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Nemotron 3 Ultra vs GLM-5

How Nemotron 3 Ultra (NVIDIA, June 2026) and GLM-5 (Z.ai (Zhipu AI), February 2026) are built, drawn on the same November 2026 blueprint. They make different choices on 8 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+++ glm-5@2026-02+0~3−5

GLM-5

Z.ai (Zhipu AI), February 2026, 744B total, 40B 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-normRMSNorm on MLA latents (no per-head QK-Norm) — Residual & normRMSNorm on MLA latents (no per-head QK…Residual & normPartial 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 layersLatent attention (MLA) — global · GQA in many modelsLatent attention (MLA)global · GQA in many modelsSparse / 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 balancingRouted 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-headINT4 QAT in SFT, FP8 rollouts — Training & inferenceINT4 QAT in SFT, FP8 rolloutsTraining & inferenceLong-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

Component by component

Nemotron 3 Ultra and GLM-5 differ on 8 of 27 components.

Blueprint componentNemotron 3 UltraGLM-5

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 = 6,144 residual stream with pre-norm sublayers; no hyper-connections in GLM-5 through GLM-5.3.

Not used

RMSNorm on MLA latents (no per-head QK-Norm)Queries and keys are normalized as compressed latents (2,048 and 512 dims) before up-projection, not per head.

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.

Same as the blueprintMLA in all 78 layers and the MTP layer, with 64 heads: 2,048-dim query latent, 512-dim KV latent plus a 64-dim rotary key (576 cached values per token per layer), q/k head dim 256 (192 + 64 rotary), v head dim 256. This is the MLA-256 variant: head dim raised from 192 to 256 with one-third fewer heads, keeping training FLOPs and parameters roughly constant while lowering decode compute.

Not used

Same as the blueprintDSA in every layer, not alternated with MLA: a lightning indexer (32 heads × 128 dims, ReLU scores with learned per-head weights) selects the top 2,048 tokens per query and MLA attends only to those. It was added after mid-training: 1,000 warm-up steps on 14 × 202,752-token sequences, then 20B tokens of sparse adaptation. The indexer is frozen in RL and uses a deterministic torch.topk. GLM-5.2 runs it in 21 of 78 layers.

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 blueprint256 routed SwiGLU experts of intermediate size 2,048, top-8 active, in 75 MoE layers. The first 3 layers use a dense SwiGLU FFN of width 12,288 instead. The MTP layer is also an MoE layer.

Not used

Same as the blueprintMuon, following GLM-4.5, with Muon Split: the MLA up-projections W_UQ, W_UK and W_UV are split per head and each block is orthogonalized separately, which brought MLA level with GQA-8 in Z.ai's ablation. The distributed implementation all-gathers only each rank's own shards. LR peaks at 2e-4, decays to 4e-5 in pretraining, then 1e-5 in mid-training.

Same as the blueprintPretrained on 20T tokens in NVFP4 (E2M1 with 16-element blocks, 2D weight scaling, Random Hadamard Transforms on wgrad inputs, stochastic rounding of gradients). The final 16 layers, Mamba output projections, latent projections, QKV/attention projections, MTP layers and embeddings stayed in higher precision. The train-loss gap to BF16 branches stayed below 0.4% on average.

INT4 QAT in SFT, FP8 rolloutsINT4 quantization-aware training during SFT with a bitwise-identical kernel; FP8 inference for RL rollouts and release.

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