LLM deployment planning
LLM GPU RAM Calculator
Estimate how much GPU memory an LLM needs from model size, numerical precision, and runtime overhead. Use it for quick VRAM planning before choosing hardware.
Model memory inputs
Overhead covers inference buffers and runtime needs. KV cache and batch size can add more memory in production.
Estimated VRAM
Formula: parameters x bytes per parameter x overhead. This is a planning estimate, not a replacement for benchmarking a specific inference stack.
What is an LLM RAM calculator?
An LLM RAM calculator estimates how much GPU memory is needed to load and serve a large language model. The most important inputs are parameter count and numerical precision, because model weights usually dominate the baseline memory requirement. Production serving then adds overhead for runtime buffers, attention state, the key-value cache, batching, and framework allocation.
Hardware selection
Estimate whether a model can fit on one GPU or needs a smaller precision, quantization, or multiple GPUs.
Serving tradeoffs
Compare FP16, INT8, and INT4 memory plans before choosing an inference runtime or model variant.
Context planning
Account for the extra memory pressure that comes from long context, concurrent requests, and KV cache growth.
LLM memory planning notes
Precision matters
FP16 generally uses half the memory of FP32. INT8 and INT4 can reduce memory further with quantization tradeoffs.
Context adds memory
Long context windows increase KV cache usage, especially with large batch sizes or concurrent requests.
Production varies
Framework, attention kernel, GPU allocation, and serving settings can shift real VRAM requirements.
How the GPU memory estimate works
A basic LLM memory estimate starts with model weights: parameters multiplied by bytes per parameter. Runtime overhead is then added to account for inference buffers, framework allocation, and serving needs. This gives a fast planning number before you benchmark a specific model, GPU, and inference stack.
Weight memory
A 70B parameter model at FP16 uses about 140 GB for weights before overhead because each parameter uses 2 bytes.
Overhead memory
Overhead varies by runtime, attention implementation, batch size, context length, and key-value cache behavior.
Quantized serving
INT8, FP8, and INT4 can reduce memory requirements, but quality, throughput, and hardware support should be checked.
Capacity planning
For production, leave headroom for concurrent requests, monitoring, framework reserves, and model warm-up behavior.
Precision examples
For model weights only, memory scales linearly with parameter count and bytes per parameter. Runtime overhead and KV cache are not included in the examples below.
| Model size | FP16 / BF16 | INT8 / FP8 | INT4 |
|---|---|---|---|
| 7B | 14 GB | 7 GB | 3.5 GB |
| 13B | 26 GB | 13 GB | 6.5 GB |
| 70B | 140 GB | 70 GB | 35 GB |
Technical references
These references explain common production factors behind LLM memory planning, including quantization, paged attention, and KV cache behavior.