Model Quantization Advisor

AdvancedopsMinimum 32K context

Advises on quantizing open-weight models for cheaper, faster inference while keeping quality acceptable. Compares formats and precisions (GGUF, AWQ, GPTQ, FP8, INT8, 4-bit), estimates memory footprint and throughput on target hardware, flags which workloads degrade most under quantization, and defines the evaluation needed to confirm the tradeoff is safe.

quantizationinferenceoptimizationopen-weightsgpuself-hosted

Use cases

  • Choosing a quantization format for a target GPU
  • Estimating VRAM footprint and throughput before deploying
  • Deciding how much quality loss is acceptable per workload
  • Designing an eval to verify a quantized model is still good enough

Example prompt

I want to self-host a 70B open-weight model for an internal coding assistant on 2x A100 80GB.

Recommend a quantization approach: which format and precision, expected VRAM use and tokens per
second, and where quality is most likely to drop (long context, code correctness, tool calling).
Define the evaluation I should run before and after to confirm the tradeoff is acceptable.

Recommended models

claude-opus-5gemini-3-6-flashgpt-5-6-sol

Compatible tools

claude-codecursorkiroany

Modalities

Input: text, code
→
Output: text, code

Related Skills

gpu-capacity-plannerinference-latency-optimizerllm-cost-optimizer

Author

OpenModels Community

@openmodelsrun