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Grok 2.5

xAI · Grok · open weights

Facts and where they come from

Released2025-08config.jsonconfig.jsonHugging Face repository creation date (api.createdAt)
Licencenot disclosednot disclosed
Total parametersnot disclosednot disclosed
Active parametersnot disclosednot disclosed
Context length128K tokensconfig.jsonconfig.jsonmax_position_embeddings
Norm placementnot disclosednot disclosednot checked in the modelling code
Norm typenot disclosednot disclosednot checked in the modelling code
QK-normnocodemodelling codeno q/k normalisation in the attention block
Positional encodingRoPEcodemodelling coderotary on the full head (default)

Architecture, drawn from the data

GQA 64q/8kv. Each column is one layer: its token mixer above, its feed-forward block below. Paler columns reuse another layer’s keys and values.

Grok 2.5: layer stack and blockslayers (64)mixer / FFNlayer 0: GQA: 64 query / 8 KV heads · head 128layer 0: MoE: 8 experts, 2 active · expert 16384 · + dense 32768layer 1: GQA: 64 query / 8 KV heads · head 128layer 1: MoE: 8 experts, 2 active · expert 16384 · + dense 32768layer 2: GQA: 64 query / 8 KV heads · head 128layer 2: MoE: 8 experts, 2 active · expert 16384 · + dense 32768layer 3: GQA: 64 query / 8 KV heads · head 128layer 3: MoE: 8 experts, 2 active · expert 16384 · + dense 32768layer 4: GQA: 64 query / 8 KV heads · head 128layer 4: MoE: 8 experts, 2 active · expert 16384 · + dense 32768layer 5: GQA: 64 query / 8 KV heads · head 128layer 5: MoE: 8 experts, 2 active · expert 16384 · + dense 32768layer 6: GQA: 64 query / 8 KV heads · head 128layer 6: MoE: 8 experts, 2 active · expert 16384 · + dense 32768layer 7: GQA: 64 query / 8 KV heads · head 128layer 7: MoE: 8 experts, 2 active · expert 16384 · + dense 32768layer 8: GQA: 64 query / 8 KV heads · head 128layer 8: MoE: 8 experts, 2 active · expert 16384 · + dense 32768layer 9: GQA: 64 query / 8 KV heads · head 128layer 9: MoE: 8 experts, 2 active · expert 16384 · + dense 32768layer 10: GQA: 64 query / 8 KV heads · head 128layer 10: MoE: 8 experts, 2 active · expert 16384 · + dense 32768layer 11: GQA: 64 query / 8 KV heads · head 128layer 11: MoE: 8 experts, 2 active · expert 16384 · + dense 32768layer 12: GQA: 64 query / 8 KV heads · head 128layer 12: MoE: 8 experts, 2 active · expert 16384 · + dense 32768layer 13: GQA: 64 query / 8 KV heads · head 128layer 13: MoE: 8 experts, 2 active · expert 16384 · + dense 32768layer 14: GQA: 64 query / 8 KV heads · head 128layer 14: MoE: 8 experts, 2 active · expert 16384 · + dense 32768layer 15: GQA: 64 query / 8 KV heads · head 128layer 15: MoE: 8 experts, 2 active · expert 16384 · + dense 32768layer 16: GQA: 64 query / 8 KV heads · head 128layer 16: MoE: 8 experts, 2 active · expert 16384 · + dense 32768layer 17: GQA: 64 query / 8 KV heads · head 128layer 17: MoE: 8 experts, 2 active · expert 16384 · + dense 32768layer 18: GQA: 64 query / 8 KV heads · head 128layer 18: MoE: 8 experts, 2 active · expert 16384 · + dense 32768layer 19: GQA: 64 query / 8 KV heads · head 128layer 19: MoE: 8 experts, 2 active · expert 16384 · + dense 32768layer 20: GQA: 64 query / 8 KV heads · head 128layer 20: MoE: 8 experts, 2 active · expert 16384 · + dense 32768layer 21: GQA: 64 query / 8 KV heads · head 128layer 21: MoE: 8 experts, 2 active · expert 16384 · + dense 32768layer 22: GQA: 64 query / 8 KV heads · head 128layer 22: MoE: 8 experts, 2 active · expert 16384 · + dense 32768layer 23: GQA: 64 query / 8 KV heads · head 128layer 23: MoE: 8 experts, 2 active · expert 16384 · + dense 32768layer 24: GQA: 64 query / 8 KV heads · head 128layer 24: MoE: 8 experts, 2 active · expert 16384 · + dense 32768layer 25: GQA: 64 query / 8 KV heads · head 128layer 25: MoE: 8 experts, 2 active · expert 16384 · + dense 32768layer 26: GQA: 64 query / 8 KV heads · head 128layer 26: MoE: 8 experts, 2 active · expert 16384 · + dense 32768layer 27: GQA: 64 query / 8 KV heads · head 128layer 27: MoE: 8 experts, 2 active · expert 16384 · + dense 32768layer 28: GQA: 64 query / 8 KV heads · head 128layer 28: