DeepSeek V4.1 · Router · OpenModelAtlas

40 layers › 2 groups of 6 layers › Source layer, pairs pooled › Mixture of experts

Router

learnedself.gate(x, image_mask) · 892

Score all 384 experts for each position and keep the best 6.

Why

Bias per modality. Image and text tokens may prefer different experts, and balancing their total load can hide an imbalance inside one of them. The router keeps one selection bias for text and one for image positions; the bias decides which experts run, and the unbiased scores weigh their outputs. DeepSeek V4.1 report §2.1.1

s=\sqrt{\operatorname{softplus}(z)}

\mathcal T=\operatorname{top}_{6}(s+b)

G_{i}=1.5\,\frac{s_{i}}{\sum_{j\in\mathcal T}s_{j}}\;(i\in\mathcal T)

All 3 steps

z=x_{\mathrm{ffn}}W_{{\htmlClass{vl-stream-context}{R}}}

s=\sqrt{\operatorname{softplus}(z)}

\mathcal T=\operatorname{top}_{6}(s+b)

G_{i}=1.5\,\frac{s_{i}}{\sum_{j\in\mathcal T}s_{j}}\;(i\in\mathcal T)

in: $x_{\mathrm{ffn}}$ [B batch, T positions, 5120 d]
out: $G$ [B batch, T positions, 384 experts]
parameters: 1,966,848 · × 2 groups = 3,933,696

Parameters

1,966,848 parameters per instance, across its learned components

Exact per-instance accounting
Component	Calculation	Parameters
Score projection	$5{,}120 \times 384$	1,966,080
Top 6, normalized	$384 + 384$	768
Per-instance total		1,966,848

Across 2 independent groups: $1{,}966{,}848 \times 2 = 3{,}933{,}696$ parameters.

Derived from the pinned reference implementation and configuration; equal to the element counts of the checkpoint’s own tensors on the text path. The quantisation scales, the vision encoder and the DSpark blocks are not counted.

Explanation

The bias decides which experts are chosen and is left out of their weights (model.py 818–826).

Codemodel.py 792–827 · config.json 1–66

model.py 792–827

class Gate(nn.Module):
    """MoE gating. The correction bias steers expert selection only; the routing weights come from the
    unbiased scores. Image-span tokens use a separate bias (training `noaux_tc_for_vl`)."""

    def __init__(self, layer_id: int, args: ModelArgs):
        super().__init__()
        n_routed_experts, n_activated_experts = args.get_moe_config(layer_id)
        self.dim = args.dim
        self.topk = n_activated_experts
        self.score_func = args.score_func
        self.gate_temp = args.gate_temp
        self.norm_topk_prob = args.norm_topk_prob
        self.route_scale = args.route_scale
        self.weight = nn.Parameter(torch.empty(n_routed_experts, args.dim))
        self.bias = nn.Parameter(torch.empty(n_routed_experts, dtype=torch.float32))
        self.bias_vl = nn.Parameter(torch.empty(n_routed_experts, dtype=torch.float32)) if args.vision_enabled else None

    def forward(self, x: torch.Tensor, image_mask: torch.Tensor | None = None) -> tuple[torch.Tensor, torch.Tensor]:
        """x: [n, dim]; image_mask: [n] bool, True for tokens inside an image span."""
        scores = linear(x.float(), self.weight.float()) / self.gate_temp
        if self.score_func == "softmax":
            scores = scores.softmax(dim=-1)
        elif self.score_func == "sigmoid":
            scores = scores.sigmoid()
        else:
            scores = F.softplus(scores).sqrt()
        bias = self.bias
        if image_mask is not None and self.bias_vl is not None:
            bias = torch.where(image_mask.unsqueeze(-1), self.bias_vl, bias)
        # the bias picks experts but does not scale them: weights come from the raw scores
        indices = (scores + bias).topk(self.topk, dim=-1)[1]
        weights = scores.gather(1, indices)
        if self.norm_topk_prob and self.topk > 1:
            weights /= weights.sum(dim=-1, keepdim=True) + 1e-20  # not norm_eps, matches training
        weights *= self.route_scale
        return weights, indices

