Nemotron-3-Ultra-550B-A55B-Base

2026-06-04

Date the weights (or, if earlier, the technical report) were first made public, YYYY-MM-DD, as dated by the publisher. A Hugging Face repository creation date is used only when no dated publisher page exists, with a note.

other (openmdw-1.1)

License of the released weights as declared by the publisher (model card metadata or license file), e.g. apache-2.0, mit, or the name of a custom license.

text

Input modalities of the released checkpoint as the publisher states them, from: text, image, video, audio, joined with ' + ' in that order ("text", "text + image + video").

550B parameters

Total parameter count as reported by the publisher. See params.note for whether embeddings, MTP heads or vision towers are included.

55B parameters

Parameters used per token in the forward pass, as reported by the publisher.

Not disclosed

Active parameters excluding the input embedding and the output head (unembedding). Stated by the publisher, or computed from params.active by subtracting vocab × d_model for each of the embedding and output head that params.active is stated to include (basis inferred, note with the arithmetic). Not disclosed when the scope of params.active is not stated.

549.3B parameters

Calculated: embedding + output head + all experts + other weights, nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-Base-BF16@9f5b80e safetensors headers; reproduces stated total (backbone) and active (with output head)

Parameters of the text backbone counted from the released checkpoint's safetensors headers (embedding, output head, all experts and other weights; excluding MTP modules, vision/audio towers and n-gram lookup tables). Shown only when the counts reproduce the publisher's stated total and active parameters within 5% under some scope.

2.147B parameters

Calculated: input embedding + output head, nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-Base-BF16@9f5b80e safetensors headers

Input embedding plus output head (once if tied), counted from the checkpoint.

547.2B parameters

Calculated: dense + routed backbone weights; excludes input embedding, output head, lookup tables, MTP and other modalities, nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-Base-BF16@9f5b80e safetensors headers; reproduces stated total (backbone) and active (with output head)

Language backbone weights excluding input embeddings, output head, lookup/Engram tables, MTP modules and vision/audio towers. Equals params.counted_total minus params.counted_embedding. Same validation as params.counted_total.

10.94B parameters

Calculated: max(sum of dense and routed weights sharing a full layers.<index> tensor path), all experts included, nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-Base-BF16@9f5b80e safetensors headers

Largest group of language-backbone weights sharing a full layers.<index> tensor path, including every routed expert. Excludes lookup/Engram tables, embeddings, output head, MTP and other modalities. Hybrid checkpoints may store mixers and FFNs as separate layers. All-gather volume also depends on wrapping, sharding and dtype.

53.91B parameters

Calculated: non-expert weights + routed experts × 22/512, excluding embedding and output head, nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-Base-BF16@9f5b80e safetensors headers; reproduces stated total (backbone) and active (with output head)

Active parameters per token excluding the input embedding and output head, counted from the checkpoint as all non-expert weights plus routed-expert weights × top_k / experts. Same validation as params.counted_total.

Rounded publisher figures; MTP inclusion not stated. The checkpoint index lists 560.5B parameters including the MTP block.

What the publisher's counts include or exclude (MTP modules, embeddings, vision encoder, n-gram tables).

60

Inferred from the source; see the explanation below.

Number of transformer blocks in the main backbone (attention or token mixer plus FFN), excluding MTP modules.

8,192

Residual stream width.

131,072

Embedding table rows (vocab_size in config, may include padding).

No

Input embedding and output head share weights.

136.533

Calculated: d_model / layers

Width-to-depth ratio, d_model divided by the number of blocks.

GQA, Mamba-2

Token-mixing layer types in the backbone, e.g. GQA, MLA, SWA, Gated DeltaNet, Mamba-2, DSA (DeepSeek sparse attention).

48 Mamba-2 + 12 GQA

Inferred from the source; see the explanation below.

Token-mixer order, summarized as a pattern or layer positions. The fact's optional sequence lists each mixer in order; a ratio alone does not establish the order. Auxiliary prediction modules and standalone FFNs are excluded.