MoE: 8 experts, 2 active · expert 16384 · + dense 32768layer 29: GQA: 64 query / 8 KV heads · head 128layer 29: MoE: 8 experts, 2 active · expert 16384 · + dense 32768layer 30: GQA: 64 query / 8 KV heads · head 128layer 30: MoE: 8 experts, 2 active · expert 16384 · + dense 32768layer 31: GQA: 64 query / 8 KV heads · head 128layer 31: MoE: 8 experts, 2 active · expert 16384 · + dense 32768layer 32: GQA: 64 query / 8 KV heads · head 128layer 32: MoE: 8 experts, 2 active · expert 16384 · + dense 32768layer 33: GQA: 64 query / 8 KV heads · head 128layer 33: MoE: 8 experts, 2 active · expert 16384 · + dense 32768layer 34: GQA: 64 query / 8 KV heads · head 128layer 34: MoE: 8 experts, 2 active · expert 16384 · + dense 32768layer 35: GQA: 64 query / 8 KV heads · head 128layer 35: MoE: 8 experts, 2 active · expert 16384 · + dense 32768layer 36: GQA: 64 query / 8 KV heads · head 128layer 36: MoE: 8 experts, 2 active · expert 16384 · + dense 32768layer 37: GQA: 64 query / 8 KV heads · head 128layer 37: MoE: 8 experts, 2 active · expert 16384 · + dense 32768layer 38: GQA: 64 query / 8 KV heads · head 128layer 38: MoE: 8 experts, 2 active · expert 16384 · + dense 32768layer 39: GQA: 64 query / 8 KV heads · head 128layer 39: MoE: 8 experts, 2 active · expert 16384 · + dense 32768layer 40: GQA: 64 query / 8 KV heads · head 128layer 40: MoE: 8 experts, 2 active · expert 16384 · + dense 32768layer 41: GQA: 64 query / 8 KV heads · head 128layer 41: MoE: 8 experts, 2 active · expert 16384 · + dense 32768layer 42: GQA: 64 query / 8 KV heads · head 128layer 42: MoE: 8 experts, 2 active · expert 16384 · + dense 32768layer 43: GQA: 64 query / 8 KV heads · head 128layer 43: MoE: 8 experts, 2 active · expert 16384 · + dense 32768layer 44: GQA: 64 query / 8 KV heads · head 128layer 44: MoE: 8 experts, 2 active · expert 16384 · + dense 32768layer 45: GQA: 64 query / 8 KV heads · head 128layer 45: MoE: 8 experts, 2 active · expert 16384 · + dense 32768layer 46: GQA: 64 query / 8 KV heads · head 128layer 46: MoE: 8 experts, 2 active · expert 16384 · + dense 32768layer 47: GQA: 64 query / 8 KV heads · head 128layer 47: MoE: 8 experts, 2 active · expert 16384 · + dense 32768layer 48: GQA: 64 query / 8 KV heads · head 128layer 48: MoE: 8 experts, 2 active · expert 16384 · + dense 32768layer 49: GQA: 64 query / 8 KV heads · head 128layer 49: MoE: 8 experts, 2 active · expert 16384 · + dense 32768layer 50: GQA: 64 query / 8 KV heads · head 128layer 50: MoE: 8 experts, 2 active · expert 16384 · + dense 32768layer 51: GQA: 64 query / 8 KV heads · head 128layer 51: MoE: 8 experts, 2 active · expert 16384 · + dense 32768layer 52: GQA: 64 query / 8 KV heads · head 128layer 52: MoE: 8 experts, 2 active · expert 16384 · + dense 32768layer 53: GQA: 64 query / 8 KV heads · head 128layer 53: MoE: 8 experts, 2 active · expert 16384 · + dense 32768layer 54: GQA: 64 query / 8 KV heads · head 128layer 54: MoE: 8 experts, 2 active · expert 16384 · + dense 32768layer 55: GQA: 64 query / 8 KV heads · head 128layer 55: MoE: 8 experts, 2 active · expert 16384 · + dense 32768layer 56: GQA: 64 query / 8 KV heads · head 128layer 56: MoE: 8 experts, 2 active · expert 16384 · + dense 32768layer 57: GQA: 64 query / 8 KV heads · head 128layer 57: MoE: 8 experts, 2 active · expert 16384 · + dense 32768layer 58: GQA: 64 query / 8 KV heads · head 128layer 58: MoE: 8 experts, 2 active · expert 16384 · + dense 32768layer 59: GQA: 64 query / 8 KV heads · head 128layer 59: MoE: 8 experts, 2 active · expert 16384 · + dense 32768layer 60: GQA: 64 query / 8 KV heads · head 128layer 60: MoE: 8 experts, 2 active · expert 16384 · + dense 32768layer 61: GQA: 64 query / 8 KV heads · head 128layer 61: MoE: 8 experts, 2 active · expert 16384 · + dense 32768layer 62: GQA: 64 query / 8 KV heads · head 128layer 62: MoE: 8 experts, 2 active · expert 16384 · + dense 32768layer 63: GQA: 64 query / 8 KV heads · head 128layer 63: MoE: 8 experts, 2 active · expert 16384 · + dense 3276803263× 64GQA: 64 query / 8 KV heads · head 128+MoE: 8 experts, 2 active · expert 16384 · + dense 32768+full attentionMoE FFN

