---
title: 'PaLM 540B: Scalable Language Model'
url: https://www.emergentmind.com/topics/palm-540b-parameter-model
type: topic
---

# PaLM 540B: Scalable Language Model

The PaLM 540B Parameter Model refers to the Pathways Language Model (PaLM) architecture at 540 billion parameters, representing one of the largest and most capable dense, decoder-only Transformer language models to date. Developed within the Pathways system by Google, the model achieves state-of-the-art results across language understanding, generation, reasoning, and code tasks. PaLM forms the foundation for further advances in scaling, adaptation, and multimodal integration.

## 1. Architectural Specification

PaLM 540B is structured as a dense, decoder-only Transformer employing extensive depth and width to achieve scalability and expressivity. The model consists of 118 Transformer layers with a hidden (model) dimension of 18,432. Each layer uses 48 attention heads of size 256, yielding a total attention width that matches the hidden dimension. The intermediate feed-forward dimension per layer is 73,728 ($4\times d_{model}$). Key architectural modifications include:

- **SwiGLU activations** in MLP blocks instead of standard GELU or ReLU, providing improved parameter and compute efficiency.
- **Parallel Transformer block computation**, fusing LayerNorm, MLP, and Attention branches: $y = x + \text{MLP}(\text{LN}(x)) + \text{Attention}(\text{LN}(x))$.
- **Multi-Query Attention** (MQA): sharing key and value projections across attention heads to accelerate autoregressive decoding.
- **Rotary Position Embeddings** (RoPE): enhancing long-range token interpolation.
- Shared input and output embeddings (SentencePiece, 256K tokens) without bias terms.
- Total parameter count is 540.35 billion; this includes all trainable weights such as attention, feed-forward, embeddings, projections, and auxiliary layers [2204.02311].

## 2. Training Data, Tokenization, and Infrastructure

PaLM 540B was trained on 780 billion tokens in a single epoch. The data distribution is: 50% multilingual social-media, 27% filtered web pages, 13% English books, 5% GitHub code (24 languages), 4% Wikipedia, and 1% English news. The model is trained on 124 languages, with English comprising approximately 78% of total tokens. Tokenization uses a lossless, whitespace-preserving SentencePiece model at 256,000 vocabulary items, including ASCII digits split individually and out-of-vocab Unicode fallback.

Training utilized Google’s Pathways ML system, spanning two TPU v4 Pods with 3072 chips each (6144 chips total). Data parallelism (256-way) and 12-way model parallelism were employed with a fully-sharded, 2D GSPMD setup. The model achieved 97% weak scaling; observed hardware utilization (MFU) was 46.2% of peak FLOPs [2204.02311].

## 3. Optimization and Scaling

No dropout was used during pretraining; finetuning adopted a dropout rate of 0.1. Optimization employed Adafactor (Adam equivalent with parameter scaling), global norm gradient clipping (1.0), and a learning rate schedule combining an initial warmup with inverse square root decay:

- Initial learning rate: $10^{-2}$ for first 10,000 steps, then $10^{-2} / \sqrt{k}$ for step $k$.
- Momentum: $β_1 = 0.9$, $β_2(k) = 1 - k^{-0.8}$.
- Weight decay: $= lr^2$ per step.
- Auxiliary $z\_loss = 10^{-4}\cdot(\log Z)^2$ encourages softmax normalizer stability.

Batch sizes were ramped from 1.05M to 4.2M tokens/step; max context length is 2048 tokens (examples concatenated with end-of-document markers and no padding).

## 4. Empirical Performance and Scaling Law Analysis

PaLM 540B demonstrated significant advancement in few-shot and zero-shot performance over previous dense large LMs. Benchmark results include:

- **English NLP**: On 29 1-shot tasks, PaLM 540B exceeds prior SOTA, achieving 81.4% (TriviaQA), 88.7% (BoolQ), and 92.6% (SuperGLUE average finetune).
- **MMLU (5-shot, 57 tasks)**: Average accuracy 69.3%, surpassing Chinchilla 70B at 67.5%.
- **BIG-bench**: 5-shot average across 150 tasks is ~49, outperforming the human average of 47. Discontinuous (“emergent”) improvements of >+10% are observed on 25% of tasks when scaling from 62B to 540B.
- **Reasoning/CoT**: 58% accuracy on GSM8K (+CoT), SOTA across arithmetic and commonsense benchmarks.
- **Code generation**: HumanEval@100 pass rate of 76.2%, MBPP@80 of 75.0%, and competitive open-domain translation BLEU scores with few-shot prompting.
- **Multilingual QA**: Finetuned TyDiQA-GoldP EM score 80.0%, 1-shot/few-shot performance lags finetuned SOTA but remains competitive.
- **Average NLU (21 tasks) and NLG (8 tasks) 1-shot**: 74.7% and 63.9%, respectively [2204.02311].

Scaling analysis shows approximately log-linear gains with model size and data. PaLM 540B also exhibits moderate memorization rates (2.4% exact match on 50-token train spans) and increased, but still sub-human, toxicity as model size increases.

## 5. Adaptation with UL2R (U-PaLM 540B)

The U-PaLM 540B variant adapts PaLM 540B using UL2R, a “mixture-of-denoisers” objective combining prefix-LM and span corruption, conferring true non-causal infilling capabilities and further improving scaling curves. Only 0.16% extra compute (~1.3B tokens, 20,000 steps on 512 TPUv4 chips) is used on the original data mix.

Empirical results indicate:

- **Compute Savings:** U-PaLM achieves the final PaLM 540B performance with roughly half the compute (2.53 × 10³ zFLOPs PaLM vs 1.08 × 10³ zFLOPs U-PaLM; 66.5 vs 69.4 average zero/few-shot task score).
- **Benchmarks**: U-PaLM 540B surpasses baseline on MMLU (70.7% vs 69.3%), BIG-Bench emergent tasks (+3.4 absolute), reasoning (e.g., GSM8K, 58.5% vs 54.9%), and exhibits qualitative improvements in infilling and flexible prompting [2210.11399].

## 6. Multimodal and Embodied Extensions

PaLM 540B serves as the LLM backbone in PaLM-E, an embodied multimodal variant (PaLM-E-562B) integrating a Vision Transformer (ViT-22B, 22B parameters) and state-vector encoders. PaLM-E processes tokens from interleaved text, images, and continuous state vectors, embedded via learned projections into a unified token space. The system is trained end-to-end—either by freezing PaLM and updating encoders, or via full finetuning—on a data mixture from web, vision-language, and robotics domains.

Key results for PaLM-E-562B include:

- **Robotic Manipulation**: TAMP VQA and planning (q₂=98.2%, plan feasibility=93.7%, pick-and-place=82.5%), tabletop block-pushing (Task 1: ~90% success), and affordance/failure prediction (F1 ≈ 0.91).
- **Vision-Language**: Zero-shot VQA v2 (80.0%), OK-VQA (66.1%—SOTA for single model), COCO CIDEr=138.7.
- **Language Tasks**: NLU retention 100.4%, NLG retention 96.2% relative to PaLM 540B (no catastrophic forgetting) [2303.03378].

PaLM-E exemplifies direct extension of large unimodal LMs into scalable, generalist multimodal and embodied reasoning agents by unified token interleaving and prefix encoding.

## 7. Limitations, Bias, and Ethical Considerations

PaLM 540B exhibits measurable bias and toxicity, e.g., higher anti-Muslim bias and increased toxicity in certain demographic prompts, with moderate mitigation via prompt filtering, output monitoring, and bloom-filter blocking during inference. Memorization, especially of repetitive or formulaic content, increases sublinearly with model scale. The model, as a highly general pretrained system, is not tailored for sensitive/safety-critical tasks without further domain-specific mitigation and caution.

Open challenges include optimal data/model scaling trade-offs, impact of data quality and freshness, effective sparsification (Mixture-of-Experts), and incorporating retrieval or modularity within the Pathways framework. PaLM 540B, as a reference model, catalyzes ongoing research into both fundamental limits of scaling and practical methodologies for efficient adaptation, alignment, and multimodal grounding [2204.02311, 2210.11399, 2303.03378].

Source: https://www.emergentmind.com/topics/palm-540b-parameter-model