12

Layers whose KV cache grows with full context (dense or sparse global attention, MLA). Excludes sliding-window and linear/recurrent layers.

4

Calculated: attn.linear_layers / attn.global_layers

attn.linear_layers divided by attn.global_layers (3 for '3 Gated DeltaNet : 1 GQA').

48

Layers with a fixed-size recurrent state (linear attention, DeltaNet variants, Mamba).

64

Query heads in the global-attention layers.

2

Key/value heads in the global-attention layers. Not applicable to MLA, whose cache is a shared latent.

8,192

Calculated: heads × head_dim

attn.heads × attn.head_dim, the query projection width of global layers.

32

Calculated: heads / kv_heads

attn.heads divided by attn.kv_heads in global layers.

128

Query/key dimension per head in global-attention layers (for MLA, nope + rope dims).

NoPE

Positional scheme in global layers: RoPE, partial RoPE, NoPE, or mixed per layer type.

No

Queries and keys are normalized per head before the dot product.

No

Query/key/value projections have bias terms.

Mamba-2: 256 heads × head dim 64, state 128, 8 groups, conv kernel 4

Configuration of the linear / SSM layers (e.g. Gated DeltaNet heads and dims, Mamba-2 state size), as one string.

256

Value heads (or SSM heads) in the linear / SSM layers.

64

Head dimension of the linear / SSM layers (value head dim for DeltaNet variants, head_dim for Mamba-2).

4

Kernel size of the causal short convolution in linear / SSM layers or attention projections.

ReLU²

FFN activation (SwiGLU, GeGLU, ...).

No

FFN projections have bias terms.

512

Routed experts per MoE layer.

22

Routed experts selected per token.

1

Always-on experts per MoE layer.

5,120

Intermediate width of one routed expert.

10,240

Total intermediate width of the shared expert(s).

0

Number of initial blocks that use a dense FFN instead of MoE.

sigmoid

Gating function over expert logits (softmax, sigmoid).

aux-loss-free bias (update rate 1e-3) + load-balancing loss 1e-4

Inferred from the source; see the explanation below.

Load-balancing method, with coefficients where disclosed.

5

Multiplier applied to the combined routed-expert output (routed_scaling_factor).

Yes

Selected expert weights are renormalized to sum to 1.

2,048

Width of the projected space routed experts operate in (LatentMoE), when smaller than d_model.

4

Calculated: d_model / moe.latent_dim

shape.d_model divided by moe.latent_dim, how much LatentMoE compresses the hidden state before routed experts.

0.625

Calculated: moe.expert_dim / d_model

moe.expert_dim divided by shape.d_model.

23.2727

Calculated: experts / top_k

Routed experts divided by experts selected per token.

0.1

Calculated: params.active / params.total

params.active divided by params.total.

2

Additional future tokens predicted during pretraining (0 when the model was trained without MTP).

1

MTP modules with distinct weights. 1 with mtp.depth > 1 means one module is applied recursively.

next-token embedding and hidden state each normed (enorm, hnorm), concatenated and projected 2×8192→8192 (eh_proj); then one GQA attention layer and one LatentMoE layer; final norm

Contents of one MTP module (mixer and FFN type, norms, how the previous hidden state and next-token embedding are combined).

MoE

FFN in the MTP module: 'dense' or 'MoE', with its width when it differs from the backbone.

Yes

MTP modules use the main model's input embedding and output head.

0.1 (0.05 per MTP head)

Weight of the MTP loss, including its schedule.

Yes

The released checkpoint contains the MTP module weights.

RMSNorm

Normalization layer type in the blocks.

pre

Pre-norm, post-norm, or sandwich (pre and post).

1e-05

Epsilon of the block normalization layers.

8.192K tokens

Inferred from the source; see the explanation below.

Sequence length of the main pretraining stage.

