LLM Quantization Memory Savings Calculator
This free LLM quantization memory savings calculator shows exactly how many gigabytes and how many GPUs a precision reduction saves, and it is built for infrastructure engineers deciding whether FP8 or INT4 quantization is worth the accuracy tradeoff. Enter model size, baseline precision, and target precision, and the tool returns weight memory at both precisions, the percentage saved, and the GPU count difference. Quantization is usually the single highest-leverage decision in an on-prem deployment: it can turn a four-GPU requirement into a one-GPU requirement for the same model.
Your numbers
Total parameters in billions. For mixture-of-experts models, use total parameters for storage sizing.
The precision you would deploy at without any quantization work.
FP8 is near-lossless on most tasks; INT4 methods like AWQ and GPTQ trade a small accuracy cost for a 4x footprint cut.
Usable memory per GPU in the target deployment class.
Extra memory beyond raw weights needed for KV cache, activations, and CUDA context at moderate concurrency.
Your results
Estimates only. Actual memory use depends on serving engine overhead, KV cache configuration, and calibration quality. Validate quantized model accuracy against your own evaluation set before production use.
Get your full quantization comparison report
We will email you a personalized memory and GPU count breakdown across FP16, FP8, and INT4, and a Netray AI specialist will follow up with an accuracy validation plan.
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How the memory math works
Weight memory is simply parameter count multiplied by bytes per parameter, so a 70B model needs 140 GB at FP16 and just 35 GB at INT4, a 75% reduction. Runtime overhead for KV cache, activations, and CUDA context adds on top of raw weights, commonly 15-30% at moderate concurrency and considerably more at high concurrency or long context. The GPU count figures divide total memory demand by usable VRAM per card and round up, which is what actually determines your hardware order, not the raw weight figure alone.
- FP8 halves memory versus FP16 with typically under 1% accuracy loss on standard benchmarks.
- AWQ and GPTQ INT4 quarter memory versus FP16 with typically 1-3% accuracy loss, concentrated in reasoning-heavy tasks.
- Aggressive INT3 quantization saves further memory but accuracy loss becomes task-dependent and needs real evaluation.
- Runtime overhead scales with context length and concurrency, so long-context or high-traffic deployments need a higher overhead assumption.
Choosing a target precision
FP8 is close to a free lunch on current-generation hardware with native FP8 tensor cores, and most teams should default to it unless VRAM is genuinely the binding constraint. INT4 methods like AWQ and GPTQ deliver the biggest memory win but require calibration against a representative dataset, and quality degradation is not uniform: extraction and summarization hold up well, multi-step reasoning and precise numerical work degrade more. Never assume a published accuracy number transfers to your domain; run your own evaluation set before committing to INT4 in production.
- Start with FP8 for near-lossless savings on H100/H200/B200-class hardware with native FP8 support.
- Move to INT4 only after validating accuracy on your own golden question set, not a public benchmark.
- Reasoning-heavy and code-generation workloads are more sensitive to aggressive quantization than extraction tasks.
Translating GPU savings into budget
The GPU count reduction this tool returns is the number that actually moves a procurement decision. Dropping from four H100s to one for a 70B model is not a marginal optimization, it is the difference between a $120,000 hardware line and a $30,000 one, before power and cooling. It also changes the deployment topology entirely: a single-GPU model needs no tensor parallelism, which removes interconnect overhead and simplifies the serving stack considerably.
How Netray handles quantization for production deployments
Netray quantizes and validates open-weight models for manufacturers and defense contractors deploying on-prem, calibrating against representative production data rather than generic benchmark sets. We run before-and-after evaluation on your actual use cases so the accuracy tradeoff is a measured decision, not a guess, and we size the resulting hardware order against real throughput and concurrency targets. Engagements typically start with a quantization feasibility study on your candidate models.
Frequently Asked Questions
Is FP8 quantization actually lossless?
Not perfectly lossless, but close on most tasks. Modern FP8 quantization typically costs under 1% on standard benchmarks because the format retains more numerical range than INT8 at the same bit width. It requires hardware with native FP8 tensor core support, which H100, H200, and B200-class GPUs provide. For most enterprise deployments FP8 is the default choice unless VRAM constraints force a more aggressive precision.
How much accuracy does INT4 quantization actually cost?
Published figures typically show 1-3% degradation on standard benchmarks for well-calibrated AWQ or GPTQ INT4 models, but that average hides real variance by task. Extraction, classification, and summarization usually hold up well. Multi-step reasoning, precise arithmetic, and long-context recall degrade more, sometimes noticeably. Always test INT4 candidates against your own golden question set rather than trusting an aggregate benchmark score.
Does quantization affect inference speed as well as memory?
Yes, and often more than teams expect. Decode speed on modern GPUs is bound by memory bandwidth, so reading a quarter as many bytes per token roughly quadruples achievable throughput for INT4 versus FP16, assuming the serving engine has efficient dequantization kernels. This is frequently a bigger practical win than the memory savings alone, since it also raises how many concurrent users one GPU can serve.
Should I quantize during fine-tuning or after?
For most teams, after. Fine-tune in FP16 or BF16 for training stability, then quantize the final checkpoint for serving using a calibration dataset representative of production traffic. QLoRA is a different case, it trains through a quantized base model directly to cut training memory, but the resulting adapter still needs its own post-training evaluation before you trust the quantized serving configuration.
Get a validated quantization plan with before-and-after accuracy scores on your own evaluation set.
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