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models / compare / nemotron-3-ultra-vs-gpt-oss-120b

Nemotron 3 Ultra vs gpt-oss-120b

How Nemotron 3 Ultra (NVIDIA, June 2026) and gpt-oss-120b (OpenAI, August 2025) are built, drawn on the same November 2026 blueprint. They make different choices on 14 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+++ gpt-oss-120b@2025-08+0~4−9

gpt-oss-120b

OpenAI, August 2025, 117B total, 5.1B 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, kFull RoPE with YaRN scaling — AttentionFull RoPE with YaRN scalingAttentionGated DeltaNet / KDA — linear · ~3 of every 4 layersGated DeltaNet / KDAlinear · ~3 of every 4 layersDense grouped-query attention — AttentionDense 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 gpt-oss-120b differ on 14 of 27 components.

Blueprint componentNemotron 3 Ultragpt-oss-120b

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 = 2,880 residual stream with pre-norm sublayers; no hyper-connections.

Same as the blueprintNoPE: the 12 attention layers apply no positional encoding (Nemotron 3 Super omits positional embeddings, and Ultra keeps the same architecture). Token order reaches them only through the Mamba-2 layers' causal convolutions and recurrent state. config.json still lists rope_theta = 10,000, but the Hugging Face implementation never applies it.

Full RoPE with YaRN scalingRotary embeddings on all 64 head dims, θ = 150,000, stretched 32× with YaRN.

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.

Not used

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.

Dense grouped-query attentionFull causal attention with 64 query heads sharing 8 KV heads, in every other block.

Not used

Same as the blueprintEvery other block (18 of 36) attends only to a banded window of the last 128 tokens, alternating with the dense blocks; same GQA head layout.

Not used

Same as the blueprintNo output gate. Each head instead has a learned sink logit added to the softmax denominator, so it can assign attention mass to nothing.

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 picks the top 4 of 128 experts; softmax over those 4 logits gives the weights.

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 blueprint128 experts, top-4 active, each a SwiGLU with intermediate size 2,880. The gate is clamped at 7.0 and the linear branch to ±7.0, combined as (x_lin + 1) · x_glu · σ(1.702 · x_glu). Expert weights ship in MXFP4.

Same as the blueprintOne shared expert per MoE layer at full width: a non-gated squared-ReLU MLP, 8,192 → 10,240 → 8,192, added to the routed output without a router weight. The released checkpoint stores it in FP8 (per-tensor), not NVFP4.

Not used

Same as the blueprintOne MTP block (an attention layer followed by a LatentMoE layer) whose weights are shared across steps and applied recursively. It ran 2 steps in pretraining (total loss weight 0.1, 0.05 per step). MTP Boosting then trains only this head with a forward-KL loss to the frozen backbone's logits over 7 steps, to match the inputs it sees when drafting.

Not used

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

Same as the blueprintThe shared MTP head drafts recursively; the published serving recipes default to 5 draft tokens. On SPEED-Bench at draft length 7 with greedy decoding, the mean acceptance length is 4.39 tokens, and 4.58 after MTP Boosting.

Not used

Same as the blueprintOnly the 12 attention layers keep a KV cache: 6 KiB per token in FP8, the released checkpoint's KV format. The 48 Mamba-2 states are constant-size and stored in FP16 with stochastic rounding. In FP32 they would exceed the FP8 KV cache below about 64K tokens. 8-bit state formats were evaluated but not shipped.

Not used

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