GPTQ Quantization Guide for RTX 5060 Ti 16GB

GPTQ remains widely available for models that predate AWQ adoption. On the RTX 5060 Ti 16GB at our hosting, GPTQ works well via ExLlama kernels but AWQ is usually preferred in 2026.

Contents

• What GPTQ is
• vs AWQ
• Serving
• When to prefer GPTQ

What GPTQ Is

GPTQ is a 4-bit post-training quantisation method published in 2022. Widely adopted before AWQ emerged. Still functional and served by most inference engines.

vs AWQ

In 2026 AWQ has overtaken GPTQ as the default quantised serving format for new models. GPTQ remains for back-compat.

Serving

python -m vllm.entrypoints.openai.api_server \
  --model TheBloke/Mistral-7B-Instruct-v0.3-GPTQ \
  --quantization gptq \
  --max-model-len 16384 \
  --gpu-memory-utilization 0.92

vLLM on Blackwell uses Marlin kernels for both AWQ and GPTQ where possible – similar performance. For very old GPTQ checkpoints (bits=3 or group_size=32) ExLlama kernel is used as fallback.

When to Prefer GPTQ

• AWQ checkpoint not available for your specific model
• Only GPTQ available in your fine-tune or derivative
• Legacy deployment where GPTQ is already in place

For new deployments: prefer FP8 > AWQ > GPTQ in that order.

See also: full format comparison, AWQ guide.