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  "body_markdown": "Do Bigger GPT Models Always Perform Better?\nExploring model size, width versus depth, and compute allocation with a small GPT model\nUpdated\nJuly 20, 2026\n•\n11\nmin read\n•\nView as Markdown\nA\nAman Jain\nProduct manager learning to build AI from the inside out — from LLM architecture to product applications.\nPart of series\nInside GPT: Experiments and Trade-offs\nTL;DR: What I tested and observed\nExperiment 1 — Different sizes, fixed tokens:\nI increased model width, depth, and total parameter count while giving every model the same 200,192 training tokens. In this setup, the smallest model achieved the lowest validation loss and trained the fastest.\nExperiment 2 — Width versus depth:\nI kept the total parameter count close to 13.3 million but distributed those parameters differently. The wider, shallower model produced the best validation result and substantially higher throughput than the deeper alternatives.\nExperiment 3 — Fixed estimated compute:\nI gave smaller models more training tokens and larger models fewer so that estimated training compute remained approximately equal. In this comparison, the smallest model achieved the strongest result by a relatively large margin.\nGitHub:\nView the complete experiment notebook\nThese results do not tell us that smaller or shallower models are universally better. They show what happened with this particular model architecture, WikiText dataset, hardware, and limited training budget.\nWhen we hear that a model has more parameters, it is natural to assume that it should perform better.\nMore parameters give a model additional capacity. In theory, that capacity can help it represent more complex patterns and relationships. But a larger model may also need more data, optimization steps, and compute before that additional capacity becomes useful.\nResearch on scaling laws has explored how model size, training data, and compute interact. Other studies have asked whether parameter count alone is enough to describe a model—or whether the way those parameters are distributed across width and depth also matters.\nI used these ideas as inspiration and explored the same questions using the small GPT-style model and WikiText dataset from my previous experiments:\nGiven my model architecture, dataset, hardware, and training budget, what happens when I change model capacity and how that capacity is distributed?\nExperiment 1: Increasing overall model capacity\nFor the first experiment, I created three progressively larger models:\nCAPACITY_TOKEN_BUDGET = 200_000\n\nCAPACITY_CONFIGS = [\n    {\"label\": \"small\",    \"emb_dim\": 64,  \"n_heads\": 2, \"n_layers\": 2},\n    {\"label\": \"baseline\", \"emb_dim\": 128, \"n_heads\": 4, \"n_layers\": 4},\n    {\"label\": \"large\",    \"emb_dim\": 192, \"n_heads\": 6, \"n_layers\": 6},\n]\nModel\nEmbedding dimension\nAttention heads\nTransformer blocks\nParameters\nSmall\n64\n2\n2\n6.54M\nBaseline\n128\n4\n4\n13.67M\nLarge\n192\n6\n6\n21.99M\nThe embedding dimension, attention-head count, number of transformer blocks, and total parameter count increased together.\nEvery model processed the same\n200,192 training tokens\nover\n782 optimizer steps\n.\nThis was a fixed-token comparison: each model saw the same amount of training data, but the larger models performed more computation for every token.\nMy hypothesis\nI expected the larger models to achieve lower validation loss because they had more representational capacity.\nAt the same time, I expected the improvement to diminish as the models became larger, especially because all three received the same relatively small token budget.\nThe results did not follow that pattern.\nResults\nMetric\nSmall\nBaseline\nLarge\nEmbedding dimension\n64\n128\n192\nAttention heads\n2\n4\n6\nTransformer blocks\n2\n4\n6\nParameters\n6.54M\n13.67M\n21.99M\nTraining tokens\n200,192\n200,192\n200,192\nRuntime\n146.7 sec\n239.9 sec\n388.3 sec\nTokens per second\n1,364.7\n834.3\n515.6\nPeak memory\n714.9 MB\n854.0 MB\n990.7 MB\nFinal validation loss ↓\n6.8850\n6.9119\n6.9598\nPerplexity ↓\n977.46\n1,004.12\n1,053.45\nTop-1 accuracy ↑\n12.19%\n12.58%\n11.48%\nEstimated compute\n0.0079 PFLOPs\n0.0164 PFLOPs\n0.0264 PFLOPs\nIn this run, the smallest model achieved the lowest validation loss and perplexity.\nThe baseline model produced slightly higher top-1 accuracy, although the difference was only 0.39 percentage points. Its validation loss was still higher than the small model’s.\nAccuracy and cross-entropy loss measure different things. Accuracy only checks whether the model’s highest-scoring token was correct. Loss also considers how much probability the model assigned to the correct token.\nThe large model was worse on both validation loss and top-1 accuracy.\nWhat changed as the model became larger?\nMoving from the small model to the large model:\nParameter count increased from 6.54 million to 21.99 million.\nRuntime increased from 146.7 to 388.3 seconds.\nThroughput fell from 1,364.7 to 515.6 tokens per second.\nPeak memory increased from 714.9 MB to 990.7 MB.\nValidation loss increased from 6.8850 to 6.9598.\nIn this setup, increasing capacity increased the cost of training without improving the final validation result.\nOne possible interpretation is that the larger models were undertrained relative to their capacity. They had more parameters to optimize but received exactly the same number of training tokens and optimizer steps as the smaller model.\nThis does not mean that the larger models could never perform better. They may have needed more training tokens, additional optimizer steps, a different learning rate, different regularization, or a larger and more varied dataset.\nThe result only tells us that the additional capacity did not help under the conditions used in this experiment.\nThe fixed-token comparison answers:\nWhich model performed better after seeing the same amount of training data?\nI also evaluated the models under a fixed wall-clock budget. As expected, the smaller model processed more tokens and returned a lower validation loss than the larger models.