AI Hardware Calculator
Size your local infrastructure.
VRAM Calculator for Local AI Models
What hardware do you need to run LLMs like Qwen, Llama, or DeepSeek locally? This calculator compares Apple Silicon and NVIDIA setups head-to-head and shows whether your target model fits a given config, what speed to expect, and where the limits are.
Built for developers, privacy-conscious users, and anyone setting up local AI inference without cloud dependencies.
What do you want to run locally?
Select everything that applies.
FAQ about the AI Hardware Calculator
How accurate is the recommendation?
It is a strong first sizing estimate based on your inputs. Before purchase, validate with an architecture review and realistic workload tests.
Is this suitable for GDPR-sensitive data?
Yes, the calculator is designed for local/on-prem deployment scenarios. Production setups still require proper access control, logging, and security policies.
Do I need multiple GPUs immediately?
Not always. Many teams start with a smaller setup and scale when concurrency or latency requirements increase.
Can I combine cloud and on-prem?
Yes. A hybrid approach is often effective: sensitive workflows on-prem, non-critical workloads optionally in the cloud.
How we calculate
VRAM = model weights (at chosen quantization) + KV cache × context length × concurrent users. A 7B model at Q4 uses ~4 GB; longer contexts add 0.5–16 GB per user.
Speed: each quantization level scales relative to Q4 — Q5 ≈ 92%, Q8 ≈ 78%, FP16 ≈ 62% of Q4 speed for LLMs. Video models scale inversely (higher precision = more seconds per clip).
Energy = TDP (W) × usage hours × 30 days ÷ 1,000 × electricity rate. The rate comes from your selected country.
TCO (Total Cost of Ownership) = hardware cost ÷ 3-year depreciation + annual energy cost.