Quantization

Match each vector-compression scheme to its core mechanism.

Match each GPU generation to the quantization format that lights up its native tensor cores.

Which quantization combo squeezes the most decode bandwidth per percent of quality lost?

Rank these chat-API cost levers from highest to lowest typical ROI

Match Hopper FP8 and Blackwell FP4 tensor cores to their throughput and bandwidth effects on decode

Weight quantization in plain terms: what changes and what stays the same?

Where does FP8 fit in modern LLM serving and which GPUs support it?

Match each 4-bit format to its niche in 2026 inference and fine-tuning.

Define activation quantization and explain how W8A8 differs from weight-only quant.

Merging a LoRA back into an NF4-quantized base hits one subtle problem: name it and explain why

FP4 vs NF4, pick the answer that captures the actual structural difference

INT8 KV cache vs INT8 weight quantization, which one is easier in production?

Name two recovery moves a serving stack can pull when KV memory runs short mid-request.

How does QLoRA fit a 65B fine-tune onto a single 48GB GPU?

Which statements about QLoRA are true?

Why does QLoRA use NF4 instead of uniform INT4?

Misconception: 'LoRA is just a quantization technique'

Which statements about FP8, INT8 and INT4 weight quantization are correct?

Match each weight-quantization regime to what it buys you and where it breaks

What does W8A8 vs W4A16 mean and which regime is appropriate for memory-bound decode?

Match each weight/activation quant regime to its mechanism and best-fit workload

Spot the errors in this tensor-core utilization claim

On the roofline, which intervention moves an LLM decode workload most directly toward higher peak throughput?

Match each post-training quantization method to what it protects against

When would you choose QAT over PTQ for inference quantization and what's the cost?