing --backend-opts=(backend=cudnn,gpu=0,max_batch=1024),(backend=cudnn,gpu=1,max_batch=1024)
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GTX 1070Ti2lc0.20.1 (cuda)20x256649632965--nncache=8000000 --max-collision-events=256 --minibatch-size=256 --backend=multiplexing --cpuct=3.1
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GTX 1070Ti2Lc0 v0.1720x256565711149--futile-search-aversion=0 (the equivalent of --no-smart-pruning) and otherwise default settings
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2LC0 Ver 17 RC2 (Windows)20x256359810970--threads=1 --fpu-reduction=0.2 --cpuct=1.2 --slowmover=1.5 --move-overhead=10 --no-smart-pruning
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GeForce GTX 1060 (3GB)lc0 v0.24.1 cuda 10.0.0 cudnn 7.4.220x2563365LS 14.3lc0 benchmark (v0.24.1)
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GTX 1060 (6GB)2lc0 v0.24.1 (cuda 10.2)20x2563359
20x256SE-jj-9-53420000
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GTX980m 10%underclock1lc0-win-20180522 (cuda 9.2)20x2562258kb1-256x20-2000000
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GTX 9602lc0 v0.18.1 windows cuda20x256212311248
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GTX980m 10%underclock220x2561855kb1-256x20-2000000--no-smart-pruning (value adjusted from 1093 to 1855 after rerun)
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GTX 750 Ti @ 1350/1350 MHz1lc0 v0.18.1 windows cuda20x256165811258GPU Load 98%, GPU Temp 59°C, Fan Speed 39%, unknown command line flag --no-smart-pruning; -nncache 300000; go nodes 725000
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AMD R9 Fury X (core + mem 20% overclock)
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v0.21.2-rc3 (Manjaro, OpenCL 2.1 AMD-APP (2841.4))
20x256151842512result at 130.000 nodes, peaks at 550.000 nodes at 1880nps, then starts decreasing
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GTX 9501
v0.16.0 custom build for Debian (cuda 9.1.85)
20x256150110687--no-smart-pruning --minibatch-size=1024 --threads=2
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GTX 750 Ti @ stock2lc0 v0.18.1 windows cudaWhat is the "0"131411258GPU Load 100%, GPU Temp 50°C, Fan Speed 35%, unknown command line flag --no-smart-pruning
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Nvidia Quadro K22002LC0 Ver 20.1 rc1 (Windows)20x256130432194
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AMD RX480 (core + mem 20% overclock)
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v0.21.2-rc3 (Manjaro, OpenCL 2.1 AMD-APP (2841.4))
20x256113342512result at 130.000 nodes, peaks at 540.000 nodes at 1385nps, then starts decreasing
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Nvidia Geforce 840M4
v0.21.2-rc3 (Manjaro, cuda 10.1.0, cudnn 7.5.1)
20x25643342512
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li16v0.21.2 OpenBlas (Windows)20x25628842699
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GTX 4702lczero v0.10 OpenCL (Windows)20x25613010021
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AMD Ryzen 3 1200 @stock4lczero v0.10 OpenBlas (Windows)20x2563510021tested using bumblebee + optirun, peaks at 570.000 nodes at 539nps, then starts decreasing, stays around 527-528nps.gp
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Intel Core i7-4900MQ 4x 2,80GHz 500GB 16GB Quadro K2100M RW
LC0 Ver 20.1 rc1 (Windows)20x25632805This was performed at the starting position. Whole game average was 63k (see latest paper and talkchess clarifications)
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TITAN V3lc0-win-20180708-cuda92-cudnn71420x25611248
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MX1502LC0 Ver 17 RC2 (Windows)20x25611045--minibatch-size=256 --backend=cudnn
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TITAN XP220x25610751This benchmark is done in Arena Chess. I suspect on Cutechess-cli Leela will achieve an even higher NPS.
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(for cudnn client start with --no-smart-pruning flag and do "go nodes 130000" and wait until it finishes). For faster GPUs let it run for 1m or even 5m nodes.
×x
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CPU
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GeForce GTX 780 Ti
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Have clean up for the last time! after that i will close it!
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LCZero Benchmark Nodes/sec. GPU & CPU
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Please put your own bench scores here in sorted NPS order if you can. If you don't know what engine type, gpu is opencl and cpu is openblas + Networks ID !
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RTX 2080 Ti @ 1290 MHz (~169W)2-t 2 --backend=cudnn-fp16 --minibatch-size=1024 --nncache=20000000; go nodes 5000000
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Run go infinite from start position and abort after depth 30 and report NPS output.
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4x TPU20x256?--threads=4 --backend=roundrobin --nncache=10000000 --cpuct=3.0 --minibatch-size=256 --max-collision-events=64 --max-prefetch=64 --backend-opts=(backend=cudnn-fp16,gpu=0),(backend=cudnn-fp16,gpu=1) go infinite; NPS checked after 100 seconds (peak was over 100k, then it starts dropping)
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