ViT-22B: Scalable Vision Transformer

• ViT-22B is a large-scale vision transformer with 22 billion parameters that pioneers architectural and training innovations for computer vision.
• It employs parallel MLP and self-attention modules with QK normalization to stabilize training and efficiently harness distributed TPU systems.
• The model delivers robust feature representations for segmentation, generative, and hybrid tasks while enabling efficient deployment through advanced pruning and quantization techniques.

ViT-22B is a large-scale Vision Transformer comprising 22 billion parameters, specifically engineered to extend transformer scaling trends from natural language processing into computer vision. Representing a substantial leap beyond prior models (e.g., dense ViT-e at 4B parameters), ViT-22B integrates architectural, optimization, and system-level modifications to achieve efficient and stable training on unprecedented scale. Its unique design principles directly impact feature representation, robustness, transfer learning, efficiency, and the foundation for versatile downstream applications.

1. Architectural Advancements and Scalability

ViT-22B adopts a non-standard block structure for transformers: MLP and multi-head self-attention (MHSA) modules are applied in parallel following a LayerNorm operation,

$$y' = \mathrm{LayerNorm}(x) \ y = x + \mathrm{MLP}(y') + \mathrm{Attention}(y')$$

This parallelization increases computation efficiency and facilitates model/data parallelism in distributed environments, crucial for training very large models (Dehghani et al., 2023, Hong, 6 Aug 2025).

A further key modification is Query/Key (QK) normalization, where layer normalization is applied to query and key projections before dot-product attention:

$$\text{Attn} = \mathrm{softmax}\left(\frac{1}{\sqrt{d}} \cdot \mathrm{LN}(X W^Q) \cdot [\mathrm{LN}(X W^K)]^T \right)$$

QK-normalization stabilizes training by controlling attention entropy; in empirical analyses, it addresses severe gradient explosion observed during local ViT-22B training, especially early in optimization (logits $>4000$, diverging losses) (Hong, 6 Aug 2025). Additional LayerNorm on the parallel MLP branch was shown to regulate gradients further, enabling stable optimization for hundreds of epochs.

The model omits bias terms in QKV projections and LayerNorm centering (but retains biases in MLP dense layers for quality), aligning with practices in large-scale LLMs to boost accelerator utilization.

2. Training Infrastructure and Optimization

ViT-22B is trained on 4B images from a heavily extended JFT dataset, using extraordinary batch sizes (e.g., 65k) and reciprocal square-root learning rate schedules with warmup/cooldown. The system leverages explicit model/data parallelism via JAX/FLAX and jax.xmap, employing asynchronous parallel linear operations—maximizing utilization of TPU matrix units and overlapping computation with communication (Dehghani et al., 2023).

Gradient clipping (empirically set to 10), mixed precision, and reduced learning rates ($1\mathrm{e}{-5}$ to $5\mathrm{e}{-4}$) were all used to mitigate instability during local “from scratch” training experiments (Hong, 6 Aug 2025).

3. Feature Representation and Semantic Encoding

Unlike CNNs, ViT-22B maintains high spatial granularity through all layers; images are partitioned into non-overlapping patches, each represented by token, query, key, and value vectors. These deep features combine fine-grained localization with high-level semantic content. Shallow layers encode spatial position, deeper layers encode object-part semantics, and intermediate layers provide a blended representation—optimal for tasks such as semantic correspondence and part co-segmentation (Amir et al., 2021).

Experiments demonstrate that, with suitable self-supervised pretraining (e.g., DINO-ViT), the feature descriptors are effective for zero-shot dense tasks: co-segmentation, part co-segmentation, and semantic alignment across classes. Keys yield robust similarity maps and have lower sensitivity to clutter, supporting dense descriptor applications with competitive accuracy to supervised methods (MSRC7, CUB, PASCAL-CO datasets).

4. Efficient Compression and Quantization

Given ViT-22B’s computational and memory demands, research has focused on efficient model deployment:

• Multi-Dimensional Pruning (Hou et al., 2021) applies dependency-based pruning jointly to attention heads, FFN neurons, and patch sequence, using Hilbert-Schmidt norm of cross-covariance operators,

$$\widehat{\|C_{z,y}\|^2_{HS}} = (B-1)^{-2} \cdot \mathrm{trace}(K \cdot C \cdot L \cdot C)$$

Pruned subnets are selected via Gaussian process Bayesian search maximizing accuracy under a FLOPs constraint. Experiments demonstrate $40\%$ to $60\%$ FLOPs reduction with little to no accuracy loss when scaling to large ViT architectures.

