LLM VRAM Calculator
Calculate GPU VRAM requirements for running Large Language Models with different quantization levels. Supports popular models like Llama, Mistral, and Qwen.
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What is VRAM and why does it matter for LLMs?
VRAM (Video Random Access Memory) is the dedicated memory on your graphics card used to store data for GPU computations. When running Large Language Models (LLMs) locally, the entire model weights must fit into VRAM for efficient inference. Unlike system RAM, VRAM provides the high bandwidth needed for the parallel computations that make LLMs work.
Running out of VRAM forces the system to swap data between GPU memory and system RAM, dramatically slowing down text generation. In many cases, if a model doesn't fit in VRAM, it simply won't run at all. This makes calculating VRAM requirements essential before downloading or attempting to run any local LLM.
How is LLM VRAM calculated?
VRAM usage for LLMs consists of three main components:
Model weights: The core neural network parameters. A 7B parameter model at FP16 (16-bit) uses approximately 14 GB, while the same model quantized to 4-bit uses only ~4 GB.
KV Cache: During text generation, the model stores key-value pairs from previous tokens. This cache grows with context length and can consume several gigabytes for long conversations.
Overhead: CUDA kernels, activation tensors, and framework overhead typically add 10-15% to the base requirements.
The formula for model size is: (Parameters × Bits per weight) ÷ 8 = Size in bytes
Tool description
This calculator estimates the VRAM required to run a Large Language Model locally on your GPU. Enter your model's parameter count, select a quantization format, and specify your available VRAM to instantly see whether the model will fit and how much context length you can support.
The tool supports all common quantization formats from llama.cpp including GGUF Q2 through Q8 variants, as well as standard FP16 and FP32 precision. It also calculates the maximum context length your GPU can handle given its VRAM capacity.
Features
- 20+ quantization formats: Full support for GGUF quantization types (Q2_K through Q8_0), i-quants (IQ2-IQ4), and standard precisions (FP16, FP32, BF16)
- Popular model presets: Quick selection for common model sizes from 1B to 405B parameters including Llama 3, Mistral, Qwen, and Phi models
- GPU presets: Pre-configured VRAM amounts for popular consumer and professional GPUs from GTX 1650 to H100
- Context length calculation: Automatically computes the maximum context window your GPU can support
- Real-time results: Instant feedback as you adjust parameters
Use cases
Before downloading a model: Check if a model will run on your hardware before spending time downloading a 50+ GB file. Know in advance which quantization level you need to fit your GPU.
Optimizing inference settings: Find the sweet spot between model quality (higher quantization) and context length. Sometimes dropping from Q6 to Q4 lets you double your context window.
Planning GPU upgrades: Compare how different GPUs would handle your target models. See exactly how much VRAM you need to run Llama 70B or other large models comfortably.
Supported quantization formats
| Format | Bits/Weight | Best For |
|---|---|---|
| FP32 | 32.0 | Maximum precision, research |
| FP16/BF16 | 16.0 | Training, high-quality inference |
| Q8_0 | 8.5 | Near-lossless quality |
| Q6_K | 6.56 | High quality with good compression |
| Q5_K_M | 5.69 | Balanced quality and size |
| Q4_K_M | 4.85 | Popular choice for consumer GPUs |
| Q4_0 | 4.5 | Good compression, slight quality loss |
| Q3_K_M | 3.65 | Aggressive compression |
| Q2_K | 2.63 | Maximum compression, noticeable quality loss |
| IQ4_XS | 4.25 | Optimized 4-bit with importance weights |
| IQ3_XXS | 3.06 | Experimental ultra-low bit |
| IQ2_XXS | 2.06 | Extreme compression |
How it works
The calculator uses these formulas:
Model Size (GB) = (Parameters in billions × 10⁹ × bits per weight) ÷ 8 ÷ 10⁹
KV Cache (GB) ≈ (Parameters × Context Length ÷ 1000 × 0.5) ÷ 1000
Total VRAM = Model Size + KV Cache + 10% overhead
The KV cache formula is a simplified approximation. Actual KV cache size depends on model architecture (number of layers, attention heads, and head dimensions), but this estimate works well for most transformer-based LLMs.
Tips
- Start with Q4_K_M: This quantization offers the best balance of quality and size for most use cases
- Leave headroom: Aim for 1-2 GB of free VRAM to avoid out-of-memory errors during longer generations
- Consider context needs: If you need long context (8K+), you may need to use more aggressive quantization
- Multiple GPUs: For multi-GPU setups, you can often split models across cards, but this calculator assumes single-GPU usage
Limitations
- KV cache estimates are approximations based on typical transformer architectures
- Actual VRAM usage varies by inference framework (llama.cpp, vLLM, TensorRT-LLM)
- Does not account for batched inference or speculative decoding overhead
- Flash Attention and other optimizations can reduce actual requirements
- Some models have non-standard architectures that may use more or less memory
FAQ
Q: Why does my model use more VRAM than calculated? A: The calculator provides baseline estimates. Inference frameworks add their own overhead, and some operations require temporary buffers that increase peak usage.
Q: Can I run models larger than my VRAM using CPU offloading? A: Yes, tools like llama.cpp support partial GPU offloading, but performance drops significantly. This calculator focuses on full GPU inference.
Q: Which quantization should I use? A: For most users, Q4_K_M offers excellent quality with ~4.85 bits per weight. If you have VRAM to spare, Q5_K_M or Q6_K provide marginally better quality. Only use Q2/Q3 formats if absolutely necessary.
Q: How accurate are these estimates? A: Within 10-20% for most common models. Actual usage depends on the specific model architecture, inference backend, and runtime settings.