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Best Nets for Lc0

In general, for game analysis and long calculation time per move, the largest network compatible with your hardware is recommended. In use cases with very low calculation time per move or slow hardware, a smaller network might be a better choice.

Network SizePurposeFiltersBlocksGPU Memory UsageFile SizeNetwork
LargeGPU76815 (mish activation)2.4 GB160-170 MBT1-768x15x24h-swa-4000000 (Right-click → “Save link as…”)
MediumGPU/CPU51215 (mish activation)1.8 GB140-150 MBT1-512x15x8h-distilled-swa-3395000 (Right-click → “Save link as…”)
SmallGPU/CPU25610 (mish activation)1.6 GB30-40 MBT1-256x10-distilled-swa-2432500 (Right-click → “Save link as…”)
Very SmallSparring vs. Humans≤128≤10-≤10 MBsee below

T1 networks above are contributed by masterkni6. The larger 768x15 network is comparable in architecture with networks in current training run1.

If you’re getting out of memory errors when using large networks on GPU, pick the next best network in the list or try adding --backend-opts=max_batch=256 to LC0 command (or UCI option: BackendOptions: max_batch=256), default: 1024. This will reduce GPU memory usage without any negative impact on playing strength. With the cuDNN backend you can also try --backend-opts=custom_winograd=false or as a UCI option: BackendOptions: custom_winograd=false.

Note for DirectX12 and OpenCL backend users: The format of the networks in the list above is not supported. However, you can download and use the LC0 ONNX-DML version instead, see the included README file for instructions on how to get the directml.dll that can’t be included in the package for licensing reasons. Alternatively you can use older networks such as the last T78 512x40 network 782344 or the last T60 384x30 network 611246.


Network Lists

Listed for completeness, includes networks from older training runs. Some download links might be outdated.

In each section, the nets are listed roughly in descending order of strength. Some may be too close to tell apart.

30 blocks x 384 filters:

NameSource for DownloadNotes
Latest T60 after 606512lczero.org run 1 networksFinished main run
hanse-69722-vf2Contributed networks on Lc0 dataTrained from 609722 on T60 data, value focus emphasizes positions with eval discrepancies. See here
J94-100 (outdated)Contributed networks on Lc0 dataBased on Sergio-V networks, trained on T60 data + value repair method. TCEC22 DivP+SuFi net
SV-3972+jio-20k (outdated)Contributed networks on Lc0 dataSubmitted for TCEC 18 Superfinal
384x30-t60-3010 (outdated)Contributed networks on Lc0 dataWon CCC13 and TCEC 17

24 blocks x 320 filters:

NameSource for DownloadNotes
T60 until 606511lczero.org run 1 networksFinished main run
J13B.2-136GitHub: jhorthos Leela Training“Terminator 2” Net

20 blocks x 256 filters:

NameSource for DownloadNotes
Leelenstein 15.015.0 PostNo account required
SV-20b-t40-1541removedTrained on T40 data
42850training.lczero.org direct downloadLast T40 net

15/16 blocks x 192 filters:

NameSource for DownloadNotes
Latest T79lczero.org run 2 networksFinished 2nd test run, LC0 v0.29 required
Latest T75lczero.org run 3 networksFinished 3rd test run
Latest T76lczero.org run 2 networksFinished 2nd test run
Latest T77lczero.org run 2 networksFinished 2nd test run
J64-210GitHub: jhorthos Leela TrainingTrained on T60 data
J20-460GitHub: jhorthos Leela TrainingTrained on T40 data

10 blocks x 128 filters:

NameSource for DownloadNotes
Latest T74lczero.org run 2 networksFinished 2nd test run
128x10-t60-2-5300removedTrained on T60 data
Tinker TK-6430Google DriveTrained on T60 data
Latest J104 netGitHub: jhorthos Leela TrainingBased on T70 network 703810, trained on T70 data + value repair method
703810training.lczero.org direct downloadLast T70 net (not to be confused with T72)
591226training.lczero.org direct downloadLast T59 net
Little Demon 2data.lczero.org repository (LD2)JH nets also here

Assorted sizes:

SizeNameSource for DownloadNotes
19b x 256fT71.5-Armageddon-Chesslczero.org run 3 network 715893Trained from scratch on Armageddon Chess
19b x 256fT71.4-FischerRandomChesslczero.org run 3 network 714700Trained from scratch on Fischer Random Chess
9b x 112fID11258-112x9-seGitHub: dkappe Distilled NetworksOther sizes also here
5b x 48fGood Gyal 5GitHub: dkappe Bad GyalOther sizes also here
2b x 16fTiny GyalGitHub: dkappe Bad GyalOther sizes also here

If you still have questions, check the Discord channels. Be sure to specify your hardware and use case so the helpful regulars know what to recommend.

Last Updated: 2023-08-18