and GPUs, Intel CPUs and GPUs, PowerPC CPUs, TPU, and AWS Neuron. - Prefix caching support - Multi-lora support For more information, check out the following: - vLLM announcing blog post (intro to PagedAttention) split.json --enable- →chunked-prefill --max-num-batched-tokens 256 ``` ## 1.3.5 Limitations - LoRA serving is not supported. - Only LLM models are currently supported. LLaVa and encoder-decoder models = parser.parse_args() main(args) ## 1.10.8 Lora With Quantization Inference Source https://github.com/vllm-project/vllm/blob/main/examples/lora_with_quantization_inference.py. ```python """

and GPUs, Intel CPUs and GPUs, PowerPC CPUs, TPU, and AWS Neuron. - Prefix caching support - Multi-lora support For more information, check out the following: - vLLM announcing blog post (intro to PagedAttention) split.json --enable- →chunked-prefill --max-num-batched-tokens 256 ``` ## 1.3.5 Limitations - LoRA serving is not supported. - Only LLM models are currently supported. LLaVa and encoder-decoder models = parser.parse_args() main(args) ## 1.10.8 Lora With Quantization Inference Source https://github.com/vllm-project/vllm/blob/main/examples/lora_with_quantization_inference.py. ```python """

and GPUs, Intel CPUs and GPUs, PowerPC CPUs, TPU, and AWS Neuron. - Prefix caching support - Multi-lora support For more information, check out the following: - vLLM announcing blog post (intro to PagedAttention) split.json --enable- →chunked-prefill --max-num-batched-tokens 256 ``` ## 1.3.5 Limitations - LoRA serving is not supported. - Only LLM models are currently supported. LLaVa and encoder-decoder models = parser.parse_args() main(args) ## 1.10.8 Lora With Quantization Inference Source https://github.com/vllm-project/vllm/blob/main/examples/lora_with_quantization_inference.py. ```python """

PowerPC CPUs, TPU, and AWS Trainium and Inferentia Accelerators. - Prefix caching support - Multi-lora support For more information, check out the following: - vLLM announcing blog post (intro to PagedAttention) split.json --enable- →chunked-prefill --max-num-batched-tokens 256 ``` ## 1.3.5 Limitations - LoRA serving is not supported. - Only LLM models are currently supported. LLaVa and encoder-decoder models parser.parse_args() main(args) ``` ## 1.10.8 Lora With Quantization Inference Source https://github.com/vllm-project/vllm/blob/main/examples/lora_with_quantization_inference.py. ```python """

Support NVIDIA GPUs and AMD GPUs - (Experimental) Prefix caching support - (Experimental) Multi-lora support For more information, check out the following: - vLLM announcing blog post (intro to PagedAttention) com/vllm-project/vllm.git $ cd vllm $ # export VLLM_INSTALL_PUNICA_KERNELS=1 # optionally build for multi-LoRA capability $ pip install -e . # This may take 5-10 minutes. ``` Tip: Building from source requires _split.json --enable- →chunked-prefill --max-num-batched-tokens 256 ``` ## 1.3.5 Limitations - LoRA serving is not supported. - Only LLM models are currently supported. LLaVa and encoder-decoder models

and GPUs, Intel CPUs and GPUs, PowerPC CPUs, TPU, and AWS Neuron. - Prefix caching support - Multi-lora support For more information, check out the following: - vLLM announcing blog post (intro to PagedAttention) split.json --enable- →chunked-prefill --max-num-batched-tokens 256 ``` ## 1.3.5 Limitations - LoRA serving is not supported. - Only LLM models are currently supported. LLaVa and encoder-decoder models = parser.parse_args() main(args) ## 1.10.8 Lora With Quantization Inference Source https://github.com/vllm-project/vllm/blob/main/examples/lora_with_quantization_inference.py. ```python """

and GPUs, Intel CPUs and GPUs, PowerPC CPUs, TPU, and AWS Neuron. - Prefix caching support - Multi-lora support For more information, check out the following: - vLLM announcing blog post (intro to PagedAttention) split.json --enable- →chunked-prefill --max-num-batched-tokens 256 ``` ## 1.3.5 Limitations - LoRA serving is not supported. - Only LLM models are currently supported. LLaVa and encoder-decoder models = parser.parse_args() main(args) ## 1.10.8 Lora With Quantization Inference Source https://github.com/vllm-project/vllm/blob/main/examples/lora_with_quantization_inference.py. ```python """

Support NVIDIA GPUs and AMD GPUs - (Experimental) Prefix caching support - (Experimental) Multi-lora support For more information, check out the following: - vLLM announcing blog post (intro to PagedAttention) com/vllm-project/vllm.git $ cd vllm $ # export VLLM_INSTALL_PUNICA_KERNELS=1 # optionally build for multi-LoRA capability $ pip install -e . # This may take 5-10 minutes. ``` Tip: Building from source requires parser.parse_args() main(args) ``` ## 1.6.7 Lora With Quantization Inference Source https://github.com/vllm-project/vllm/blob/main/examples/lora_with_quantization_inference.py. ```python """

Support NVIDIA GPUs and AMD GPUs - (Experimental) Prefix caching support - (Experimental) Multi-lora support For more information, check out the following: - vLLM announcing blog post (intro to PagedAttention) com/vllm-project/vllm.git $ cd vllm $ # export VLLM_INSTALL_PUNICA_KERNELS=1 # optionally build for multi-LoRA capability $ pip install -e . # This may take 5-10 minutes. ``` Tip: Building from source requires parser.parse_args() main(args) ``` ## 1.8.7 Lora With Quantization Inference Source https://github.com/vllm-project/vllm/blob/main/examples/lora_with_quantization_inference.py. ```python """

Support NVIDIA GPUs and AMD GPUs - (Experimental) Prefix caching support - (Experimental) Multi-lora support For more information, check out the following: - vLLM announcing blog post (intro to PagedAttention) split.json --enable- →chunked-prefill --max-num-batched-tokens 256 ``` ## 1.3.5 Limitations - LoRA serving is not supported. - Only LLM models are currently supported. LLaVa and encoder-decoder models = parser.parse_args() main(args) ``` ## 1.10.7 Lora With Quantization Inference Source https://github.com/vllm-project/vllm/blob/main/examples/lora_with_quantization_inference.py. ```python """