61 transformer blocks › 60 MoE blocks › Mixture of experts › 384 routed experts
Gate features
×tensorself.gate_proj(x)
Features that drive the SiLU branch.
- shape
- [B batch, T positions, 2048 expert d_ff]
Sources
Contextual evidence from Gate projection
Each routed expert is its own SwiGLU MLP of moe_intermediate_size hidden features.
expert · moonshotai/Kimi-K2-Instruct / modeling_deepseek.py
Pinned revision fd1984e2b7a3 ↗ · lines 486–515
Read code excerpt
if hasattr(config, "ep_size") and config.ep_size > 1:
assert config.ep_size == dist.get_world_size()
self.ep_size = config.ep_size
self.experts_per_rank = config.n_routed_experts // config.ep_size
self.ep_rank = dist.get_rank()
self.experts = nn.ModuleList(
[
(
DeepseekV3MLP(
config, intermediate_size=config.moe_intermediate_size
)
if i >= self.ep_rank * self.experts_per_rank
and i < (self.ep_rank + 1) * self.experts_per_rank
else None
)
for i in range(config.n_routed_experts)
]
)
else:
self.ep_size = 1
self.experts_per_rank = config.n_routed_experts
self.ep_rank = 0
self.experts = nn.ModuleList(
[
DeepseekV3MLP(
config, intermediate_size=config.moe_intermediate_size
)
for i in range(config.n_routed_experts)
]
)Contextual evidence from Gate projection
Weights are stored in FP8 (e4m3) with one scale per 128 × 128 block; the scales are quantization metadata and are excluded from parameter counts.
linear · moonshotai/Kimi-K2-Instruct / config.json
Pinned revision fd1984e2b7a3 ↗ · lines 38–46
Read code excerpt
"quantization_config": {
"activation_scheme": "dynamic",
"fmt": "e4m3",
"quant_method": "fp8",
"weight_block_size": [
128,
128
]
},