Models / Laya

Laya

edge
HuggingFace → HuggingBay → ConvAI Innovations → Find for my hardware

ModernBERT-large encoder (421M total) with a from-scratch decision head: option markers scored at their own MASK token, softmaxed per question. Answer schema is defined at request time, so new question types need no retraining. Three checkpoints routed automatically: English (512 ctx), multilingual mmBERT 322M (1024 ctx, 100+ languages), typed-decisions (1024 ctx). RLCD training. 33ms single-question answers on a T4.

AI-generated content marks

The provider reports that this model does not add embedded watermarks or provenance metadata to generated output.

general chat
Parameters
420M
Context
1k
License
apache 2.0
Developer
ConvAI Innovations
Released
Sep 2026

Guides covering Laya

Ollaya: the open decision-model runner, with the numbers Sep 26, 2026 Laya answers yes/no questions in 33 milliseconds, for free Sep 19, 2026

Save your hardware and every model page answers the real question: will it run on your machine, and how fast?

Join free - save your rig →

Run it locally

Per-quant memory needs and a static "can you run it?" reference - no rig entry required

Q4_K_M
0.3GB 0.5GB min 0.8GB rec
Balanced - the usual local sweet spot
Q8_0
0.5GB 0.8GB min 1.2GB rec
Near-lossless
BF16
0.8GB 1.2GB min 2.0GB rec
Full quality, largest

The reference hardware

Schematic of the 4x H100 80GB (320GB) reference rig - 320GB VRAM, 13400 GB/s aggregate bandwidth
Schematic of the NVIDIA DGX Station 748GB reference rig - 748GB unified memory, 8000 GB/s aggregate bandwidth
Schematic of the 8x RTX 3090 rack (192GB) reference rig - 192GB VRAM, 7489 GB/s aggregate bandwidth
Schematic of the 4x RTX 5090 (128GB) reference rig - 128GB VRAM, 7168 GB/s aggregate bandwidth
Schematic of the AMD Instinct MI300X (192GB) reference rig - 192GB VRAM, 5324 GB/s aggregate bandwidth
Schematic of the 4x RTX 4090 (96GB) reference rig - 96GB VRAM, 4032 GB/s aggregate bandwidth
Schematic of the 2x RTX 5090 (64GB) reference rig - 64GB VRAM, 3584 GB/s aggregate bandwidth
Schematic of the 2x RTX 3090 (48GB) reference rig - 48GB VRAM, 1872 GB/s aggregate bandwidth
Schematic of the Single RTX 5090 (32GB) reference rig - 32GB VRAM, 1792 GB/s aggregate bandwidth
Schematic of the RTX PRO 6000 Blackwell (96GB) reference rig - 96GB VRAM, 1792 GB/s aggregate bandwidth
Schematic of the Mac Studio M4 Ultra 192GB reference rig - 192GB unified memory, 1092 GB/s aggregate bandwidth
Schematic of the Mac Studio M4 Ultra 512GB reference rig - 512GB unified memory, 1092 GB/s aggregate bandwidth
Schematic of the Single RTX 4090 (24GB) reference rig - 24GB VRAM, 1008 GB/s aggregate bandwidth
Schematic of the MacBook Pro M5 Max 128GB reference rig - 128GB unified memory, 614 GB/s aggregate bandwidth
Schematic of the Single GTX 1080 Ti (11GB) reference rig - 11GB VRAM, 484 GB/s aggregate bandwidth
Schematic of the Dual EPYC 9004 + 768GB DDR5-4800 reference rig - 768GB unified memory, 460 GB/s aggregate bandwidth
Schematic of the DGX Spark 128GB unified reference rig - 128GB unified memory, 273 GB/s aggregate bandwidth
Schematic of the Ryzen AI Max+ 395 128GB reference rig - 128GB unified memory, 256 GB/s aggregate bandwidth
Schematic of the Jetson AGX Orin 64GB reference rig - 64GB unified memory, 204 GB/s aggregate bandwidth
Schematic of the Epyc + 512GB DDR4-3200 + 2x RTX 3090 reference rig - 560GB unified memory, 204 GB/s aggregate bandwidth
Schematic of the Epyc + 512GB DDR4-2400 + 2x RTX 3090 reference rig - 560GB unified memory, 153 GB/s aggregate bandwidth
Schematic of the NVIDIA Jetson Orin NX 16GB reference rig - 16GB unified memory, 102 GB/s aggregate bandwidth

22 reference configs, drawn in-house. Scroll for more.

