Calculadora VRAM para LLM
Calcule los requisitos exactos de memoria VRAM de GPU, escalado de caché KV y rendimiento en tokens por segundo para modelos LLM abiertos como LLaMA 3 y DeepSeek.
Calculate exact VRAM allocation, KV cache scaling, and hardware compatibility across consumer GPUs and data center accelerators.
| GPU / Accelerator | Total VRAM | Memory Bandwidth | Inference Fit | Speed Estimate |
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Acerca de Calculadora de VRAM para LLM y Hardware de GPU
Deploying open-weight Large Language Models (LLMs) requires careful GPU memory planning. Running out of video memory (OOM) halts inference and crashes training runs. Accurately estimating memory before provisioning cloud GPU clusters on RunPod, Lambda Labs, or AWS saves substantial operational expenses.
GPU memory consumption consists of four primary components: model weights, KV cache buffers for maintaining conversation history, activation memory during forward passes, and CUDA framework context overhead. Quantization schemes like FP8, INT4, and GGUF drastically alter these figures.
This LLM GPU VRAM Calculator calculates complete hardware requirements for leading model architectures including LLaMA 3, Mistral, Mixtral Mixture of Experts, and DeepSeek Multi-Head Latent Attention (MLA). It computes memory footprints, KV cache scaling across long context windows, and tokens-per-second performance estimates.
For designing physical electrical systems and transformers, use our transformer calculator. To plan network topologies for GPU clusters, test the subnet calculator.
Especificaciones de ingeniería y detalles de ejecución
Model parameter calculations account for hidden dimension, number of layers, query attention heads, and key-value heads (Grouped-Query Attention).
Supports Mixture of Experts (MoE) architectures, calculating both total model parameters and active parameters per token.
Implements DeepSeek Multi-Head Latent Attention (MLA) formulas, quantifying dramatic KV cache compression via low-rank latent projections.
Computes KV cache size using formula: 2 * layers * contextLength * batchSize * kvHeads * headDim * bytesPerElement (standard MHA/GQA) and latent projection dimensions (MLA).
Quantization precision bytes-per-parameter mapping: FP32 (4.0B), FP16/BF16 (2.0B), FP8/INT8 (1.0B), NVFP4 (0.56B), INT4/Q4_K_M (0.55B), BitNet (0.27B).
Hardware roofline performance model calculates memory-bandwidth-bound generation speed: GPU Bandwidth (GB/s) / Model Weights (GB).
Privacidad de datos y aislamiento en memoria
All calculations run locally in your browser memory using client-side JavaScript math routines.
Zero proprietary model dimensions, hardware configurations, or prompt lengths are transmitted to external servers.
Immediate results with zero latency and full privacy for enterprise infrastructure planning.
Casos de uso comunes en producción
- AI engineers and machine learning teams sizing GPU instances before deploying vLLM, Ollama, or TensorRT-LLM production endpoints.
- Self-hosting enthusiasts determining whether a specific open-source model fits on home desktop GPUs like the RTX 3060, 4080, or 4090.
- Financial planners estimating annual cloud GPU cluster costs for serving high-concurrency LLM application backends.
Casos extremos y límites operativos
- Extreme context windows (128K+ tokens): At extended context lengths, KV cache memory frequently exceeds the size of the model weights themselves, demonstrating the necessity of KV quantization or MLA architectures.
- Mixture of Experts offloading: While all experts must reside in VRAM for fast generation, only active experts execute per token, reducing compute requirements while maintaining large memory capacity requirements.
Preguntas Frecuentes
How much VRAM do I need to run LLaMA 3 8B?
At standard 16-bit precision (FP16), LLaMA 3 8B requires roughly 16 GB for weights plus 2 to 4 GB for KV cache and context, fitting comfortably on a 24 GB RTX 4090. At 4-bit quantization (Q4_K_M), it requires only 6 to 8 GB of VRAM, running on common 8 GB or 12 GB consumer GPUs.
What is Grouped-Query Attention (GQA) and how does it reduce memory?
GQA shares key and value projection heads across multiple query heads (for example, an 8:1 ratio in LLaMA 3). This reduces the memory size of the KV cache by a factor of 8 compared to traditional multi-head attention.
How does DeepSeek MLA compress the KV cache?
Multi-Head Latent Attention compresses key and value representations into a compact low-rank latent vector before caching, cutting KV cache memory consumption by over 80% compared to standard GQA architectures.