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+## Benchmarking LLM inference speed
+
+Calling `llama-bench` from [`llama.cpp`](https://github.com/ggerganov/llama.cpp)
+to benchmark the speed of prompt processing and text generation,
+using different models and different number of tokens.
+
+Each benchmark scenario is repeated 5 times and run on its own to be able to
+enforce a timeout, which is calculated based on the model size (to be loaded
+into memory/VRAM using a conservative 250 MB/sec read speed), the number of
+tokens tested, and expected min tokens/sec -- requiring faster inference speed
+for more tokens as per below.
+
+**Prompt processing performance targets:**
+
+| Tokens | Expected tokens/sec |
+|--------|-------------------|
+| 16 | 2 |
+| 128 | 10 |
+| 512 | 25 |
+| 1024 | 50 |
+| 4096 | 250 |
+| 16384 | 1000 |
+
+**Text generation performance targets:**
+
+| Tokens | Expected tokens/sec |
+|--------|-------------------|
+| 16 | 1 |
+| 128 | 5 |
+| 512 | 25 |
+| 1024 | 50 |
+| 4096 | 250 |
+
+So running the benchmark on a hardware that can generate 512 tokens with 22
+tokens/sec speed will not test 1024 and larger token lenghts and will stop early
+to save compute resources. If you want to allow longer runs, use the
+`--benchmark-timeout-scale` flag to increase the timeouts.
+
+### Usage
+
+```sh
+docker run --gpus all --rm --init ghcr.io/sparecores/benchmark-llm:main
+```
+
+### Models
+
+The default list of models to download and benchmark is:
+
+- [SmolLM-135M](https://huggingface.co/QuantFactory/SmolLM-135M-GGUF/resolve/main/SmolLM-135M.Q4_K_M.gguf)
+- [Qwen1.5-0.5B](https://huggingface.co/Qwen/Qwen1.5-0.5B-Chat-GGUF/resolve/main/qwen1_5-0_5b-chat-q4_k_m.gguf)
+- [gemma-2b](https://huggingface.co/mlabonne/gemma-2b-GGUF/resolve/main/gemma-2b.Q4_K_M.gguf)
+- [LLaMA-7b](https://huggingface.co/TheBloke/LLaMA-7b-GGUF/resolve/main/llama-7b.Q4_K_M.gguf)
+- [phi-4](https://huggingface.co/microsoft/phi-4-gguf/resolve/main/phi-4-q4.gguf)
+- [Llama-3.3-70B](https://huggingface.co/unsloth/Llama-3.3-70B-Instruct-GGUF/resolve/main/Llama-3.3-70B-Instruct-Q4_K_M.gguf)
+
+You can override the default list of models by passing the `--model-urls` flag,
+but note that the models should be GGUF files, and ordered by size (start with
+the smallest).
+
+The models are cached in the `/models` directory by default, which is a
+temporary docker volume, but you can override this by passing the `--models-dir`
+flag. If you might need to rerun the benchmark multiple times, you might want to
+set a different models directory or attach an external location to avoid
+re-downloading the same models.