• ADFQ-ViT Quantization (Jiang et al., 3 Jul 2024) introduces per-patch outlier-aware quantizers for post-LayerNorm activations,

$$s_i = \frac{\max(x_i) - \min(x_i)}{2^k - 1}, \quad z_i = \lfloor -\min(x_i)/s_i \rfloor$$

and shift-log2 quantizers for post-GELU activations. Module-wise optimization refines quantization parameters by minimizing reconstruction error and preserving attention score distributions. This yields substantial accuracy gains at extreme low-bit ($4$-bit) quantization, with a $10.23\%$ gain for ViT-B, and is plausible for scaling to ViT-22B.

• Quasar-ViT Hardware-Oriented NAS (Li et al., 25 Jul 2024) co-designs quantization-aware architecture search and FPGA hardware mapping. Training a row-wise mixed-precision supernet, the framework’s hardware modeling guides NAS to select subnets maximizing inference speed (e.g., up to 251.6 FPS on ZCU102 FPGA) at high top-1 accuracy. For ViT-22B, the same principles point to significant improvements in hardware efficiency via flexible bit-width allocation.

5. Application Versatility: Segmentation, Generation, and Hybrid Tasks

ViT-22B exhibits emergent properties for dense prediction. Encoder-only Mask Transformer (EoMT) (Kerssies et al., 24 Mar 2025) shows that—given sufficient scale and pretraining—the plain ViT can serve as an effective panoptic segmentation model. By appending learnable queries after $L_1$ blocks and jointly processing them with patch tokens in final layers, EoMT matches the accuracy of systems with bespoke adapters/decoders, while being significantly faster (e.g., up to $4\times$ throughput improvement). Mask annealing enables efficient training and unmasked inference.

For generative tasks, ViT-22B has been evaluated both within DDPM frameworks (hybrid discriminative-generative GenViT/HybViT (Yang et al., 2022)) and in transformer+CNN architectures (ViTUnet (Hong, 6 Aug 2025)). In HybViT, a single ViT backbone is conditioned by time embedding for both denoising and classification, with joint loss

$$L_{\mathrm{total}} = L_{\mathrm{CE}} + \alpha \cdot L_{\mathrm{simple}}$$

showing stable training and strong metrics (95.9% CIFAR-10 accuracy, IS=7.68, FID=26.4). ViTUnet merges transformer attention and CNN residuals for image synthesis; while ViT-22B outperforms original ViT in some FID benchmarks, qualitative performance varies by domain, often subject to inductive bias limitations.

6. Robustness, Fairness, and Human Alignment

ViT-22B sets new benchmarks for robustness on OOD and adversarial datasets (ImageNet-C/R, ObjectNet), maintaining accuracy and reducing degradation. Fairness assessments based on demographic parity (e.g., CelebA attributes) indicate that, with model scaling, disparity gaps decrease: debiased ViT-22B consistently outperforms smaller variants (Dehghani et al., 2023). Notably, the model exhibits elevated shape bias (up to $87\%$), yielding error patterns that closely match human vision (surpassing typical CNNs in texture reliance).

7. Visualization and Interpretability

Interpretability tools such as EL-VIT (Zhou et al., 23 Jan 2024) aid in analysis by visualizing model pipeline, patch similarity (cosine similarity maps between tokens), and attention distributions through interactive multi-view systems. Cosine similarity heatmaps clarify CLS-patch relationships important for prediction and enable expert-level scrutiny of semantic content at every layer.

Tabular Summary of Modifications and Deployment Strategies

Technique/Modification	Paper	Purpose / Impact
Parallel Layer MLP/Attention	(Dehghani et al., 2023, Hong, 6 Aug 2025)	Efficiency, easier parallelism, mitigates gradient explosion
QK Normalization	(Dehghani et al., 2023, Hong, 6 Aug 2025)	Controls attention entropy, stabilizes large-scale training
LayerNorm in Parallel MLP	(Hong, 6 Aug 2025)	Stabilizes gradients, enables successful local training
Multi-Dimensional Pruning	(Hou et al., 2021)	Reduces FLOPs, optimal computation-accuracy trade-off
Per-Patch Outlier Quantizer	(Jiang et al., 3 Jul 2024)	Preserves accuracy at low-bit; isolates activation outliers
Row-wise Mixed Precision	(Li et al., 25 Jul 2024)	Hardware-aware quantization for FPGA deployment

Conclusion

ViT-22B operationalizes transformer scaling for computer vision through architectural innovations, robust distributed training, and careful optimization of both features and compute pathways. Its richly localized and semantically encoded representations support state-of-the-art results in classification, segmentation, and hybrid generative-discriminative tasks, with improvements in fairness, robustness, and human-aligned perception bias. Efficient deployment is facilitated by advanced quantization, pruning, and architecture search, ensuring practical utility for large-scale, resource-constrained environments. The model’s foundation, further amplified by visualization tools and systematic analysis, positions ViT-22B at the forefront of transformer-based vision research.