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This research aims to enhance large language model inference efficiency on consumer-grade multi-GPU systems.

February 6, 20260 citationsOpen Access

Efficient Large Language Model Inference on Consumer-Grade Multi-GPU Systems: A Comprehensive Survey and Implementation of the Ember Engine

Key Points

This research aims to enhance large language model inference efficiency on consumer-grade multi-GPU systems.
Survey of existing LLM inference optimization techniques
Implementation of Ember, a lightweight CUDA inference engine
Utilization of Pipeline Parallelism with Chunked Prefill with Overlap
Experimental setup using dual NVIDIA RTX 3080 Ti GPUs
Ember achieves a 1.16× speedup ratio on dual-GPU configuration
Ember outperforms llama.cpp's Layer Split strategy, achieving 1.01× speedup
Compared to ExLlamaV3's strategy, Ember maintains lower latency growth
Results validate the effectiveness of the proposed optimization methods.

Abstract

Deploying large language model (LLM) inference has become a core challenge in artificial intelligence.Current mainstream inference frameworks are primarily optimized for datacenter-grade hardware, withrelatively insufficient support for consumer-grade multi-GPU systems. This paper systematicallysurveys key techniques in LLM inference optimization, including FlashAttention, PagedAttention, KVCache management, and multi-GPU parallelism strategies, while providing an in-depth analysis of thetechnical characteristics and limitations of existing inference frameworks. Building upon thisfoundation, we propose and implement Ember—a lightweight CUDA inference engine specificallyoptimized for consumer-grade multi-GPU systems. Ember employs a Pipeline Parallelism strategy withChunked Prefill with Overlap technique to reduce PCIe communication exposure. Experimental resultsdemonstrate that on a dual NVIDIA RTX 3080 Ti configuration, Ember achieves a 1.16× dual-GPUspeedup ratio, while llama.cpp's Layer Split strategy only achieves 1.01× speedup, validating theeffectiveness of our proposed approach. Compared to ExLlamaV3's Tensor Parallel strategy, Ember'sPipeline Parallel approach achieves comparable scaling efficiency while maintaining lower time-to-first-token latency growth. This research provides practical guidance for LLM inference on consumer-grade hardware and demonstrates the commercial application potential in this field.

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