LLM Quantization Trade-Off Calculator
Compare VRAM requirements and quality trade-offs between quantization levels. Find the cheapest GPU that runs your model at your chosen precision.
- MMLU: −2.50 points
- HumanEval: −3.50 points
- Acceptable, ~5–7% quality drop
Quality estimates from published research (approximate). Always eval on your task. GPU prices verified 2026-08-19. · LLM RAM Calculator
How to Use
Choose your model
Select the open-weight model you want to quantize. VRAM requirements are calculated from parameter count.
Set baseline and target
Select your current precision (baseline) and the quantization level you're considering. FP16 is the standard baseline.
Review the trade-off
The calculator shows VRAM saved, approximate quality loss on MMLU and HumanEval benchmarks, and throughput improvement.
See GPU cost savings
Find the cheapest GPU that fits each quantization level and the monthly cost difference.
Understanding quantization quality loss
Benchmark numbers are approximations based on published research. Real-world quality loss depends heavily on your specific task — classification is nearly unaffected at Q4, while complex code generation degrades measurably. Always run your task-specific evals before committing to a lower quantization level in production.
How to Cut This Cost
Drop from FP16 to Q4_K_M — VRAM halves and throughput roughly doubles. Quality loss on most practical tasks is under 3%.
Use a smaller model at FP16 rather than a large model at Q3. Llama 3.3 70B Q4 usually outperforms Llama 3.1 405B at Q3 on coherence.
Use spot GPU instances — running a quantized model on a spot RTX 4090 ($0.74/hr) is often cheaper than on-demand A10G at FP16.
FAQ
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Last verified: 2026-08-20 · methodology · data sources