Datasets:

IAExplore
/

iaexplore-hardware

---
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/}
}
```

	---
	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/}
	}
	```