Can you run it? - reference rigs

Rig Q4_K_M Q8_0 BF16
4x H100 80GB (320GB) fast 29480.0t/s fast 16377.8t/s fast 8773.8t/s
NVIDIA DGX Station 748GB fast 17600.0t/s fast 9777.8t/s fast 5238.1t/s
8x RTX 3090 rack (192GB) fast 16477.1t/s fast 9154.0t/s fast 4903.9t/s
4x RTX 5090 (128GB) fast 15769.6t/s fast 8760.9t/s fast 4693.3t/s
AMD Instinct MI300X (192GB) fast 11714.6t/s fast 6508.1t/s fast 3486.5t/s
4x RTX 4090 (96GB) fast 8870.4t/s fast 4928.0t/s fast 2640.0t/s
2x RTX 5090 (64GB) fast 7884.8t/s fast 4380.4t/s fast 2346.7t/s
2x RTX 3090 (48GB) fast 4119.3t/s fast 2288.5t/s fast 1226.0t/s
Single RTX 5090 (32GB) fast 3942.4t/s fast 2190.2t/s fast 1173.3t/s
RTX PRO 6000 Blackwell (96GB) fast 3942.4t/s fast 2190.2t/s fast 1173.3t/s
Mac Studio M4 Ultra 192GB fast 2620.8t/s fast 1456.0t/s fast 780.0t/s
Mac Studio M4 Ultra 512GB fast 2620.8t/s fast 1456.0t/s fast 780.0t/s
Single RTX 4090 (24GB) fast 2217.6t/s fast 1232.0t/s fast 660.0t/s
MacBook Pro M5 Max 128GB fast 1473.6t/s fast 818.7t/s fast 438.6t/s
Single GTX 1080 Ti (11GB) fast 1064.8t/s fast 591.6t/s fast 316.9t/s
Dual EPYC 9004 + 768GB DDR5-4800 fast 1013.8t/s fast 563.2t/s fast 301.7t/s
DGX Spark 128GB unified fast 600.6t/s fast 333.7t/s fast 178.8t/s
Ryzen AI Max+ 395 128GB fast 563.2t/s fast 312.9t/s fast 167.6t/s
Jetson AGX Orin 64GB fast 450.6t/s fast 250.3t/s fast 134.1t/s
Epyc + 512GB DDR4-3200 + 2x RTX 3090 fast 450.6t/s fast 250.3t/s fast 134.1t/s
Epyc + 512GB DDR4-2400 + 2x RTX 3090 fast 337.9t/s fast 187.7t/s fast 100.6t/s
NVIDIA Jetson Orin NX 16GB fast 225.3t/s fast 125.2t/s fast 67.1t/s

Fit tiers use the same will-it-run logic as the rig finder. For comfortable fits, the badge reflects decode speed: fast >=20 t/s, ok 8-20 t/s, slow <8 t/s. t/s is a bandwidth estimate, not a measured benchmark.

Download options

Q4_K_M -4% vs fp16
0.3GB dl 0.5GB min 0.8GB rec
REC RAM vs largest quant
GGUF on HF →
Q8_0 -1% vs fp16
0.5GB dl 0.8GB min 1.2GB rec
REC RAM vs largest quant
GGUF on HF →
BF16
0.8GB dl 1.2GB min 2.0GB rec
REC RAM vs largest quant
GGUF on HF →

Or run it in the cloud

No per-token API provider pricing tracked for Laya yet. For flagship list prices, see the calculator.

PRICE HISTORY

Inference cost over time

Data accumulates from the first daily sync - longer ranges populate over time. Prices come from OpenRouter snapshots, not a historical API.

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