\nExperiment 2: Where should the parameters go?\nExperiment 1 changed several architectural variables together. The larger models were wider, deeper, and larger in total parameter count.\nThat made it difficult to know whether the result was mainly related to overall capacity, additional depth, or a combination of both.\nFor the second experiment, I kept the total parameter count nearly constant and changed how that capacity was distributed:\nSHAPE_TOKEN_BUDGET = 200_000\n\nSHAPE_CONFIGS = [\n    {\"label\": \"wide_shallow\", \"emb_dim\": 128, \"n_heads\": 4, \"n_layers\": 2},\n    {\"label\": \"balanced\",     \"emb_dim\": 120, \"n_heads\": 4, \"n_layers\": 7},\n    {\"label\": \"narrow_deep\",  \"emb_dim\": 112, \"n_heads\": 4, \"n_layers\": 13},\n]\nShape\nEmbedding dimension\nAttention heads\nTransformer blocks\nParameters\nWide–shallow\n128\n4\n2\n13.28M\nBalanced\n120\n4\n7\n13.29M\nNarrow–deep\n112\n4\n13\n13.24M\nThe parameter spread across the three models was less than\n0.4%\n, with each model containing approximately 13.3 million parameters.\nMy hypothesis\nI thought the deeper model might learn richer transformations by applying more transformer blocks sequentially.\nAt the same time, I expected it to train more slowly because those blocks must execute one after another. This could have created a quality-versus-speed trade-off.\nThat trade-off did not appear in my results.\nFixed-token results\nMetric\nWide–shallow\nBalanced\nNarrow–deep\nEmbedding dimension\n128\n120\n112\nAttention heads\n4\n4\n4\nTransformer blocks\n2\n7\n13\nParameters\n13.28M\n13.29M\n13.24M\nTraining tokens\n200,192\n200,192\n200,192\nRuntime\n208.0 sec\n296.0 sec\n367.7 sec\nTokens per second\n962.6\n676.2\n544.5\nPeak memory\n903.4 MB\n946.7 MB\n846.9 MB\nFinal validation loss ↓\n6.8615\n6.8739\n6.8949\nPerplexity ↓\n954.77\n966.73\n987.26\nTop-1 accuracy ↑\n13.05%\n11.80%\n12.03%\nEstimated compute\n0.0159 PFLOPs\n0.0160 PFLOPs\n0.0159 PFLOPs\nCompared with the narrow–deep model, the wide–shallow model:\nCompleted the run approximately 44% faster.\nProcessed about 77% more tokens per second.\nAchieved validation loss that was 0.0334 lower.\nAchieved top-1 accuracy that was approximately one percentage point higher.\nDespite having similar parameter counts and estimated compute, the three models had very different runtimes.\nA deeper model must execute more transformer blocks sequentially. In this setup, that additional depth increased runtime without producing an obvious quality benefit.\nThe absolute loss difference was not enormous, so I would not interpret this as a universal preference for shallow models.\nWhat stood out more clearly was that the deeper architecture did not show enough improvement to compensate for its substantially longer runtime.\nExperiment 3: Same estimated compute, different allocations\nIn Experiment 1, every model processed the same number of tokens. This meant that the larger models also consumed more estimated computation.\nFor the final experiment, I held approximate training compute constant instead.\nI used the following approximation:\n$$C \\approx 6NT$$\nwhere:\n(C) is estimated training compute.\n(N) is the number of model parameters.\n(T) is the number of training tokens.\nUnder this approximation, a model with more parameters must process fewer tokens if the overall compute budget remains fixed.\nThe resulting configurations were:\nModel\nParameters\nToken budget\nEstimated compute\nSmall\n6.54M\n418,304\n0.0164 PFLOPs\nBaseline\n13.67M\n200,192\n0.0164 PFLOPs\nLarge\n21.99M\n124,416\n0.0164 PFLOPs\nBased on the first experiment, I expected the small model to benefit from receiving more training tokens.\nBut I did not know whether that advantage would be large enough to overcome the additional representational capacity of the larger models.\nThe result was much clearer than in the earlier comparisons.\nThe smallest model achieved the strongest result by the largest margin of the three experiments.\nOne possible interpretation is that, under this estimated compute budget, the small model benefited more from additional training exposure than the large model benefited from additional capacity.\nThe large model had more parameters, but it received only 124,416 training tokens. In this setup, that may not have been enough training for the model to use its additional capacity effectively.\nWhat I observed across the three experiments\nEach experiment answered a slightly different question.\nFixed tokens\nThe smallest model achieved the lowest validation loss among the progressively scaled small, baseline, and large configurations.\nThe larger models required more runtime, memory, and estimated compute, but the additional capacity did not improve validation quality within the 200,192-token budget.\nApproximately fixed parameter count\nThe wide–shallow architecture achieved the best validation result and the highest training throughput.\nThe deeper architectures did not show enough quality improvement to compensate for their additional runtime. In these runs, their validation results were also slightly worse.\nFixed estimated compute\nThe smallest model processed substantially more training tokens and achieved the strongest result by the largest margin.\nIn this setup, allocating the budget toward more training exposure appeared more effective than allocating it toward a larger parameter count.\nLimitations of the experiment\nOne learning rate for every architecture\nOne primary random seed\nLimited data and training budget\nApproximate compute measurement\nMemory measurement included helper processes - not just model.\nReferences\nKaplan, J. et al. (2020).\nScaling Laws for Neural Language Models\nHoffmann, J. et al. (2022).\nTraining Compute-Optimal Large Language Models\nTay, Y. et al. (2021).\nScale Efficiently: Insights from Pre-training and Fine-tuning Transformers\nPetty, J. et al. (2023).\nThe Impact of Depth on Compositional Generalization in Transformer Language Models\n#\ndeeplearning\n#\nllm\n#\ntransformers\n1\nviews",
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