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AI VRAM Calculator — Estimate GPU Memory for LLMs (Gemma, Llama, Mistral) [2026]

Calculate VRAM needed to run AI models locally. Select model size, quantization (FP16, INT8, INT4, GGUF Q4-Q8) and see GPU memory required with recommended GPUs. Supports Gemma 4, Llama 3, Mistral, Qwen. Free calculator.

Gemma 4

Llama 3

Mistral

Qwen

Estimated Total VRAM Required

68.9 GB

31B params · FP16 / BF16 · 4K context

Model Weights

57.7 GB

KV Cache

2.2 GB

Overhead (15%)

9.0 GB

GPU Compatibility

GPUVRAMStatus
RTX 40608 GBNot enough
RTX 4060 Ti16 GBNot enough
RTX 4070 Ti Super16 GBNot enough
RTX 408016 GBNot enough
RTX 409024 GBNot enough
RTX 509032 GBNot enough
A600048 GBNot enough
A10080 GBTight fit
H10080 GBTight fit

Estimates include a 15% overhead buffer. Actual usage may vary by framework (llama.cpp, vLLM, Transformers) and batch size.

What is AI VRAM Calculator?

Running large language models (LLMs) locally has become increasingly practical with the release of open-source models like Google Gemma 4, Meta Llama 3, Mistral, and Qwen. The key limiting factor is GPU VRAM — if you don't have enough video memory, the model simply won't load. VRAM requirements depend on three main factors: the number of model parameters (measured in billions), the quantization level (how many bytes each parameter uses), and the context length (which determines KV cache size). A 7B parameter model at FP16 needs roughly 14 GB of VRAM, but the same model quantized to INT4 fits in just 3.5 GB. This calculator helps you estimate VRAM requirements before downloading or deploying a model. Whether you're checking if your current GPU can handle Gemma 4 31B Dense, or planning a hardware upgrade for a 70B model, this tool gives you the numbers you need.

How to Use AI VRAM Calculator

Select a model preset (Gemma 4 E2B/E4B/26B MoE/31B Dense, Llama 3, Mistral, Qwen, and more) or enter a custom parameter count. Choose a quantization level such as FP16, INT8, INT4, or a GGUF variant. Adjust the context length if needed. The calculator instantly shows the estimated VRAM breakdown (model weights, KV cache, overhead) and a GPU compatibility table showing which GPUs can run your configuration.

How AI VRAM Calculator Works

The calculator uses the standard formula for estimating LLM VRAM requirements: **Total VRAM = Model Weights + KV Cache + Overhead** 1. **Model Weights:** Parameters (in billions) multiplied by bytes per parameter based on quantization: FP32 = 4 bytes, FP16/BF16 = 2 bytes, INT8 = 1 byte, INT4 = 0.5 bytes. GGUF quantization levels fall between these: Q4_K_M ≈ 0.56, Q5_K_M ≈ 0.69, Q6_K ≈ 0.81, Q8_0 ≈ 1.0 bytes per parameter. 2. **KV Cache:** Scales with model size and context length. Estimated as (params / 7) × (context / 4096) × 0.5 GB. Longer contexts need proportionally more cache memory. 3. **Overhead:** A 15% buffer added for framework runtime, CUDA kernels, and memory fragmentation. For Mixture-of-Experts (MoE) models like Gemma 4 26B MoE, the full parameter count is used for weights (all experts must be in memory), but active computation uses fewer parameters.

Common Use Cases

  • Checking if your current GPU (RTX 4060, 4070, 4090, etc.) can run a specific model before downloading it
  • Comparing VRAM requirements across different quantization levels to find the best quality-to-memory tradeoff
  • Planning GPU purchases or upgrades for local AI inference workloads
  • Estimating VRAM for Gemma 4 variants (E2B for mobile, E4B for edge, 26B MoE, 31B Dense)
  • Understanding how context length affects memory usage when processing long documents
  • Evaluating whether to use FP16 for maximum quality or GGUF quantization for fitting larger models

Frequently Asked Questions

How much VRAM do I need for Gemma 4?▼

It depends on the variant and quantization. Gemma 4 E2B (2B params) at INT4 needs about 1.5 GB — perfect for phones. E4B (4B) at INT4 needs about 3 GB. The 26B MoE at Q4_K_M needs about 17 GB (fits an RTX 4090). The 31B Dense at FP16 needs about 68 GB (needs an A100 or multi-GPU setup), but at Q4_K_M it fits in about 20 GB.

What is quantization and how does it reduce VRAM?▼

Quantization reduces the precision of model weights from 16-bit or 32-bit floating point to lower-bit representations like 8-bit or 4-bit integers. This directly reduces memory usage (FP16 uses 2 bytes per parameter, INT4 uses only 0.5 bytes) with a modest quality tradeoff. Modern quantization methods like GGUF Q4_K_M preserve most of the model quality while cutting memory usage by 75%.

What is the difference between GGUF quantization levels?▼

GGUF (GPT-Generated Unified Format) offers several quantization levels. Q4_K_M (0.56 bytes/param) is the most popular balance of size and quality. Q5_K_M (0.69 bytes) offers better quality at moderate size increase. Q6_K (0.81 bytes) is near-lossless. Q8_0 (1.0 bytes) is equivalent to INT8 precision. Lower numbers mean smaller files but slightly lower quality.

How does context length affect VRAM usage?▼

Longer context windows require more KV (Key-Value) cache memory. At 4096 tokens context, KV cache is relatively small. At 32K tokens, it can add several GB. At 128K+ tokens, KV cache can exceed the model weights themselves. If you need long context, consider using models with GQA (Grouped Query Attention) which reduces KV cache size.

Can I split a model across multiple GPUs?▼

Yes — tools like llama.cpp, vLLM, and Hugging Face Accelerate support tensor parallelism across multiple GPUs. For example, a 70B FP16 model (~140 GB) can run on two A100-80GB GPUs. However, multi-GPU setups add communication overhead and require GPUs connected via NVLink or PCIe for good performance.

What is MoE and how does it affect VRAM?▼

Mixture-of-Experts (MoE) models like Gemma 4 26B MoE and Mistral 8x7B have many parameters but only activate a subset (experts) for each token. The full model must be in VRAM (all 26B or 46.7B params), but inference is faster because only a fraction is computed per token. MoE models offer better performance-per-VRAM than dense models of similar quality.

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