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Networks
The networks below are our strongest available, and the first listed (BT4-spsa-1740) is what is currently being sent to engine competitions like the TCEC and CCC. In general, the largest network compatible with your hardware is recommended. To download, right click the corresponding link and select "Save link as..."
| Network Size | Purpose | Filters | Blocks | GPU Memory Usage | File Size | Network |
|---|---|---|---|---|---|---|
| Very Large | Large GPU | 1024 | 15 | 4 GB | 380 MB | BT4-spsa-1740 |
| Very Large | Large GPU | 1024 | 15 | 4 GB | 330 MB | BT4-1024x15x32h-swa-6147500 |
| Large | GPU | 768 | 15 | 2.6 GB | 190 MB | BT3-768x15x24h-swa-2790000 |
| Large | GPU | 768 | 15 | 2.4 GB | 160-170 MB | T82-768x15x24h-swa-7464000 |
| Medium | GPU/CPU | 512 | 15 | 1.8 GB | 150-155 MB | t3-512x15x16h-distill-swa-2767500 |
| Medium | GPU/CPU | 512 | 15 | 1.8 GB | 140-150 MB | T1-512x15x8h-distilled-swa-3395000 |
| Small | GPU/CPU | 256 | 10 | 1.6 GB | 30-40 MB | T1-256x10-distilled-swa-2432500 |
| Very Small | Sparring vs. Humans | ≤128 | ≤10 | - | ≤10 MB | see below |
If you're getting out of memory errors when using large networks on GPU, you can tell the engine to process positions in smaller chunks by adding --minibatch-size=16 to the lc0 command or config file. Alternatively, you can switch to a smaller network.
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, or use one of the legacy nets listed below.
This section includes networks from older training runs. The strongest among these are T78 and T60. Some download links might be outdated.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.
| Name | Source for Download | Notes |
|---|---|---|
| Latest T60 after 606512 | lczero.org run 1 networks | Finished main run |
| hanse-69722-vf2 | Contributed networks on Lc0 data | Trained from 609722 on T60 data, value focus emphasizes positions with eval discrepancies. See here |
| J94-100 (outdated) | Contributed networks on Lc0 data | Based on Sergio-V networks, trained on T60 data + value repair method. TCEC22 DivP+SuFi net |
| SV-3972+jio-20k (outdated) | Contributed networks on Lc0 data | Submitted for TCEC 18 Superfinal |
| 384x30-t60-3010 (outdated) | Contributed networks on Lc0 data | Won CCC13 and TCEC 17 |
| Name | Source for Download | Notes |
|---|---|---|
| T60 until 606511 | lczero.org run 1 networks | Finished main run |
| J13B.2-136 | GitHub: jhorthos Leela Training | "Terminator 2" Net |
| Name | Source for Download | Notes |
|---|---|---|
| Leelenstein 15.0 | 15.0 Post | No account required |
| SV-20b-t40-1541 | removed | Trained on T40 data |
| 42850 | storage.lczero.org direct download | Last T40 net |
| Name | Source for Download | Notes |
|---|---|---|
| Latest T79 | lczero.org run 2 networks | Finished 2nd test run, LC0 v0.29 required |
| Latest T75 | lczero.org run 3 networks | Finished 3rd test run |
| Latest T76 | lczero.org run 2 networks | Finished 2nd test run |
| Latest T77 | lczero.org run 2 networks | Finished 2nd test run |
| J64-210 | GitHub: jhorthos Leela Training | Trained on T60 data |
| J20-460 | GitHub: jhorthos Leela Training | Trained on T40 data |
| Name | Source for Download | Notes |
|---|---|---|
| Latest T74 | lczero.org run 2 networks | Finished 2nd test run |
| 128x10-t60-2-5300 | removed | Trained on T60 data |
| Tinker TK-6430 | Google Drive | Trained on T60 data |
| Latest J104 net | GitHub: jhorthos Leela Training | Based on T70 network 703810, trained on T70 data + value repair method |
| 703810 | training.lczero.org direct download | Last T70 net (not to be confused with T72) |
| 591226 | training.lczero.org direct download | Last T59 net |
| Little Demon 2 | data.lczero.org repository (LD2) | JH nets also here |
| Size | Name | Source for Download | Notes |
|---|---|---|---|
| 19b x 256f | T71.5-Armageddon-Chess | lczero.org run 3 network 715893 | Trained from scratch on Armageddon Chess |
| 19b x 256f | T71.4-FischerRandomChess | lczero.org run 3 network 714700 | Trained from scratch on Fischer Random Chess |
| 9b x 112f | ID11258-112x9-se | GitHub: dkappe Distilled Networks | Other sizes also here |
| 5b x 48f | Good Gyal 5 | GitHub: dkappe Bad Gyal | Other sizes also here |
| 2b x 16f | Tiny Gyal | GitHub: dkappe Bad Gyal | Other 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.