DeepSeek · 2026-04 · Sparse MoE

DeepSeek-V4-Flash Base

DeepSeek-V4-Flash Base is a multi-query attention (MQA) transformer released by DeepSeek in 2026-04, with 43 layers, hidden size 4096 and a context window of 1,048,576 tokens.

Layer stack

Attention ×43 · Layers 43

Key facts

FamilyDeepSeek
Released2026-04
Params284 B
Active13 B
Context1,048,576 tokens
AttentionMQA (64:1)
Layers43
Hidden4,096
Heads64
Vocab129,280
PositionRoPE
NormRMSNorm
Activationsilu
Dtypefp8+fp4
Architecture classDeepseekV4ForCausalLM

Architecture overview

t₁ t₂ t₃ t₄ input tokens Embedding · Vocab 129,280 → Hidden 4,096 Q1 Q2 Q3 Qn KV1 Heads 64 KV heads 1 head dim 512 · RoPE Router E1 E2 E3 E4 E5 E6 E7 E8 +248… Shared expert ×1 top-6 of 256 experts · ≈ 13B params active per token × 43 transformer block Attention Feed-forward / MoE RMSNorm pre-norm Final norm · RMSNorm LM head → Vocab 129,280 p p p → next token MTP ×1 → +1 future tokens Position RoPE Dtype FP8+FP4 Context 1024K tok
Drawn from the shipped config.json · 43 layers / width 4,096 / context 1,048,576. Original diagram by this atlas.

Attention

multi-query attention (MQA) — 64 q-heads / 1 kv-heads. RoPE θ=10,000.

Feed-forward / MoE

It is a sparse mixture-of-experts with 256 routed experts and top-6 routing; about 13B parameters are active per token.

The configuration ships 1 extra multi-token-prediction (MTP) layers used to accelerate decoding.

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 DeepSeek-V4-Flash
ModelDeepSeek-V4-Flash BaseDeepSeek-V4-FlashRatio
model_typedeepseek_v4deepseek_v4
architecturesDeepseekV4ForCausalLMDeepseekV4ForCausalLM
hidden_size40964096≈1
num_hidden_layers4343≈1
num_attention_heads6464≈1
num_key_value_heads11≈1
head_dim512512≈1
hidden_actsilusilu
n_routed_experts256256≈1
num_experts_per_tok66≈1
n_shared_experts11≈1
moe_intermediate_size20482048≈1
qk_rope_head_dim6464≈1
q_lora_rank10241024≈1
rope_theta1000010000≈1
max_position_embeddings10485761048576≈1
sliding_window128128≈1
rms_norm_eps0.0000010.000001≈1
vocab_size129280129280≈1
torch_dtypebfloat16bfloat16
num_nextn_predict_layers11≈1
attention_biasfalsefalse
attention_dropout00
bos_token_id00
eos_token_id11≈1
expert_dtypefp8fp4

Most similar architectures

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

Raw config fields

46 fields
architecturesDeepseekV4ForCausalLM
attention_biasfalse
attention_dropout0
bos_token_id0
eos_token_id1
expert_dtypefp8
hc_eps0.000001
hc_mult4
hc_sinkhorn_iters20
head_dim512
hidden_actsilu
hidden_size4096
index_head_dim128
index_n_heads64
index_topk512
initializer_range0.02
max_position_embeddings1048576
model_typedeepseek_v4
moe_intermediate_size2048
n_routed_experts256
n_shared_experts1
norm_topk_probtrue
num_attention_heads64
num_experts_per_tok6
num_hidden_layers43
num_hash_layers3
num_key_value_heads1
num_nextn_predict_layers1
o_groups8
o_lora_rank1024
q_lora_rank1024
qk_rope_head_dim64
rms_norm_eps0.000001
rope_theta10000
routed_scaling_factor1.5
scoring_funcsqrtsoftplus
sliding_window128
swiglu_limit10
tie_word_embeddingsfalse
topk_methodnoaux_tc
torch_dtypebfloat16
transformers_version4.57.1
use_cachetrue
vocab_size129280
compress_rope_theta160000
compress_ratios0×3 + 4×21 + 128×20