If I have a vocabulary of 100K words tokens, how can I optimize transformer architecture

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Table of Contents

• If I have a vocabulary of 100K words tokens how can I optimize transformer architecture

• Compute Efficiency Optimizations

• Memory Usage Optimizations

• Inference Speed Improvements

• Training Efficiency Improvements

Compute Efficiency Optimizations

• Softmax & Feedforward Improvements: The output softmax over 100K vocabulary can be a compute bottleneck. Recent work addresses this via approximate methods and skipping unnecessary calculations. Cut Cross-Entropy (CCE) computes the loss without building the full [batch×vocab] logit matrix, instead computing only the correct-token logit and doing a streamed log-sum-exp (Cut Your Losses in Large-Vocabulary Language Models). This not only saves memory (as discussed later) but also avoids redundant multiply-add operations. Moreover, CCE leverages the sparsity of the gradient: it skips backpropagating through vocabulary entries with negligible contribution, i.e. below numerical precision . This selective gradient computation yields the same training outcome while avoiding countless tiny updates, improving compute efficiency in the backward pass. Other methods for large-vocab softmax include hierarchical or class-based softmax (grouping tokens into clusters) to reduce the computation needed per prediction – for instance, predicting a token via two smaller classifiers (first the cluster, then the token). Recent 2024 research has revisited such ideas with modern heuristics (like using BPE merges for grouping) and found they can maintain accuracy while cutting compute by only evaluating a fraction of the 100K possibilities (LLM Vocabulary Compression for Low-Compute Environments). Additionally, sparsity in feed-forward layers (Mixture-of-Experts or gating out unused neurons) has been explored in prior works to reduce effective FLOPs, though 2024 studies primarily focus on attention and softmax as the main optimization targets.

Memory Usage Optimizations

• Embedding/Softmax Layer Compression: In a 100K-vocabulary Transformer, the token embedding matrix and output projection are large (hundreds of millions of parameters). Techniques to compress or factorize these layers yield major memory savings. Vocabulary layer compression (Azab et al., 2024) groups tokens by their BPE merge history and uses a two-step prediction (group and token) with shared linear layers . This effectively implements a hierarchical softmax without an extra model, reducing memory footprint of the final layer by up to 3.4× with minimal performance loss . Crucially, it also avoids materializing the huge logits tensor during training, improving throughput by ~3× . Similarly, Cut Cross-Entropy (CCE) (Apple ML, 2024) eliminates the need to ever store the full vocabulary logits in memory. By computing logits on the fly in SRAM and only for relevant tokens, CCE shrinks the loss computation memory from 24 GB to ~1 MB in a 2B-parameter model (vocab 100k) . The total classifier head memory during training dropped from 28 GB to 1 GB, removing the last major memory bottleneck without hurting convergence .

• Low-Precision Representations: Reducing the precision of weights, activations, and token representations is a powerful way to cut memory usage. 4-bit and 8-bit quantization methods matured in 2024 enable storing models and intermediate states at a fraction of the original size. For instance, QRazor (Lee et al., 2025) demonstrates an end-to-end 4-bit quantization scheme (weights, activations, and the KV cache) with negligible accuracy drop (QRazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring) . It uses a “significant data razoring” compression to keep only the most salient bits and even performs arithmetic in compressed form, yielding over 60% reduction in memory and power on custom hardware . Others like QuaRot (Ashkboos et al., 2024) use learned rotations to eliminate outlier values before quantization, successfully compressing all activation and cache tensors to 4-bit without accuracy loss . These advances mean a 100K vocab LLM can run with a much smaller memory footprint, enabling deployment on GPUs with limited VRAM. Notably, reducing model precision also reduces memory bandwidth needs, often improving speed (if computation is not the bottleneck).

• Activation Memory & Offloading: Beyond embeddings and weights, activations for long input sequences consume huge memory. Approaches like activation checkpointing (store activations sparsely and recompute on the fly) are standard, but 2024 brought further improvements. For example, CompAct (Liu et al., 2025) compresses activations during training by 25–50% without extra error (CompAct: Compressed Activations for Memory-Efficient LLM Training). It does so by quantizing and encoding the intermediate tensors between layers, substantially lowering peak memory usage. On the system side, memory-centric optimizations like pipeline-parallel-aware offloading overlap CPU-GPU transfer of activations with computation (HERE) . This hides offload latency and allows models to leverage host memory as an extension of GPU memory. By carefully scheduling activation offloads per pipeline stage, Yuan et al. (2024) achieve negligible overhead for using CPU memory as swap space . Such strategies are crucial for training or inferencing with 100K vocab models that also have long sequences or many layers, keeping memory demand within hardware limits.

Inference Speed Improvements

• Speculative Decoding and Multi-Token Generation: Large-vocabulary models (100K+ tokens) suffer from slow autoregressive generation, as each token output requires a full forward pass and softmax over all vocab items. Speculative sampling algorithms address this by generating multiple tokens per iteration and then validating them with the full model. Notably, SpecExec (Svirschevski et al., 2024) demonstrated massively parallel decoding that can produce up to 20 tokens in one go, using a smaller “draft” model to propose continuations which the large model then verifies in one batch (SpecExec: Massively Parallel Speculative Decoding for Interactive LLM Inference on Consumer Devices) . This yields drastic wall-clock speedups (up to 15× in throughput) and even enables running 50B+ models on a single GPU with CPU offloading . For very long outputs, TriForce (Sun et al., 2024) introduces a hierarchical speculative decoding that segments the generation, reducing overhead for extremely long sequences (HERE). However, speculative methods face diminishing returns when the vocabulary is huge, because the draft model’s softmax still must consider 100K possibilities. A recent innovation to fix this is FR-Spec (Frequency-Ranked Speculative Sampling) (HERE) . Zhao et al. (2025) restrict the draft model to a pruned vocabulary subset (e.g. top 25% most frequent tokens) when proposing tokens. This shrinks the draft computation by 75% for a 128K vocab model , with no loss in final output fidelity (the full model still has the entire vocab for verification). By compressing the candidate space, FR-Spec boosted decoding speed by ~12% over prior speculative decoding on large-vocab LLMs .

