Discover Sparse Llama: A 50% pruned, GPU-optimized Llama 3.1 model with 2:4 sparsity, enabling faster, cost-effective inference without sacrificing accuracy.

Large Language Models (LLMs) have emerged as transformative tools in research and industry, with their performance directly correlating to model size. However, training these massive models presents significant challenges, related to computational resources, time, and cost. The training process for state-of-the-art models like Llama 3 405B requires extensive hardware infrastructure, utilizing up to 16,000 H100 GPUs over 54 days. Similarly, models like GPT-4, estimated to have one trillion parameters, demand extraordinary computational power. These resource requirements create barriers to entry and development in the field, highlighting the critical need for more efficient training methodologies for advancing LLM technology while reducing

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I have been working on a side project with an LLM integration, which someday I will share more about. For this side project, I have been experimenting with various LLM-quantization formats and found t...

The paper evaluates the performance of code LLMs (large language models) on various benchmarks, focusing on inference time and solution correctness. The evaluation includes four code LLMs: CodeLlama, CodeGemma, CodeQwen, and DeepSeek Coder. The models were tested on three benchmarks: HumanEval, MBPP, and MCEVAL. The paper also explores the effect of quantization levels (2-bit, 4-bit, and 8-bit) on the models' performance. The results show that inference time grows with higher precision (more qbits), and failed solutions take longer to generate than correct ones. CodeQwen stands out, taking more time to infer both correct and incorrect solutions, especially on the HumanEval benchmark. The paper also presents a regression analysis, which confirms the observed effects and highlights the interactions between the models, benchmarks, and quantization levels. The analysis shows that correct solutions require less time to generate, and inference time increases with more qbits. The paper concludes that a longer inference time does not necessarily translate to better performance, and the effect of quantization varies across models and benchmarks.

Local LLMs matured fast in 2025: open-weight families like Llama 3.1 (128K context length (ctx)), Qwen3 (Apache-2.0, dense + MoE), Gemma 2 (9B/27B, 8K ctx), Mixtral 8×7B (Apache-2.0 SMoE), and Phi-4-mini (3.8B, 128K ctx) now ship reliable specs and first-class local runners (GGUF/llama.cpp, LM Studio, Ollama), making on-prem and even laptop inference practical if you match context length and quantization to VRAM.

Run 70B LLMs locally on a 4GB GPU using AirLLM — no quantization, no cloud bills. Open-source, free, and supports Llama, Mistral & more. Continue reading on Towards AI »

Self-LLM: Run & fine-tune LLaMA, DeepSeek, ChatGLM locally with 1 line of code. Simplify LLM setup, quantization & training.

Local LLMs matured fast in 2025: open-weight families like Llama 3.1 (128K context length (ctx)), Qwen3 (Apache-2.0, dense + MoE), Gemma 2 (9B/27B, 8K ctx), Mixtral 8×7B (Apache-2.0 SMoE), and Phi-4-mini (3.8B, 128K ctx) now ship reliable specs and first-class local runners (GGUF/llama.cpp, LM Studio, Ollama), making on-prem and even laptop inference practical if you match context length and quantization to VRAM.

Large Language Models (LLMs) like ChatGPT and LLaMA drive rapid progress in generative AI, yet their huge parameter scales create severe computational and environmental burdens. High training costs, energy use, and limited device deployment hinder accessibility. Existing compression - pruning, distillation, low-rank, and quantization - reduces size but ignores complex inter-layer correlations. We propose KARIPAP, a quantum-inspired tensor network compression using Infinite Projected Entangled Pair States (iPEPS) and Tensor Renormalization Group (TRG) contraction. Unlike 1D Matrix Product States, iPEPS captures multi-directional entanglement in attention and deep transformer layers. TRG ensures polynomial-time contraction, making tensorization feasible while preserving key correlation geometry. Experiments on LLaMA-2 7B show up to 93% memory and 70% parameter reduction, with 50% faster training, 25% faster inference, and only 2-3% accuracy loss. Layer-wise entanglement profiling reveals redundancy in deeper layers, confirming their suitability for tensor factorization. KARIPAP demonstrates that modern LLMs occupy low-dimensional entanglement manifolds, enabling scalable, energy-efficient, and quantum-aware AI architectures.

When working with large language models (LLMs), one of the biggest challenges researchers and practitioners face is GPU memory limitations…

Large Language Models (LLMs) excel in diverse applications but suffer inefficiency due to massive scale. While quantization reduces computational costs, existing methods degrade accuracy in medium-sized LLMs (e.g., Llama-3-8B) due to activation outliers. To address this, we propose QUAD (Quantization with Activation Decomposition), a framework leveraging Singular Value Decomposition (SVD) to suppress activation outliers for effective 4-bit quantization. QUAD estimates activation singular vectors offline using calibration data to construct an orthogonal transformation matrix P, shifting outliers to additional dimensions in full precision while quantizing rest components to 4-bit. Additionally, QUAD enables parameter-efficient fine-tuning via adaptable full-precision outlier weights, narrowing the accuracy gap between quantized and full-precision models. Experiments demonstrate that QUAD achieves 94% ~ 96% accuracy under W4A4 quantization and 98% accuracy with W4A4/A8 and parameter-efficient fine-tuning for Llama-3 and Qwen-2.5 models. Our code is available at \href{https://github.com/hyx1999/Quad}{repository}.

Quantized Large Language Models (LLMs) are used more often in qualitative analysis because they run fast and need fewer computing resources. This study examines how different lower bits quantization levels (8-bit, 4-bit, 3-bit, and 2-bit) and quantization types affect the performance of LLaMA-3.1...

I am running a RAG pipeline, with LlamaIndex and quantized LLama3-8B-Instruct. I just installed these libraries: !pip install --upgrade huggingface_hub !pip install --upgrade peft !pip install llama-index bitsandbytes accelerate llama-index-llms-huggingface llama-index-embeddings-huggingface !pip install --upgrade transformers !pip install --upgrade sentence-transformers Then I was looking to run the quantization pipeline like this: import torch from llama_index.llms.huggingface import HuggingFaceLLM from transformers import BitsAndBytesConfig quantization_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_compute_dtype=torch.float16, bnb_4bit_quant_type="nf4", bnb_4bit_use_double_quant=True, ) However, I got this error returned to me: `ModuleNotFoundError: No module named 'huggingface_hub.inference._types'`. Last time I worked with this pipeline two months ago, the code worked, so I think LlamaIndex has changed something; especially since when I clicked on the error, it referenced to: ` from huggingface_hub.inference._types import ConversationalOutput`, but `ConversationalOutput` module doesn’t exist in HuggingFace docs. So, what should I do to fix this error and be able to run this RAG pipeline?