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Nemotron 3 Ultra vs Qwen3.5-397B-A17B

How Nemotron 3 Ultra (NVIDIA, June 2026) and Qwen3.5-397B-A17B (Alibaba Qwen, February 2026) are built, drawn on the same November 2026 blueprint. They make different choices on 10 of 27 components. Select a component in either diagram to find it in the table.

--- frontier-llm@2026-11+++ nemotron-3-ultra@2026-06+0~4−7

Nemotron 3 Ultra

NVIDIA, June 2026, 550B total, 55B active

Text · code · tool outputText · code · tool outputImages · video · audioImages · video · audioByte-level BPE tokenizer — 128K–262K vocabByte-level BPE tokenizer128K–262K vocabVision / audio encoder — native ViT or encoder-free patchesVision / audio encodernative ViT or encoder-free patchesToken embedding — media embeds spliced in placeToken embeddingmedia embeds spliced in placeEngram n-gram memory — hashed 2–4-gram lookup (V4.1)Engram n-gram memoryhashed 2–4-gram lookup (V4.1)Single residual stream, one sublayer per layer — Residual & normSingle residual stream, one sublayer p…Residual & normRMSNorm — pre-normRMSNormpre-normQK-Norm — per-head RMSNorm on q, kQK-Normper-head RMSNorm on q, kPartial RoPE / NoPE — rotate 64 dims, or nonePartial RoPE / NoPErotate 64 dims, or noneMamba-2 selective state-space layers — AttentionMamba-2 selective state-spac…AttentionGlobal GQA attention without positional encoding — AttentionGlobal GQA attention without…AttentionSparse / compressed attn — DSA indexer · CSA / HCASparse / compressed attnDSA indexer · CSA / HCASliding-window attention — local · 128–1,024-token windowSliding-window attentionlocal · 128–1,024-token windowOutput gate + sink — σ(x·W_g) ⊙ o · learnable sinkOutput gate + sinkσ(x·W_g) ⊙ o · learnable sink⊕ residual⊕residualRMSNorm — pre-normRMSNormpre-normMoE router — σ / √softplus · bias balancingMoE routerσ / √softplus · bias balancingLatentMoE routed experts (ReLU², 2,048-dim latent) — Mixture of expertsLatentMoE routed experts (ReLU², 2,048…Mixture of expertsShared expert — 1–2 · always activeShared expert1–2 · always active⊕ residual⊕residualFinal RMSNormFinal RMSNormLM head — untied · vocab logitsLM headuntied · vocab logitsMTP module(s) — predict t+2 … t+kMTP module(s)predict t+2 … t+kNext-token logitsNext-token logitsDraft tokensDraft tokensMuon optimizer — Newton–Schulz · QK-Clip · per-headMuon optimizerNewton–Schulz · QK-Clip · per-headFP8 → FP4 training — block-scaled FP8 · MXFP4/NVFP4 QATFP8 → FP4 trainingblock-scaled FP8 · MXFP4/NVFP4 QATLong-context extension — staged 4K → 1M · YaRNLong-context extensionstaged 4K → 1M · YaRNRL with verifiable rewards — GRPO family · async agentic RLRL with verifiable rewardsGRPO family · async agentic RLOn-policy distillation — reverse KL to specialist teachersOn-policy distillationreverse KL to specialist teachersSpeculative decoding — MTP self-drafting · DSparkSpeculative decodingMTP self-drafting · DSparkKV-cache compression — FP4 KV · cross-layer reuse · SSDKV-cache compressionFP4 KV · cross-layer reuse · SSD
AttentionMixture of expertsResidual & normInput & outputTraining & inferenceData flowResidual / skipAddedReplacedNot used