1.049M tokens

Longest sequence length trained on after context extension.

1.049M tokens

Longest context the publisher says the model supports, possibly beyond trained length via RoPE scaling.

Long-context CPT at constant LR 2.5e-6 for 33B tokens: 92% of iterations at 1M, 8% at 4K; blend 46% long-context (document QA + SFT-style), 54% phase-2 data

How context was extended (stages, RoPE base change, YaRN factor).

20T tokens

Tokens seen in pretraining including annealing and context extension, as reported.

AdamW

Inferred from the source; see the explanation below.

Optimizer used for the bulk of parameters.

0.00025

Peak learning rate of the main pretraining stage.

2.5e-06

Learning rate at the end of the decay.

WSD: 200B-token warmup, stable to 15T, minus-sqrt decay over the final 5T tokens

Learning-rate schedule shape with phase boundaries.

200B tokens

Warmup length in steps or tokens.

3,072 × 8,192 (25.17M tokens)

Inferred from the source; see the explanation below.

Global batch size, including any batch-size ramp: sequences × sequence length (token total), or the token total alone when only that is stated.

25.17M tokens

Inferred from the source; see the explanation below.

Global batch size in tokens during the main pretraining stage, after any ramp (sequences × sequence length when stated in sequences, marked inferred).

0.1

Inferred from the source; see the explanation below.

Decoupled weight decay coefficient.

(0.9, 0.95)

Inferred from the source; see the explanation below.

(beta1, beta2) of the Adam-family optimizer or its AdamW fallback.

Not disclosed

Global gradient-norm clipping threshold.

NVFP4 (E2M1; 2D block quantization on weights; RHT on wgrad inputs; stochastic rounding on gradients). Higher precision for the final 15% of the network (16 layers), Mamba output projections, latent projections, QKV/attention projections, MTP layers and embeddings; FP32 gradient reduction.

Matmul precision of pretraining (BF16, FP8 recipe, NVFP4 recipe) with the scaling granularity where disclosed. Never inferred from the released checkpoint dtype.

Divergence at ~8T attributed to lowering local gradient accumulation for the output layer from FP32 to BF16 (MTP wgrad contribution lost): rollback + FP32 gradient reduction. Divergence at ~16T (cause undetermined): rollback to the 15T checkpoint + immediate LR decay.

Interventions used during pretraining to keep it stable (QK-clip, z-loss, logit caps, spike handling), with coefficients.

Two-phase curriculum over 19 categories, switching from diversity- to quality-biased mix after ~15T tokens (~75%); quality-filtered + synthetic web crawl ~49% of phase 1 and ~38% of phase 2; also finepdfs, math, code (refreshed to Sep 2025), Nemotron-CC-Code, Wikipedia, academic, legal, multilingual (11 languages), Crawl++, SFT-style data

Pretraining data composition as disclosed (domain shares, synthetic fraction, repetition).

September 2025

Pretraining data cutoff date.

GB200

Accelerators used for pretraining, count and duration where stated.

GB200

Accelerator count and type for the main pretraining run, as the count with thousands separators and the chip's short name ("2,048 H800", "2,048 GH200"). A stated range or bound stays as stated ("2,048–4,096 GH200").

Checkpoint merge with a 500B-token window, selected from merges over 125B-1T windows and several orderings, used as the start of the long-context phase

EMA or checkpoint merging applied to produce the released base weights.

• phase-1

  Tokens

  15000000000000

  Seq len

  8192

  Batch sequences

  3072

  Source: paper · PDF pages 1, 9; §1, §2.5

  with 15 trillion tokens of data in the first phase focusing on diversity and broad domain coverage

  Diversity-biased mix; warmup and stable LR. seq_len and batch from the Super recipe, which Ultra §2.4 keeps.

