Latent-MoE

Implementation of LatentMoE — Toward Optimal Accuracy per FLOP and Parameter in Mixture of Experts (Elango et al., NVIDIA 2026) — in Pytorch. A single-file, dependency-light layer you can drop in place of a standard MoE FFN.

The idea is simple. A standard MoE routes and computes its experts in the model hidden dimension d. LatentMoE first projects each token down into a smaller latent dimension l = d / alpha with a shared down-projection, runs all routed experts inside that latent space, then projects back up to d. Because dispatch traffic and expert weights now live in l rather than d, both all-to-all communication volume and per-expert weight-loading memory drop by a factor of alpha.

Those savings are reinvested by scaling the number of experts N' = alpha * N, exponentially expanding the space of expert combinations. Two flavors:

• l-MoE_eff — keep top-k K fixed → match baseline accuracy at lower inference cost.
• l-MoE_acc — scale top-k K' = alpha * K → match baseline cost while improving accuracy (recommended, Pareto-optimal).

The router and shared experts continue to operate in the original dimension d, since they are not the memory/communication bottleneck.

Install

uv pip install latent-moe

Usage

import torch
from latent_moe import LatentMoE, LatentMoEConfig

config = LatentMoEConfig(
    d = 2048,          # model hidden dim
    m = 1408,          # expert intermediate width
    n_experts = 64,    # base routed experts (N)
    top_k = 6,         # base active experts per token (K)
    alpha = 4,         # latent compression factor (l = d / alpha)
    n_shared = 2,      # always-on shared experts
    variant = "acc",   # "acc" (iso-cost, higher accuracy) or "eff" (cheaper)
)

layer = LatentMoE(config)

x = torch.randn(2, 128, config.d)  # (batch, seq, d)
y = layer(x)                       # (batch, seq, d)

assert y.shape == x.shape

Inspect the asymptotic cost quantities from Table 1 of the paper:

for k, v in layer.cost_summary().items():
    print(f"{k}: {v:,.2f}")

Transformer Usage

import torch
from latent_moe.transformer import TransformerConfig, MoETransformer

torch.manual_seed(0)

cfg = TransformerConfig(
    vocab_size=1000,
    d_model=256,
    n_layers=4,
    n_heads=8,
    n_kv_heads=2,  # GQA: 4 query heads share each KV head
    max_seq_len=128,
    d_ff=256,
    n_experts=8,
    top_k=2,
    alpha=2,
    n_shared=1,
)
model = MoETransformer(cfg)
print(f"params        : {model.num_params():,}")
print(f"primary device: {model.primary_device}")

idx = torch.randint(0, cfg.vocab_size, (2, 64))
targets = torch.randint(0, cfg.vocab_size, (2, 64))

logits, loss = model(idx, targets)
print(f"logits shape  : {tuple(logits.shape)}")
print(f"loss          : {loss.item():.4f}")
print(f"moe aux+z loss: {model.aux_loss.item():.4f}")

loss.backward()
print("backward OK")

out = model.generate(idx[:, :8], max_new_tokens=16, top_k=20)
print(f"generated     : {tuple(out.shape)}")

Citations

@article{elango2026latentmoe,
    title   = {LatentMoE: Toward Optimal Accuracy per FLOP and Parameter in Mixture of Experts},
    author  = {Elango and others},
    journal = {arXiv preprint arXiv:2601.18089},
    year    = {2026},
}

License

This project is licensed under the Apache License Version 2.0