Kimi · 2025-09 · Sparse MoE

Kimi K2 0905

Kimi K2 0905 is a multi-head latent attention (MLA) transformer released by Kimi in 2025-09, with 61 layers, hidden size 7168 and a context window of 262,144 tokens.

Layer stack

Attention ×61 · Layers 61

Key facts

FamilyKimi
Released2025-09
Params1,040.2 B
Active32.8 B
Context262,144 tokens
AttentionMLA · kv_lora_rank 512
Layers61
Hidden7,168
Heads64
Vocab163,840
PositionRoPE
NormRMSNorm
Activationsilu
Dtypefp8
Architecture classDeepseekV3ForCausalLM

Architecture overview

t₁ t₂ t₃ t₄ input tokens Embedding · Vocab 163,840 → Hidden 7,168 Q · 64 Wᴰᴷⱽ ↓ c_KV · 512 kᴿ · RoPE 64 Wᵁᴷ/Wᵁⱽ ↑ ⊕kᴿ Attention KV cache/token ≈ 512+64 vs dense 2·7,168 → ≈ 1/24.9 Router E1 E2 E3 E4 E5 E6 E7 E8 +376… Shared expert ×1 top-8 of 384 experts · ≈ 32.8B params active per token × 61 transformer block Attention Feed-forward / MoE RMSNorm pre-norm Final norm · RMSNorm LM head → Vocab 163,840 p p p → next token Position RoPE Dtype FP8 Context 256K tok
Drawn from the shipped config.json · 61 layers / width 7,168 / context 262,144. Original diagram by this atlas.

Attention

multi-head latent attention (MLA). kv_lora_rank=512. RoPE θ=50,000.

Feed-forward / MoE

It is a sparse mixture-of-experts with 384 routed experts and top-8 routing; about 32.8B parameters are active per token.

Field-level comparison

Compared against the previous model of the same lab; where none exists, against the structurally closest model. The ratio column is this model divided by the comparison model.

Full-field comparison vs predecessor Kimi K2 Base
ModelKimi K2 0905Kimi K2 BaseRatio
model_typekimi_k2kimi_k2
architecturesDeepseekV3ForCausalLMDeepseekV3ForCausalLM
hidden_size71687168≈1
num_hidden_layers6161≈1
num_attention_heads6464≈1
num_key_value_heads6464≈1
intermediate_size1843218432≈1
hidden_actsilusilu
n_routed_experts384384≈1
num_experts_per_tok88≈1
n_shared_experts11≈1
moe_intermediate_size20482048≈1
kv_lora_rank512512≈1
qk_rope_head_dim6464≈1
q_lora_rank15361536≈1
rope_theta5000050000≈1
max_position_embeddings262144131072×2.00
rms_norm_eps0.000010.000001×10.00
vocab_size163840163840≈1
torch_dtypebfloat16bfloat16
num_nextn_predict_layers00
_attn_implementation_autosetfalse
_name_or_path
add_cross_attentionfalse
attention_biasfalsefalse
attention_dropout00

Most similar architectures

Similarity values range from 0 (no shared categorical features) to 1 (identical profiles).

Raw config fields

76 fields
_attn_implementation_autosetfalse
_name_or_path
add_cross_attentionfalse
architecturesDeepseekV3ForCausalLM
attention_biasfalse
attention_dropout0
aux_loss_alpha0.001
bos_token_id163584
chunk_size_feed_forward0
diversity_penalty0
do_samplefalse
early_stoppingfalse
encoder_no_repeat_ngram_size0
eos_token_id163585
ep_size1
first_k_dense_replace1
hidden_actsilu
hidden_size7168
initializer_range0.02
intermediate_size18432
is_decoderfalse
is_encoder_decoderfalse
kv_lora_rank512
length_penalty1
max_length20
max_position_embeddings262144
min_length0
model_typekimi_k2
moe_intermediate_size2048
moe_layer_freq1
n_group1
n_routed_experts384
n_shared_experts1
no_repeat_ngram_size0
norm_topk_probtrue
num_attention_heads64
num_beam_groups1
num_beams1
num_experts_per_tok8
num_hidden_layers61
num_key_value_heads64
num_nextn_predict_layers0
num_return_sequences1
output_attentionsfalse
output_hidden_statesfalse
output_scoresfalse
pad_token_id163839
pretraining_tp1
q_lora_rank1536
qk_nope_head_dim128
qk_rope_head_dim64
remove_invalid_valuesfalse
repetition_penalty1
return_dicttrue
return_dict_in_generatefalse
rms_norm_eps0.00001
rope_theta50000
routed_scaling_factor2.827
scoring_funcsigmoid
seq_auxtrue
temperature1
tf_legacy_lossfalse
tie_encoder_decoderfalse
tie_word_embeddingsfalse
top_k50
top_p1
topk_group1
topk_methodnoaux_tc
torch_dtypebfloat16
torchscriptfalse
transformers_version4.51.3
typical_p1
use_bfloat16false
use_cachetrue
v_head_dim128
vocab_size163840