High E2E latency on fine-tuned Gemma 4 26B despite low TTFT [R]

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A recent post on a British ML subreddit discusses issues with high end-to-end (E2E) latency for a fine-tuned Gemma 4 model, despite the effective inference footprint being much smaller than expected. The author observed that even though the model is finely tuned to handle about 4 billion parameters during serving, the overall generation process takes between 3 and 5 seconds.

• This discrepancy in latency suggests there might be underlying hardware or software bottlenecks not directly related to the size of the model itself.
• The post highlights the challenges faced by researchers and engineers when dealing with large models like Gemma, where optimizing for both performance and effective inference is crucial but often difficult due to various system limitations.
• It also indicates that speculative decoding techniques such as EAGLE or Medusa-style methods might be necessary steps in addressing this issue, though the author acknowledges these are not yet proven solutions.

The discussion underscores the ongoing challenges and research needed for large language models to perform at optimal levels without significant latency increases.

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