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vLLM Faster LLM Inference || Gemma-2B and Camel-5B
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vLLM is a fast and easy-to-use library for LLM inference and serving.
vLLM is fast with:
- State-of-the-art serving throughput
- Efficient management of attention key and value memory with PagedAttention
- Continuous batching of incoming requests
- Fast model execution with CUDA/HIP graph
- Quantization: GPTQ, AWQ, SqueezeLLM, FP8 KV Cache
- Optimized CUDA kernels
Reach out to me:
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Timestamp
00:00 Introduction
01:23 Code Implementation
03:40 Gemma-2B Inference
10:00 Camel-5B Inference
#llm #largelanguagemodels #ai #generativeai #vllm
vLLM is fast with:
- State-of-the-art serving throughput
- Efficient management of attention key and value memory with PagedAttention
- Continuous batching of incoming requests
- Fast model execution with CUDA/HIP graph
- Quantization: GPTQ, AWQ, SqueezeLLM, FP8 KV Cache
- Optimized CUDA kernels
Reach out to me:
------------------------
------------------------
Timestamp
00:00 Introduction
01:23 Code Implementation
03:40 Gemma-2B Inference
10:00 Camel-5B Inference
#llm #largelanguagemodels #ai #generativeai #vllm
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