config.json 1–66

{
    "vocab_size": 129280,
    "dim": 5120,
    "moe_inter_dim": 2304,
    "n_layers": 40,
    "n_mtp_layers": 3,
    "dspark_block_size": 5,
    "dspark_noise_token_id": 128799,
    "dspark_target_layer_ids": [37, 38, 39],
    "dspark_markov_rank": 256,
    "dspark_n_routed_experts": 128,
    "dspark_n_activated_experts": 3,
    "n_heads": 64,
    "n_routed_experts": 384,
    "n_shared_experts": 1,
    "n_activated_experts": 6,
    "score_func": "sqrtsoftplus",
    "route_scale": 1.5,
    "swiglu_limit": 10.0,
    "q_lora_rank": 1280,
    "head_dim": 512,
    "rope_head_dim": 64,
    "norm_eps": 1e-20,
    "o_groups": 8,
    "o_lora_rank": 1024,
    "window_size": 128,
    "kv_source_layers": [2, 8, 14, 20],
    "index_source_layers": [2, 8, 14, 20, 24, 28, 32, 36],
    "original_seq_len": 65536,
    "rope_theta": 10000,
    "rope_factor": 16,
    "beta_fast": 32,
    "beta_slow": 1,
    "index_n_heads": 32,
    "index_head_dim": 128,
    "index_topk": 512,
    "candidate_source_layer": 20,
    "candidate_topk_blocks": 2048,
    "candidate_block_size": 8,
    "hc_mult": 4,
    "hc_sinkhorn_iters": 20,
    "hc_eps": 1e-06,
    "engram_layer_ids": [1, 14],
    "engram_vocab_size": 16000000,
    "engram_num_embeddings": [384006168, 384016682],
    "engram_max_ngram_size": 4,
    "engram_pad_id": 2,
    "engram_compressed_vocab_size": 99092,
    "dtype": "fp8",
    "expert_dtype": "fp4",
    "compress_rope_theta": 160000,
    "compress_ratios": [0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],
    "vision_n_layers": 32,
    "vision_dim": 1024,
    "vision_n_heads": 16,
    "vision_inter_dim": 2816,
    "vision_patch_size": 14,
    "vision_downsample_ratio": 3,
    "vision_max_n_token": 1024,
    "vision_min_pixels": 295936,
    "vision_max_wh_ratio": null,
    "image_token_id": 129264,
    "engram_n_heads": 8,
    "engram_head_dim": 256,
    "vision_rope_theta": 10000
}

Sources2

Direct evidence

The router: the square root of softplus of a projection, a selection bias (a second one at image positions), the best six, their unbiased scores normalised and scaled.

gate · deepseek-ai/DeepSeek-V4.1-Flash / inference/model.py
Pinned revision 2cba9e42aa02 ↗ · lines 792–827

Read code excerpt

class Gate(nn.Module):
    """MoE gating. The correction bias steers expert selection only; the routing weights come from the
    unbiased scores. Image-span tokens use a separate bias (training `noaux_tc_for_vl`)."""

    def __init__(self, layer_id: int, args: ModelArgs):
        super().__init__()
        n_routed_experts, n_activated_experts = args.get_moe_config(layer_id)
        self.dim = args.dim
        self.topk = n_activated_experts
        self.score_func = args.score_func
        self.gate_temp = args.gate_temp
        self.norm_topk_prob = args.norm_topk_prob
        self.route_scale = args.route_scale
        self.weight = nn.Parameter(torch.empty(n_routed_experts, args.dim))
        self.bias = nn.Parameter(torch.empty(n_routed_experts, dtype=torch.float32))
        self.bias_vl = nn.Parameter(torch.empty(n_routed_experts, dtype=torch.float32)) if args.vision_enabled else None

