Architecting a Zero-Latency Anycast Network for LLM Token Routing

Quantizing model weights from 16-bit to 4-bit precision roughly quadruples the number of model replicas that fit on a given GPU fleet, which is an enormous cost lever if quality holds. The risk is that quality degradation from quantization is rarely uniform: it tends to concentrate in specific task types, particularly multi-step reasoning and precise numerical output, while leaving simpler tasks like classification largely unaffected.

Methodology: Workload-Stratified Benchmarking

Rather than benchmarking against a single general-purpose evaluation set, we stratified our production traffic into six task categories, classification, summarization, structured extraction, multi-step reasoning, code generation, and open-ended generation, and ran identical prompts from each category against full-precision, GPTQ 4-bit, and AWQ 4-bit variants of the same base model.

Where Quantization Held Up

For classification and structured extraction, both GPTQ and AWQ 4-bit variants matched full-precision output on over 98% of sampled requests, with disagreements concentrated in genuinely ambiguous edge cases rather than systematic degradation. Summarization quality, scored via a reference-based ROUGE comparison, showed less than a 1.5-point degradation across both quantization methods.

Where It Did Not: Multi-Step Reasoning

Multi-step reasoning tasks, particularly those requiring the model to carry intermediate numerical state across several steps, showed measurable degradation under GPTQ specifically, with a 6.8% increase in final-answer error rate compared to full precision. AWQ performed meaningfully better on this category, at a 2.1% error-rate increase, which we attribute to AWQ's activation-aware weight selection preserving precision on the weights that most influence downstream activations, exactly the weights that matter most when errors compound across reasoning steps.

quantization:
  method: awq
  bits: 4
  group_size: 128
  zero_point: true
  routing:
    classification: quantized
    structured_extraction: quantized
    multi_step_reasoning: full_precision
    code_generation: full_precision

quantization:
  method: awq
  bits: 4
  group_size: 128
  zero_point: true
  routing:
    classification: quantized
    structured_extraction: quantized
    multi_step_reasoning: full_precision
    code_generation: full_precision

quantization:
  method: awq
  bits: 4
  group_size: 128
  zero_point: true
  routing:
    classification: quantized
    structured_extraction: quantized
    multi_step_reasoning: full_precision
    code_generation: full_precision

Production Routing Decision

Based on these results, we route classification, structured extraction, summarization, and open-ended generation through AWQ 4-bit replicas by default, while multi-step reasoning and code generation continue to route to full-precision replicas. This stratified approach captured roughly 70% of the cost benefit of full quantization while avoiding the quality regression on the task types where it would have mattered most.