--- annotations_creators: - machine-generated - expert-generated language: - en - es - pt license: mit multilinguality: - multilingual size_categories: - n<1K source_datasets: - original task_categories: - tabular-classification - tabular-regression - feature-extraction task_ids: [] pretty_name: "2026 LLM Hardware & VRAM Memory Profiling Dataset" tags: - llm - vram - inference - hardware - llama-cpp - ollama - vllm - gqa - moe - benchmarks - iaexplore configs: - config_name: default data_files: - split: train path: - hardware_2026.json - models_2026.json --- # 🧠 IAExplore 2026 LLM Hardware & VRAM Memory Profiling Dataset [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.14889210.svg)](https://zenodo.org/badge/latestdoi/14889210) [![Documentation](https://img.shields.io/badge/docs-IAExplore.com-blue.svg)](https://www.iaexplore.com/html/en/blog/ai/npu-tpu-gpu-ai-hardware-guide.html) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) Official dataset providing verified architectural parameters for cutting-edge Large Language Models (LLMs) and 2026 consumer/workstation AI hardware compute profiles. Curated by the **[IAExplore Research & Systems Infrastructure Team](https://www.iaexplore.com/)**. --- ## 📌 Dataset Summary This dataset serves as the ground-truth technical foundation for: 1. **Mathematical VRAM Allocation**: Weight sizing with unquantized metadata overhead factor ($1.08$), KV-Cache memory consumption accounting for Grouped-Query Attention (GQA), and CUDA/Metal runtime execution buffers. 2. **Mixture-of-Experts (MoE) Bandwidth Profiling**: Distinguishing active parameter footprints ($P_{\text{active}}$) from total stored parameters ($P_{\text{total}}$) per token generation step. 3. **Hardware Latency & Tokens/Second Estimation**: Exact physical memory bandwidths ($\text{BW}_{\text{mem}}$), bus topologies (GDDR7, LPDDR5X, GDDR6X, HBM), and bus saturation efficiency coefficients ($\eta$). --- ## 🗂️ Data Structure ### 1. `models_2026.json` Contains verified tensor specifications for: - `llama-3.3-70b` (Meta AI) - `llama-3.1-8b` (Meta AI) - `deepseek-r1-14b` (DeepSeek AI) - `deepseek-r1-32b` (DeepSeek AI) - `qwen-2.5-coder-32b` (Alibaba Cloud) - `mixtral-8x7b-moe` (Mistral AI) - `gemma-2-9b` (Google DeepMind) - `phi-4-14b` (Microsoft Research) Key fields: - `total_params_b`: Total billion parameters. - `active_params_b`: Active parameters loaded per generation token (vital for MoE architectures). - `num_hidden_layers` ($L$): Number of transformer blocks. - `num_attention_heads` ($H_{\text{q}}$): Query heads. - `num_key_value_heads` ($H_{\text{kv}}$): Key/Value heads for GQA/MHA. - `head_dim` ($D_{\text{head}}$): Dimension per head. - `gqa_factor`: Compression ratio ($H_{\text{q}} / H_{\text{kv}}$). ### 2. `hardware_2026.json` Profiles for 2026 silicon across Discrete GPUs, Apple Silicon, and NPU/APU SoC systems: - **NVIDIA GeForce RTX 5090** (32 GB GDDR7, 1792 GB/s, 575W) - **NVIDIA GeForce RTX 4090** (24 GB GDDR6X, 1008 GB/s, 450W) - **NVIDIA GeForce RTX 4070 SUPER** (12 GB GDDR6X, 504 GB/s, 220W) - **NVIDIA GeForce RTX 4060 Mobile** (8 GB GDDR6, 256 GB/s, 115W) - **Apple M4 Max** (128 