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Note: we highly recommend to turn on attn_temperature_tuning to improve accuracy for long contexts longer than 32K tokens, and VLLM_DISABLE_COMPILE_CACHE=1 is required.
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**Multimodality:**
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The Llama 4 models excel at image understanding up to 8-10 images. By default, vLLM server accepts 1 image per request. Please pass `--limit-mm-per-prompt image=10` to serve up to 10 images per request with OpenAI-compatible API. We also recommend checking out our multi-image offline inference example with Llama-4 [here](https://github.com/vllm-project/vllm/blob/v0.8.3/examples/offline_inference/vision_language_multi_image.py).
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***Boost Performance & Context Length:** Set `--kv-cache-dtype fp8` to potentially double the usable context window and gain a performance boost. We observe little to no accuracy drop in relevant evaluations with this setting.
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***Maximize Context Window (up to 10M):** To fully utilize the maximum context windows (up to 10M for Scout), we recommend serving across multiple nodes using tensor parallelism or pipeline parallelism. Follow our distributed inference guide [here](https://docs.vllm.ai/en/latest/serving/distributed_serving.html).
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***Improve Long Context Accuracy (\>32K):** We highly recommend adding `--override-generation-config='{"attn_temperature_tuning": true}'` to improve accuracy for contexts longer than 32K tokens.
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