    def forward(self, x: torch.Tensor, image_mask: torch.Tensor | None = None) -> tuple[torch.Tensor, torch.Tensor]:
        """x: [n, dim]; image_mask: [n] bool, True for tokens inside an image span."""
        scores = linear(x.float(), self.weight.float()) / self.gate_temp
        if self.score_func == "softmax":
            scores = scores.softmax(dim=-1)
        elif self.score_func == "sigmoid":
            scores = scores.sigmoid()
        else:
            scores = F.softplus(scores).sqrt()
        bias = self.bias
        if image_mask is not None and self.bias_vl is not None:
            bias = torch.where(image_mask.unsqueeze(-1), self.bias_vl, bias)
        # the bias picks experts but does not scale them: weights come from the raw scores
        indices = (scores + bias).topk(self.topk, dim=-1)[1]
        weights = scores.gather(1, indices)
        if self.norm_topk_prob and self.topk > 1:
            weights /= weights.sum(dim=-1, keepdim=True) + 1e-20  # not norm_eps, matches training
        weights *= self.route_scale
        return weights, indices

Direct evidence

The reference configuration: 40 layers of width 5,120, 64 heads of 512 features with a 64-feature rotary part, a 1,280-feature query latent, a grouped output of 8 groups at rank 1,024, a window of 128, the per-layer compression ratios and the two source lists, the indexer and its candidate stage, hyper-connections of 4 streams, 384 routed experts of which 6 are used with 1 shared, Engram before layers 1 and 14, and three DSpark blocks.

config · deepseek-ai/DeepSeek-V4.1-Flash / inference/config.json
Pinned revision 2cba9e42aa02 ↗ · lines 1–66

Read code excerpt

{
    "vocab_size": 129280,
    "dim": 5120,
    "moe_inter_dim": 2304,
    "n_layers": 40,
    "n_mtp_layers": 3,
    "dspark_block_size": 5,
    "dspark_noise_token_id": 128799,
    "dspark_target_layer_ids": [37, 38, 39],
    "dspark_markov_rank": 256,
    "dspark_n_routed_experts": 128,
    "dspark_n_activated_experts": 3,
    "n_heads": 64,
    "n_routed_experts": 384,
    "n_shared_experts": 1,
    "n_activated_experts": 6,
    "score_func": "sqrtsoftplus",
    "route_scale": 1.5,
    "swiglu_limit": 10.0,
    "q_lora_rank": 1280,
    "head_dim": 512,
    "rope_head_dim": 64,
    "norm_eps": 1e-20,
    "o_groups": 8,
    "o_lora_rank": 1024,
    "window_size": 128,
    "kv_source_layers": [2, 8, 14, 20],
    "index_source_layers": [2, 8, 14, 20, 24, 28, 32, 36],
    "original_seq_len": 65536,
    "rope_theta": 10000,
    "rope_factor": 16,
    "beta_fast": 32,
    "beta_slow": 1,
    "index_n_heads": 32,
    "index_head_dim": 128,
    "index_topk": 512,
    "candidate_source_layer": 20,
    "candidate_topk_blocks": 2048,
    "candidate_block_size": 8,
    "hc_mult": 4,
    "hc_sinkhorn_iters": 20,
    "hc_eps": 1e-06,
    "engram_layer_ids": [1, 14],
    "engram_vocab_size": 16000000,
    "engram_num_embeddings": [384006168, 384016682],
    "engram_max_ngram_size": 4,
    "engram_pad_id": 2,
    "engram_compressed_vocab_size": 99092,
    "dtype": "fp8",
    "expert_dtype": "fp4",
    "compress_rope_theta": 160000,
    "compress_ratios": [0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],
    "vision_n_layers": 32,
    "vision_dim": 1024,
    "vision_n_heads": 16,
    "vision_inter_dim": 2816,
    "vision_patch_size": 14,
    "vision_downsample_ratio": 3,
    "vision_max_n_token": 1024,
    "vision_min_pixels": 295936,
    "vision_max_wh_ratio": null,
    "image_token_id": 129264,
    "engram_n_heads": 8,
    "engram_head_dim": 256,
    "vision_rope_theta": 10000
}