Fast tokens. Slow answers.
Why a fast decoder can still leave you waiting, and the four measurements that make a comparison useful.
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The numbers behind the numbers.
Fast tokens. Slow answers.
Why a fast decoder can still leave you waiting, and the four measurements that make a comparison useful.
Read the storyThe reading desk.
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Best abliterated LLM APIs
The useful comparison starts with the work you need done, not the loudest promise of zero refusals.
Make the model fit.
A smaller weight file is the beginning of a memory budget. Context, runtime and quality still have a bill to pay.
Abliterated LLMs, via API
A working request, a sensible model choice and a way to check the answer. Leave the GPU setup out of your afternoon.
What abliteration changes
A refusal behavior, a weight modification and a training process are three different things. The distinction matters.
Compatible, up to a point
A familiar request format is a useful beginning. A reliable application needs a few more checks.
A big context can forget.
Fitting the documents into the window is easy to count. Getting the right fact back is the part to test.
Do the inference maths.
Idle hours, output tokens and rejected answers belong in the same budget. Start with your own assumptions.
Test the work, not the vibe.
A small, carefully chosen task set tells you more than an impressive answer selected after the fact.
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A model needs
a budget, too.
Tokens are only part of the bill. Work through the cost of an API, a rented GPU or the machine already on your desk.
Do the inference maths First, make the model fit ↗