Comparing tokens per second across LLMs

Tokens per second (TPS) is one of the most important metrics we track for LLM performance. When comparing performance across two different LLMs, you need to adjust TPS based on the models’ tokenizers. The tokenizer is a small model that takes human-readable input text and turns it into the tokens that an LLM uses for inference. Different LLMs have different tokenizers of varying levels of efficiency:

• Llama 3 models use a variant of tiktoken, a tokenizer developed by OpenAI and used by GPT-4.

• Llama 2 models use a custom-made LlamaTokenizer developed by Meta based on sentencepiece.

• Mistral 7B and Mixtral 8x7B use Mistral’s first-generation tokenizer, while Mixtral 8x22B uses Mistral’s third-generation tokenizer.

• GPT-4 uses tiktoken, an open source tokenizer developed by OpenAI.

Tokenizers vary widely in efficiency. In the Llama 3 announcement, Meta claimed that the Llama 3 tokenizer is up to 15% more efficient than the Llama 2 tokenizer. If that’s the case, generating 85 tokens with Llama 3 yields the same human-readable output as generating 100 tokens with Llama 2.

But making a true comparison across LLMs is more complicated than that. Depending on the language, style, and structure of the model input and output, different tokenizers can be more or less efficient. For example, the Llama 3 tokenizer is substantially better than the Llama 2 tokenizer for code, but only slightly better for prose.

When evaluating multiple LLMs, or upgrading to the latest generation (e.g. switching from Llama 2 to Llama 3), you need to adjust your TPS comparisons to reflect real-world use to accurately calculate changes in latency, throughput, and cost.

Comparing tokenizers across inputs

As a simple demonstration of tokenizer variance across different inputs, I ran three different text samples through various tokenizers:

1. Novel: the first paragraph of Moby Dick (1,107 characters)

2. Poetry: Shakespeare’s Sonnet 18 (620 characters)

3. Code: The sample inference code for Llama 3 from its model card on Hugging Face (842 characters).

Token counts were generated using this fantastic tokenizer playground by Joshua Lochner (Xenova), which runs in-browser with transformers.js.

Here's how many tokens each model takes to encode each text sample:

Versus Llama 3’s efficient tokenizer, other open source LLMs like Llama 2 and Mistral need substantially more tokens to encode the same text. GPT-4 has nearly identical performance, as it uses a very similar tiktoken-based tokenizer.

Here's the relative increase in tokens needed by each model versus Llama 3:

Llama 3’s tokenizer is more efficient in general, but shines brightest when tokenizing code samples. So if you’re building a code completion app, you should adjust TPS metrics differently than if you’re building a novel summarizing tool.

Making clear TPS comparisons for open source LLMs

End users don’t explicitly care about tokens per second. They care that the software they’re using is fast and responsive. When switching between open source LLMs, such as upgrading from Llama 2 to Llama 3, ensure your TPS metrics for latency and total throughput are accurate by:

1. Curating a representative sample of inputs and outputs for your use case, whether it’s code autocomplete in Python or summarizing YouTube videos in Korean.

2. Running your sample texts through the tokenizer for the old and new models. Calculate the relative efficiency of each tokenizer.

3. Adjusting your TPS calculation to account for the relative value of each token generated.

As an example, let’s say that I was switching from Llama 2 to Llama 3. My app, running on Llama 2, required 100 tokens per second for a great user experience. When I deploy Llama 3 with the same configuration and batch size, I notice I’m only getting 90 tokens per second. But after testing the tokenizer, I find that it’s 15% more efficient on sample input and output from my app. In this case, the 90 tokens per second is actually slightly faster than what I had before with Llama 2 in real-world use.

By adjusting TPS calculations for variance in tokenizer, you can set accurate performance targets when switching between open source models. Then, you can hit these performance targets with hardware like H100 MIG GPUs and software optimizations like TensorRT-LLM and FP8 precision.