Efficient Deployment of Lightweight Language Models on Low-Resource Personal Computers: A Quantitative Benchmark Study

This paper presented the first systematic benchmark study of five quantized lightweight LLMs deployed exclusively on CPU-based consumer laptop hardware with 8 GB RAM, providing empirical evidence to answer a question of significant practical importance: which models can actually run usably on the hardware that most people own. Our core finding is clear and actionable: sub-1B parameter models quantized to Q2_K or Q4_K_M format are fully viable for real-time local inference on 8 GB RAM hardware, achieving 19–23 tok/s within a 1 GB memory footprint. This performance level supports interactive applications including chatbots, writing assistants, coding helpers, and offline document analysis tools — all without cloud dependency, internet connection, or GPU. The TinyLlama 1.1B Q2_K model, in particular, emerges as the optimal choice for constrained edge deployment, offering the best throughput, lowest RAM usage, and fastest cold-start time of any model tested. The quantization ablation analysis reveals an important and non-obvious insight: on memory-bandwidth-constrained consumer hardware, more aggressive quantization (Q2_K) can outperform higher-precision alternatives (Q4_K_M) in throughput. This occurs because reduced weight size decreases memory bandwidth demand per decoding step, a bottleneck that dominates performance on DDR4-constrained systems. This finding challenges the common assumption that higher quantization quality always translates to better system performance. A critical finding for practitioners considering Llama 3.2 deployment is the RAM amplification caused by large context windows. The 128K token context window in Llama 3.2 1B and 3B causes KV cache pre-allocation that consumes 4–5 GB of RAM regardless of actual inference context length, making these models impractical for 8 GB RAM deployment in their default configuration. The --ctx-size 2048 optimization flag resolves this issue, potentially transforming Llama 3.2 1B into one of the most capable models deployable within a 1.5 GB RAM budget. This optimization, combined with partial GPU offload for available discrete GPUs, represents the most impactful configuration changes for edge deployment. Looking at the broader picture, our results validate the edge AI deployment hypothesis: meaningful, general-purpose AI inference is achievable on consumer hardware without cloud dependency. The combination of efficient quantized model formats, optimized CPU inference engines, and careful model selection creates a viable pathway to private, offline, and accessible AI applications running directly on end-user devices. Future work will extend this study in several directions. GPU offload characterization using the GTX 1650 Max-Q will complete the CPU+GPU hybrid deployment picture. Output quality evaluation via perplexity measurements and task-specific benchmarks (MMLU, HumanEval, GSM8K) will quantify the quality-efficiency trade-offs across quantization levels. Extension to a broader hardware range including AMD Ryzen laptops and Apple Silicon will test generalizability of our findings. Finally, evaluation of newer model families including Mistral 3B, Gemma 2 2B, and Qwen 1.5B will keep the benchmark current as the lightweight LLM landscape continues to evolve rapidly.

[1] T. Brown et al., "Language Models are Few-Shot Learners," in Advances in Neural Information Processing Systems (NeurIPS), vol. 33, pp. 1877–1901, 2020. [2] K. Schwartz, J. Dodge, N. A. Smith, and O. Etzioni, "Green AI," Communications of the ACM, vol. 63, no. 12, pp. 54–63, Dec. 2020. [3] G. Gerganov, "llama.cpp: LLM Inference in C/C++," GitHub Repository, 2023. [Online]. Available: https://github.com/ggml-org/llama.cpp [4] B. Jacob et al., "Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference," in Proc. IEEE/CVF CVPR, pp. 2704–2713, 2018. [5] E. Frantar, S. Ashkboos, T. Hoefler, and D. Alistarh, "GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers," arXiv preprint arXiv:2210.17323, Oct. 2022.