NVIDIA Optimises vLLM and llama.cpp With Up To 1.9x Performance Boost on RTX GPUs

NVIDIA has announced streamlined local AI support specifically targeting RTX graphics cards with 24GB or more VRAM, addressing a critical gap for developers running inference at home or in small data centres. The company has optimised two of the most popular open-source inference frameworks—vLLM and llama.cpp—achieving performance improvements of up to 1.9x on compatible hardware.

These optimisations directly reduce latency and increase throughput for local LLM deployments, making it more practical to run larger models on consumer and prosumer GPUs. The focus on enabling simplified setup removes friction for practitioners looking to move away from cloud API costs and take full control of their inference infrastructure.

For engineers deploying local models, this represents a meaningful efficiency gain that could enable previously unviable use cases on mid-tier hardware. The improvements span both the CUDA-optimised vLLM path and the portable llama.cpp implementation, giving practitioners options across their existing GPU ecosystems.

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Source: Google News · Relevance: 9/10