Modelled costs

From the cost model, batch size 1. Totals the lab states are in the table above; differences come from rounding, from what a lab counts, or from parts the model does not describe (listed on the about page).

Parameters (modelled)270B
Active per token (modelled)115B
Without embeddings and output head267B total, 113B active
KV cache per token, BF16 (layers that grow with context)256 KiB
KV cache + state at 128K tokens, BF1632 GiB
Decode FLOPs per token at 4K context236 GFLOP
Prefill FLOPs for a 4K prompt941 TFLOP

KV cache against context

Grok 2.5: KV cache bytes against context length101001,00010,000100,000980 KiB9.5 MiB95 MiB950 MiB9.3 GiBcontext (tokens)KV cache + state (BF16)Grok 2.5

Compare with other models →

Every architecture field

FieldValueSource
d_model8,192config.jsonconfig.jsonhidden_size
vocab131,072config.jsonconfig.jsonvocab_size
tied_embeddingsfalsecodemodelling codetransformers 5.18.0: PretrainedConfig.tie_word_embeddings defaultnot in config
mixers.full.typeattncodemodelling codeattention block
mixers.full.heads64config.jsonconfig.jsonnum_attention_heads
mixers.full.kv_heads8config.jsonconfig.jsonnum_key_value_heads
mixers.full.head_dim128config.jsonconfig.jsonhead_dim
ffns.moe.typemoecodemodelling codeMoE block
ffns.moe.experts8config.jsonconfig.jsonnum_local_experts
ffns.moe.active2config.jsonconfig.jsonnum_experts_per_tok
ffns.moe.d_expert16,384config.jsonconfig.jsonmoe_intermediate_size
ffns.moe.gatedtruecodemodelling codeexperts are gated MLPs
ffns.moe.dense_parallel_d_ff32,768config.jsonconfig.jsonintermediate_size
layout64× full/moeconfig.jsonconfig.jsonnum_hidden_layers

Sources

Listed in the LLM Architecture Gallery checklist as “Grok 2.5 (270B)” (name only; see about).