GB Unified LPDDR5X, 546 GB/s, 90W) - **Apple M4 Pro** (48 GB Unified LPDDR5X, 273 GB/s, 60W) - **Apple M4** (16 GB Unified LPDDR5X, 120 GB/s, 30W) - **AMD Ryzen AI Max+ 395 Strix Halo** (96 GB assignable, 256 GB/s, 126 TOPS, 120W) - **Intel Core Ultra 9 288V Lunar Lake** (24 GB shared, 136 GB/s, 120 TOPS, 30W) - **Qualcomm Snapdragon X Elite** (48 GB assignable, 135 GB/s, 45 TOPS, 45W) --- ## 🔬 Mathematical Formulas ### Model Weights Memory: $$\text{VRAM}_{\text{weights}} = \frac{P_{\text{total}} \times 10^9 \times (\text{bits}_{\text{quant}} / 8)}{1024^3} \times 1.08$$ ### Exact KV-Cache Memory (with GQA): $$\text{VRAM}_{\text{kv}} = \frac{2 \times L \times H_{\text{kv}} \times D_{\text{head}} \times C_{\text{ctx}} \times B_{\text{batch}} \times (\text{bits}_{\text{kv}} / 8)}{1024^3}$$ ### Autoregressive Inference Throughput (Tokens/Second): $$\text{TPS}_{\text{gen}} = \frac{\text{BW}_{\text{mem}} \times \eta_{\text{efficiency}}}{\text{VRAM}_{\text{active}} + (\text{VRAM}_{\text{kv}} \times 0.15)}$$ --- ## 🔗 Authoritative References & In-Depth Guides - **AI Hardware Architecture Guide (EN)**: [NPU, TPU and GPU Complete Guide](https://www.iaexplore.com/html/en/blog/ai/npu-tpu-gpu-ai-hardware-guide.html) - **Guía de Hardware de IA (ES)**: [Arquitecturas NPU, TPU y GPU para Inteligencia Artificial](https://www.iaexplore.com/html/es/blog/ia/npu-tpu-gpu-guia-hardware-inteligencia-artificial.html) - **Guia de Hardware de IA (PT)**: [Hardware para IA: Guia Completo NPU, TPU e GPU](https://www.iaexplore.com/html/pt/blog/ia/npu-tpu-gpu-guia-hardware-inteligencia-artificial.html) - **2026 PC & Hardware Benchmarks (EN)**: [Best Computers of 2026: Benchmark Guide](https://www.iaexplore.com/html/en/blog/comparisons/best-computers-which-pc-to-buy-based-on-your-needs-and-budget.html) - **Comparativas de Ordenadores 2026 (ES)**: [Los Mejores Ordenadores de 2026: Guía y Benchmarks](https://www.iaexplore.com/html/es/blog/comparativas/los-mejores-ordenadores-que-pc-comprar-segun-tu-uso-y-presupuesto.html) - **Melhores Computadores 2026 (PT)**: [Melhores Computadores 2026: Guia Completo de Compra](https://www.iaexplore.com/html/pt/blog/comparativos/melhores-computadores-que-pc-comprar-segundo-uso-orcamento.html) - **ARM & Mobile AI Devices 2026 (PT)**: [Melhores Tablets e Dispositivos ARM](https://www.iaexplore.com/html/pt/blog/comparativos/melhores-tablets-qualidade-preco.html) - **Landauer Principle & Reversible Computing (ES)**: [Física de la Computación y Límite Termodinámico](https://www.iaexplore.com/html/es/blog/tecnologia/computacion-reversible-principio-landauer-calor-procesadores.html) - **Interactive Neural Network Simulator**: [IAExplore Real-Time Simulator](https://www.iaexplore.com/neural-network-sim/) --- ## 📜 Citation If you use this dataset in academic research, benchmark pipelines, or LLM serving infrastructure, please cite: ```bibtex @misc{iaexplore_llm_profiler_2026, author = {IAExplore Research Team}, title = {IAExplore LLM Profiler: Deterministic VRAM and Hardware Inference Dataset}, year = {2026}, publisher = {Zenodo}, doi = {10.5281/zenodo.14889210}, url = {https://www.iaexplore.com/} } ```