Exploring the differences between models in the same family is always extra interesting as we get to isolate what at least on the surface appears to be a single variable - the number of parameters.
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Exploring the differences between models in the same family is always extra interesting as we get to isolate what at least on the surface appears to be a single variable - the number of parameters.
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But is this really a single variable?
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But is this really a single variable?
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If we pick a specific example (lets say 
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If we pick a specific example (lets say 
https://huggingface.co/google/gemma-3-4b-it
google/gemma-3-4b-it
model
resource
https://huggingface.co/google/gemma-3-4b-it
 vs 
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 vs 
https://huggingface.co/google/gemma-3-12b-it
google/gemma-3-12b-it
model
resource
https://huggingface.co/google/gemma-3-12b-it
 vs 
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 vs 
https://huggingface.co/google/gemma-3-27b-it
google/gemma-3-27b-it
model
resource
https://huggingface.co/google/gemma-3-27b-it
) we find that this single variable is actually a plethora of differences:
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) we find that this single variable is actually a plethora of differences:
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- The 
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- The 
hidden_size
`hidden_size`
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, 
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, 
intermediate_size
`intermediate_size`
inline_code
 and 
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 and 
num_hidden_layers
`num_hidden_layers`
inline_code
 are all different: not only are there more layers as the model grows larger, the layers themselves grow larger.
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 are all different: not only are there more layers as the model grows larger, the layers themselves grow larger.
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- 
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- 
num_attention_heads
`num_attention_heads`
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 and 
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 and 
num_key_value_heads
`num_key_value_heads`
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 are also all different: the larger models split the attention into more groups.
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 are also all different: the larger models split the attention into more groups.
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What is the final outcome of all these differences?  This answer turns out to be task specific but has 4 archetypes worth noting:
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What is the final outcome of all these differences?  This answer turns out to be task specific but has 4 archetypes worth noting:
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Cars, Objects, Shapes: Everyone passes - smaller can do the job.
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Cars, Objects, Shapes: Everyone passes - smaller can do the job.
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Arithmetic, Brackets: Everyone fails - model architecture is ill-suited for this task
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Arithmetic, Brackets: Everyone fails - model architecture is ill-suited for this task
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Boolean, Sort: There's a threshold of "big enough" you have to cross, any smaller and it fails.
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Boolean, Sort: There's a threshold of "big enough" you have to cross, any smaller and it fails.
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Objects, Shuffle: Bigger models demonstrate robust scaling in performance especially as complexity grows.
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Objects, Shuffle: Bigger models demonstrate robust scaling in performance especially as complexity grows.
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The practical lesson here is that since size is not really a single variable, *bigger is not necessarily better*! There's a surprising number of domains where you can 'downgrade' to a smaller model and gain improvements in latency and throughput without sacrificing your downstream task performance.
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The practical lesson here is that since size is not really a single variable, *bigger is not necessarily better*! There's a surprising number of domains where you can 'downgrade' to a smaller model and gain improvements in latency and throughput without sacrificing your downstream task performance.
Exploring the differences between models in the same family is always extra interesting as we get to isolate what at least on the surface appears to be a single variable - the number of parameters.

But is this really a single variable?

If we pick a specific example (lets say https://huggingface.co/google/gemma-3-4b-it vs https://huggingface.co/google/gemma-3-12b-it vs https://huggingface.co/google/gemma-3-27b-it) we find that this single variable is actually a plethora of differences:

- The `hidden_size`, `intermediate_size` and `num_hidden_layers` are all different: not only are there more layers as the model grows larger, the layers themselves grow larger.
- `num_attention_heads` and `num_key_value_heads` are also all different: the larger models split the attention into more groups.

What is the final outcome of all these differences?  This answer turns out to be task specific but has 4 archetypes worth noting:

Cars, Objects, Shapes: Everyone passes - smaller can do the job.

Arithmetic, Brackets: Everyone fails - model architecture is ill-suited for this task

Boolean, Sort: There's a threshold of "big enough" you have to cross, any smaller and it fails.

Objects, Shuffle: Bigger models demonstrate robust scaling in performance especially as complexity grows.

The practical lesson here is that since size is not really a single variable, *bigger is not necessarily better*! There's a surprising number of domains where you can 'downgrade' to a smaller model and gain improvements in latency and throughput without sacrificing your downstream task performance.
🚀
John6666