• Layer Skipping and Early Exits: Another approach to speed up inference is to skip redundant computations for “easy” predictions. Several 2024 works explore layer skipping in Transformers during generation. Instead of running all N Transformer layers for every new token, the model can adaptively decide to use fewer layers for some steps. CLaSp (Li et al., 2024) is a self-speculative decoding method that dynamically adjusts how many decoder layers are executed based on the current context . By skipping internal layers when possible, it achieves around 1.3×–1.7× faster generation with virtually no change to the output distribution . Importantly, CLaSp requires no extra training; it uses the original model’s layers in a truncated fashion for draft generation and then finalizes with the full depth only when needed. This kind of adaptive computation reduces wasted work on tokens that the lower layers can confidently predict.

• Quantization for Faster Inference: As noted, quantization not only saves memory but also often improves latency due to better cache utilization and specialized hardware instructions. In 2024, Chen et al. introduced INT8 Token-wise Attention integrated with FlashAttention (INT-FlashAttention: Enabling Flash Attention for INT8 Quantization) . Their INT-FlashAttention executes the entire attention module with INT8 inputs/outputs, achieving 72% higher inference throughput than the FP16 version without accuracy loss . This showcases that even the core Transformer operations can be quantized for speed. Beyond attention, fully quantized LLMs (4-bit weights and INT4/INT8 activations) running on GPUs (or even ASICs) are becoming feasible, dramatically accelerating per-token inference. These improvements are particularly beneficial for large vocabularies because the sooner each token is produced, the more overall latency is amortized when generating long sequences.

Training Efficiency Improvements

• Parallelism and System-Level Optimizations: Training a Transformer with 100K vocab (often with billions of parameters) is resource-intensive. Recent research has focused on maximizing hardware utilization during training. Yuan et al. (2024) propose a combined strategy of pipeline-parallel scheduling and checkpointing that significantly boosts training throughput (HERE) . By offloading activations to host memory in a pipelined manner and carefully balancing recomputation vs storage (Compute-Memory Balanced Checkpointing), their method increased a 175B model’s FLOPs utilization from 32.3% to 42.7% on 256 GPUs . This is a large jump in efficiency obtained without changing the model architecture – purely through better use of memory and parallel compute. Another aspect is optimizing the search for the best parallelism configuration (data, tensor, pipeline splits) for a given cluster , which can cut idle times and communication overhead during training. Such automated parallelism tuning has become essential as models and vocabularies grow.

• Low-Precision Training: Using lower precision arithmetic during training can vastly improve speed and memory usage, but requires algorithmic care to maintain model quality. 2024 saw the first successful scaling of FP8 training to large LLMs. Fishman et al. (2025) managed to train a 7B model on 2 trillion tokens using 8-bit floats for weights and gradients (HERE) . They identified stability issues (outlier amplification in activations like SwiGLU) that only appear at ultra-long training durations , and introduced Smooth-SwiGLU, a modified activation function to mitigate this. With these fixes, they achieved parity with BF16 training accuracy while delivering a 34% speedup in throughput on Intel Gaudi2 accelerators . Likewise, NVidia’s research on COAT and related frameworks compresses optimizer states and gradients to 8-bit, showing that even Adam’s momentum/variance can be quantized without loss . By compressing optimizer memory and keeping more in fast memory, these methods allow larger batch sizes or sequence lengths, indirectly improving training efficiency per step.

• Memory-Efficient Training Methods: Optimizations that reduce memory per training step can be traded for speed or scale. CompAct (2025) and similar techniques compress activations, gradients, and optimizer states on the fly (CompAct: Compressed Activations for Memory-Efficient LLM Training). For example, by encoding activations in a compact form during backward pass, one can shrink memory overhead by ~30%, which can be used to increase batch size (improving GPU utilization) or to fit longer sequences (reducing the number of forward passes needed). Another line of work reduces the need to update all parameters: parameter-efficient fine-tuning (e.g. adapters, LoRA) was extended in 2024 to extremely large models to avoid storing full gradient/history for the 100% of weights. Instead, only a small subset of weights are updated, which cuts memory and compute. For instance, Facebook’s TokenTune (EMNLP 2024) selects a subset of input tokens to backpropagate through, saving activation memory by not storing gradients for every token in the batch (HERE) . This method achieved fine-tuning memory usage at ~21% of the full finetune cost, with similar accuracy . While geared toward finetuning, it exemplifies strategies to curtail unnecessary computation and storage during training. In aggregate, these innovations enable training 100K-vocab Transformers more efficiently – whether by doing less work (through quantization or selective updates) or by utilizing hardware better (through parallelism and memory management). Each contributes to pushing the feasible limits of model size and vocabulary without incurring prohibitive training times or costs.