DeepSeek-V3.2-Exp · Transformer blocks

in: $x_0$ [B batch, T positions, 7168 d] $R$ [T positions, 32 pairs] $M$ [T queries, T keys]
out: $x_{61}$ [B batch, T positions, 7168 d]
parameters: 670,024,578,816

Parameters

670,024,578,816 parameters per instance, across its learned components

Exact per-instance accounting
Component	Calculation	Parameters
Dense transformer block	$597{,}442{,}816 \times 3$	1,792,328,448
MoE transformer block	$11{,}521{,}245{,}696 \times 58$	668,232,250,368
Per-instance total		670,024,578,816

Derived from the pinned reference configuration and inference code. Checkpoint tensors have not been independently counted; FP8 block scales and the multi-token-prediction module are not counted.

Explanation

The reference picks the feed-forward network by layer index: MLP when layer_id < args.n_dense_layers, MoE otherwise (model.py 827).

Codemodel.py 826–908

model.py 880–887

        self.embed = ParallelEmbedding(args.vocab_size, args.dim)
        self.layers = torch.nn.ModuleList()
        for layer_id in range(args.n_layers):
            self.layers.append(Block(layer_id, args))
        self.norm = RMSNorm(args.dim)
        # lm_head in the checkpoint is stored in bf16, while the parameter here is stored in fp32 for easier computation of logits later.
        self.head = ColumnParallelLinear(args.dim, args.vocab_size, dtype=torch.float32)
        self.register_buffer("freqs_cis", precompute_freqs_cis(args), persistent=False)

model.py 901–908

        seqlen = tokens.size(1)
        freqs_cis = self.freqs_cis[start_pos:start_pos+seqlen]
        mask = torch.full((seqlen, seqlen), float("-inf"), device=tokens.device).triu_(1) if seqlen > 1 else None
        h, residual = self.embed(tokens), None
        for layer in self.layers:
            h, residual = layer(h, residual, start_pos, freqs_cis, mask)
        h, _ = self.norm(h, residual)
        logits = self.head(h[:, -1].float())

model.py 826–829

        self.attn = MLA(args)
        self.ffn = MLP(args.dim, args.inter_dim) if layer_id < args.n_dense_layers else MoE(args)
        self.attn_norm = RMSNorm(args.dim)
        self.ffn_norm = RMSNorm(args.dim)

model.py 844–851

        if residual is None:
            x, residual = self.attn_norm(x), x
        else:
            x, residual = self.attn_norm(x, residual)
        x = self.attn(x, start_pos, freqs_cis, mask)
        x, residual = self.ffn_norm(x, residual)
        x = self.ffn(x)
        return x, residual

Sources2

Direct evidence

The model embeds tokens, applies all blocks with a causal mask, normalizes and projects to vocabulary logits with an untied head. The reference generator projects only the last position; training projects every position.

transformer · deepseek-ai/DeepSeek-V3.2-Exp / inference/model.py
Pinned revision 87e509a2e5a1 ↗ · lines 880–908

Read code excerpt

        self.embed = ParallelEmbedding(args.vocab_size, args.dim)
        self.layers = torch.nn.ModuleList()
        for layer_id in range(args.n_layers):
            self.layers.append(Block(layer_id, args))
        self.norm = RMSNorm(args.dim)
        # lm_head in the checkpoint is stored in bf16, while the parameter here is stored in fp32 for easier computation of logits later.
        self.head = ColumnParallelLinear(args.dim, args.vocab_size, dtype=torch.float32)
        self.register_buffer("freqs_cis", precompute_freqs_cis(args), persistent=False)

… lines 888–900 omitted …

        seqlen = tokens.size(1)
        freqs_cis = self.freqs_cis[start_pos:start_pos+seqlen]
        mask = torch.full((seqlen, seqlen), float("-inf"), device=tokens.device).triu_(1) if seqlen > 1 else None
        h, residual = self.embed(tokens), None
        for layer in self.layers:
            h, residual = layer(h, residual, start_pos, freqs_cis, mask)
        h, _ = self.norm(h, residual)
        logits = self.head(h[:, -1].float())

Direct evidence

A block applies attention-norm, MLA, ffn-norm and the FFN; the first n_dense_layers blocks use the dense MLP, the rest MoE. Residual additions happen inside the next normalization.

block · deepseek-ai/DeepSeek-V3.2-Exp / inference/model.py
Pinned revision 87e509a2e5a1 ↗ · lines 826–851

Read code excerpt

        self.attn = MLA(args)
        self.ffn = MLP(args.dim, args.inter_dim) if layer_id < args.n_dense_layers else MoE(args)
        self.attn_norm = RMSNorm(args.dim)
        self.ffn_norm = RMSNorm(args.dim)

… lines 830–843 omitted …

        if residual is None:
            x, residual = self.attn_norm(x), x
        else:
            x, residual = self.attn_norm(x, residual)
        x = self.attn(x, start_pos, freqs_cis, mask)
        x, residual = self.ffn_norm(x, residual)
        x = self.ffn(x)
        return x, residual