Quantization is a tradeoff, and the person asking this question already knows it. They are not asking whether to quantize; they are asking how to measure the cost of doing so on a model as capable as DeepSeek V3.2. That is the right instinct, because the hardest part of runtime quantization is not the math, it is proving that the degradation stays within acceptable bounds for your specific use case. Benchmarks are not a formality here; they are the entire argument for or against adoption.
The practical answer is to stop thinking in terms of a single score and start thinking in terms of task-level fidelity. General-purpose benchmarks like MMLU or HellaSwag will give you a coarse signal, but they will not tell you whether the quantized model fumbles code generation, reasoning chains, or long-context retrieval. For DeepSeek V3.2, which is a mixture-of-experts model, the risk is that quantization disproportionately affects the routing and expert selection mechanisms, not just the weights. So run benchmarks that stress those exact behaviors: code completion with execution checks, multi-step math with verifiable answers, and summarization with factual consistency scoring. Compare those against the unquantized baseline, not against published numbers, because your runtime environment and data distribution are unique.
What you are really measuring is not quality in the abstract; it is quality relative to your workflow. That is why the most useful benchmarks are the ones you build from your own data. Take a sample of the actual prompts your team will feed the model, run them through both versions, and have humans or a strong judge model rate the outputs blindly. This is more work than running a standard suite, but it is the only way to catch silent regressions, the kind where the model still sounds fluent but quietly drops a constraint or flips a number. Standard benchmarks will not catch that, because they are designed to reward pattern matching, not operational reliability.
The concrete next step is to establish a tolerance threshold before you run a single test. Decide now what percentage of quality loss you can accept, say, 2% on task-specific metrics, and design your evaluation to detect that difference with statistical confidence. Run each benchmark multiple times to account for sampling variance, and use paired comparisons where possible. If the quantized model stays within your threshold across your custom suite, you have your answer. If it does not, you have learned something more valuable than a pass or fail: you have learned exactly which capabilities suffer most, and you can decide whether those tradeoffs are worth the speed and memory gains. That is the only defensible way to move forward.