TensorRT-LLM: NVIDIA's Open-Source Library for Optimized LLM Inference
Deploying large language models in production requires more than just loading weights onto a GPU. To achieve acceptable throughput and latency, …
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Quantization
MLX LM: LLM Inference and Fine-Tuning on Apple Silicon
The promise of running LLMs locally on a MacBook has been seductive but incomplete. Ollama and llama.cpp made it possible, but performance left …
llama.cpp: High-Performance LLM Inference on CPU and GPU
The dream of running powerful language models entirely on your own hardware, without sending data to cloud APIs, was once considered impractical …
ik_llama.cpp: Fork of llama.cpp with IQ4_NL and Advanced Quantization
The ecosystem around llama.cpp has produced numerous forks, each exploring different optimization strategies for running LLMs efficiently on …
GPTQModel: Production-Ready LLM Quantization Toolkit for GPU and CPU
Large language models are powerful, but their size makes them expensive to deploy. A 70-billion-parameter model in 16-bit precision requires …
ExLlamaV3: High-Performance LLM Inference Engine
Running large language models on consumer hardware requires efficient inference engines that squeeze every drop of performance from available GPU …