LLM VRAM Calculator
How much GPU memory do you need to run or fine-tune a model? Pick the size, precision, and what you're doing — get a VRAM estimate and a GPU that fits. Everything runs in your browser.
Estimate only — actual usage varies with batch size, sequence length, framework, and kernels. For models that use grouped-query or multi-query attention (most 70B+ open models), the KV-cache figure here is a deliberate over-estimate, so real inference memory is often lower.
Related guides
- LoRA & QLoRA fine-tuning guide — how the modes above actually differ
- LLM inference engines compared — once it fits, serve it efficiently
- Self-hosting LLMs vs cloud APIs — is buying the GPU worth it?
Related reading
Fine-Tune LLMs with LoRA and QLoRA: 2026 Guide
Learn to fine-tune LLMs with LoRA and QLoRA in 2026. VRAM requirements, dataset prep, Unsloth/Axolotl setup, hyperparameters, and evaluation.
Read →Unsloth on One GPU: Our Llama 3.1 8B Throughput, VRAM, and Quality Test
We benchmarked Unsloth against a conventional Hugging Face TRL QLoRA pipeline on one RTX 4090. Here are our measured training time, VRAM use, quality results, dependency failures, and deployment limits.
Read →From Notebook to Production SLA: Running vLLM on Kubernetes with the Production Stack
A practitioner's guide to moving vLLM from a single-GPU notebook demo to a Kubernetes deployment with measured p50/p95 latency under concurrency, grounded in the vLLM production-stack release and the 2026 GLM-5.2 production SLA architecture.
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