--- frontier-llm@2026-11+++ qwen3-5@2026-02+0~3−3

Qwen3.5-397B-A17B

Alibaba Qwen, February 2026, 397B total, 17B active

Text · code · tool outputText · code · tool outputImages · video · audioImages · video · audioByte-level BPE tokenizer — 128K–262K vocabByte-level BPE tokenizer128K–262K vocabVision / audio encoder — native ViT or encoder-free patchesVision / audio encodernative ViT or encoder-free patchesToken embedding — media embeds spliced in placeToken embeddingmedia embeds spliced in placeEngram n-gram memory — hashed 2–4-gram lookup (V4.1)Engram n-gram memoryhashed 2–4-gram lookup (V4.1)Single residual stream — Residual & normSingle residual streamResidual & normRMSNorm — pre-normRMSNormpre-normQK-Norm — per-head RMSNorm on q, kQK-Normper-head RMSNorm on q, kPartial RoPE / NoPE — rotate 64 dims, or nonePartial RoPE / NoPErotate 64 dims, or noneGated DeltaNet / KDA — linear · ~3 of every 4 layersGated DeltaNet / KDAlinear · ~3 of every 4 layersGated grouped-query attention — AttentionGated grouped-query attentionAttentionSparse / compressed attn — DSA indexer · CSA / HCASparse / compressed attnDSA indexer · CSA / HCASliding-window attention — local · 128–1,024-token windowSliding-window attentionlocal · 128–1,024-token windowOutput gate + sink — σ(x·W_g) ⊙ o · learnable sinkOutput gate + sinkσ(x·W_g) ⊙ o · learnable sink⊕ residual⊕residualRMSNorm — pre-normRMSNormpre-normSoftmax top-k router — Mixture of expertsSoftmax top-k routerMixture of expertsRouted experts — top-6–22 of 256–896 · SwiGLURouted expertstop-6–22 of 256–896 · SwiGLUShared expert — 1–2 · always activeShared expert1–2 · always active⊕ residual⊕residualFinal RMSNormFinal RMSNormLM head — untied · vocab logitsLM headuntied · vocab logitsMTP module(s) — predict t+2 … t+kMTP module(s)predict t+2 … t+kNext-token logitsNext-token logitsDraft tokensDraft tokensMuon optimizer — Newton–Schulz · QK-Clip · per-headMuon optimizerNewton–Schulz · QK-Clip · per-headFP8 → FP4 training — block-scaled FP8 · MXFP4/NVFP4 QATFP8 → FP4 trainingblock-scaled FP8 · MXFP4/NVFP4 QATLong-context extension — staged 4K → 1M · YaRNLong-context extensionstaged 4K → 1M · YaRNRL with verifiable rewards — GRPO family · async agentic RLRL with verifiable rewardsGRPO family · async agentic RLOn-policy distillation — reverse KL to specialist teachersOn-policy distillationreverse KL to specialist teachersSpeculative decoding — MTP self-drafting · DSparkSpeculative decodingMTP self-drafting · DSparkKV-cache compression — FP4 KV · cross-layer reuse · SSDKV-cache compressionFP4 KV · cross-layer reuse · SSD
AttentionMixture of expertsResidual & normInput & outputTraining & inferenceData flowResidual / skipAddedReplacedNot usedNot disclosed

Component by component

Nemotron 3 Ultra and Qwen3.5-397B-A17B differ on 10 of 27 components.

Blueprint componentNemotron 3 UltraQwen3.5-397B-A17B

Not used

Same as the blueprintA 27-layer ViT (width 1,152, 16 heads, MLP 4,304) with 16-px patches; video frames are patched in pairs (temporal patch 2). A 2×2 spatial merge and an MLP project into d = 4,096, and the DeepStack multi-layer injection is disabled (empty index list). Trained by early fusion on trillions of multimodal tokens.

Single residual stream, one sublayer per layer108 pre-norm layers on one d = 8,192 residual stream; each layer is one mixer or one MoE, not an attention+FFN pair.

Single residual streamOne d = 4,096 pre-norm residual stream with zero-centred RMSNorm gains; no hyper-connections or depth attention.

Not used

Same as the blueprintPer-head RMSNorm (zero-centred gain) over the 256-dim heads on q and k in the 15 gated-attention layers, applied before RoPE. The Gated DeltaNet layers instead L2-normalize q and k inside the kernel.

Mamba-2 selective state-space layers48 of the 60 mixer layers: SSD with 256 heads × 64 dims, state size 128, 8 B/C groups, causal conv of kernel 4.

Same as the blueprintGated DeltaNet in 45 of 60 layers, three before every attention layer. 16 query/key heads and 64 value heads of dim 128 (each q/k head serves 4 value heads); short causal conv (kernel 4) + SiLU on q, k, v; per-head scalar decay g = −eᴬ·softplus(a + dt_bias) and write strength β = σ(b). Each layer keeps a 64 × 128 × 128 state per sequence, held in FP32.

Global GQA attention without positional encoding12 layers of dense causal attention: 64 query heads share 2 KV heads (d_h = 128); no RoPE, QK-norm, gate or sink.

Gated grouped-query attentionEvery fourth layer: full causal GQA with 32 query heads sharing 2 KV heads of dim 256, QK-Norm and a sigmoid output gate.

Not used

Same as the blueprintIn the attention layers q_proj emits 2 × 32 × 256 values per token; the second half is an elementwise sigmoid gate, computed from the layer input, applied to the attention output before W_o. Gated DeltaNet layers gate their output with SiLU(z) after a per-head RMSNorm. No sink logits.

Same as the blueprintLinear router at the full 8,192 width: sigmoid scores over 512 experts, a per-expert bias steers top-22 selection, and the selected scores are renormalized and scaled by 5.0. Balancing follows the Nemotron 3 Super recipe: aux-loss-free bias updates (rate 1e-3) plus a standard load-balancing loss (coefficient 1e-4).

Softmax top-k routerA linear router scores 512 experts; softmax, keep the top 10, renormalize. No selection bias.

LatentMoE routed experts (ReLU², 2,048-dim latent)512 non-gated squared-ReLU experts of width 5,120 run in a 2,048-dim latent space; 22 active per token.

Same as the blueprint512 SwiGLU experts with intermediate size 1,024 at the full model width 4,096 (no latent projection); 10 are active per token, about 2% of the pool. Weights ship in BF16, with an official FP8 variant.

Not used

Not disclosed

Same as the blueprintMulti-teacher on-policy distillation (MOPD) in two iterations. More than ten domain-specialized teachers (STEM, coding, SWE, search, terminal use, office work, chat, safety and others) give dense token-level reverse-KL signals on the student's own rollouts. The RLVR student acts as a self-teacher for uncovered domains.

Not disclosed

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