• phase-2

  Tokens

  5000000000000

  Seq len

  8192

  Batch sequences

  3072

  Source: paper · PDF pages 1, 9; §1, §2.5

  followed by 5 trillion tokens of data in the second phase focusing on high quality data

  Quality-biased mix; spans the minus-sqrt LR decay.

• long-context

  Tokens

  33000000000

  Seq len

  1048576

  Lr

  2.5e-06

  Batch tokens

  25165824

  Source: paper · PDF pages 1, 9; §1, §2.5

  Eventually, the LC-Phase was trained for 33B tokens.

  92% of iterations at 1M, 8% at 4K (math and code SFT-style data only); starts from a 500B-token checkpoint merge.

Ordered pretraining stages. Each item: name, tokens, seq_len, lr, batch_tokens, batch_sequences, steps, note, quote, and source when it differs from the fact's source.

Nemotron 3 Super recipe and hyperparameters, adjusted to a 20T WSD horizon and 2.5e-4 peak LR

How peak LR, batch size or other hyperparameters were chosen for the released run (fitted scaling law with its formula, μP transfer from a proxy width, sweep), as stated.

36.3636

Calculated: train.tokens / params.total

train.tokens divided by params.total.

370.979

Calculated: train.tokens / params.counted_active_non_embedding

train.tokens divided by params.counted_active_non_embedding, or by the stated params.active_non_embedding when there is no validated checkpoint count.

0.0503316

Calculated: train.batch_tokens / (train.lr_peak × train.weight_decay × train.tokens)

B / (η λ D) with B = train.batch_tokens, η = train.lr_peak, λ = train.weight_decay and D = train.tokens: the AdamW EMA timescale as a fraction of training (Bergsma et al. 2025, Power Lines, arXiv 2505.13738). Computed only when the optimizer is AdamW; uses the peak LR.

12,288 bytes

Calculated: kv.bytes_at_context / 131072 tokens; uniform BF16, payload only; quantization overhead excluded. Global KV: 12 × (512 × 16/8) B/entry

Retained KV cache divided by context length, including bounded SWA caches and indexer keys. Exports use uniform BF16 at 131,072 tokens. The website can select uniform payload precision (no quantization overhead) or sourced component formats (including scales and documented padding). This is cache payload, not peak prefill memory or persistent prefix storage.

12,288 bytes

Calculated: 12288 B / 1 tokens (Global KV); uniform BF16, payload only; quantization overhead excluded. Global KV: 12 × (512 × 16/8) B/entry

Long-context global KV bytes per token, including indexer keys; excludes bounded SWA caches. Compression rates and cross-layer cache reuse are included. Exports use uniform BF16; the website's storage selector also applies here. This is the field to compare with global-KV figures in reports, using the matching storage recipe.

223 bytes

Calculated: kv.bytes_per_token (131072 tokens) / (params.active / 1e9); uniform BF16, payload only; quantization overhead excluded. Global KV: 12 × (512 × 16/8) B/entry

kv.bytes_per_token divided by active parameters in billions (131,072 tokens in exports).

29,283,868 bytes

Calculated: kv.bytes_at_context (131072 tokens) / (params.active / 1e9); uniform BF16, payload only; quantization overhead excluded. Global KV: 12 × (512 × 16/8) B/entry

kv.bytes_at_context divided by active parameters in billions (131,072 tokens in exports).

1,610,612,736 bytes

Calculated: 12288 B × 131072 (Global KV); uniform BF16, payload only; quantization overhead excluded. Global KV: 12 × (512 × 16/8) B/entry

Retained KV cache for one sequence at the selected context, including SWA windows and indexer keys. Exports use uniform BF16 at 131,072 tokens. Excludes recurrent state, compressor tail state, draft/MTP caches, temporary prefill tensors, allocator metadata and persistent prefix snapshots. Does not estimate peak prefill or total serving memory.

23

Artificial Analysis Intelligence Index v4.3.2 of the post-trained model, from a dated Wayback snapshot of its Artificial Analysis page. The note names the variant (reasoning setting).