Quantization Memory Savings Calculator
Compare model VRAM across fp32, fp16, int8 and int4.
With the default inputs, int4 needs 42 GB vs 168 GB at fp16 — Quantizing a 70B model cuts VRAM up to 75%. Enter your own numbers below to recompute instantly; the full step-by-step math is shown under the worked example.
Results update automatically as you type.
| fp32 | 4 bytes | 336 GB |
| fp16 | 2 bytes | 168 GB |
| int8 | 1 bytes | 84 GB |
| int4 | 0.5 bytes | 42 GB |
See how quantization shrinks a model's memory footprint. Compare VRAM at float32, float16, int8 and int4 for any parameter count.
How this is calculated
For each precision we compute VRAM = parameters (billions) × bytes per parameter × overhead, then compare fp32, fp16, int8 and int4 side by side. int4 uses 1/8 the weight memory of fp32 and 1/4 of fp16.
Is this a good result? What to do next
Quantization is the cheapest way to fit a bigger model on smaller hardware — often the difference between one GPU and several. Modern 8-bit and 4-bit methods keep most quality for many tasks, but always evaluate on your own workload.
Typical planning ranges
- int4 vs fp32
- 1/8 the weight memory
- int4 vs fp16
- 1/4
- Overhead
- KV cache/activations on top
Ranges are typical planning figures to sanity-check your result, not authoritative benchmarks. Your numbers will vary with use case, volume, and vendor.
How to improve this number
- Use int8/int4 to fit cheaper or fewer GPUs.
- Evaluate quality after quantizing on your tasks.
- Combine with smaller context to shrink KV cache.
Common mistakes
- Assuming quantization is always quality-neutral.
- Comparing only weights and ignoring KV/activation overhead.
When to use a different approach
For full VRAM and GPU count, use the GPU VRAM calculator. For speed effects, use the local LLM throughput calculator.
Worked example (defaults)
With the default inputs above, here is the result:
| fp32 | 4 bytes | 336 GB |
| fp16 | 2 bytes | 168 GB |
| int8 | 1 bytes | 84 GB |
| int4 | 0.5 bytes | 42 GB |
Frequently asked questions
How much does int4 save?+
int4 uses 1/8 the weight memory of float32 and 1/4 of float16 — often the difference between fitting on one GPU or several.
Does quantization reduce quality?+
Modern 8-bit and 4-bit methods keep most quality for many tasks, but always evaluate on your own workload.