Technical Summary: Studying Quantization Trade-offs for Efficient Inference Deployment in Machine Translation

Problem Statement

Deploying large language models (LLMs) for machine translation (MT) in realistic server environments presents a dual challenge: maintaining high translation quality while minimizing latency and maximizing throughput. While quantization is a standard technique to reduce memory footprints and improve inference efficiency, its impact on end-to-end latency and throughput is rarely evaluated under controlled, orchestration-level workloads. Furthermore, standard MT benchmarks typically rely on isolated sentences, failing to capture the dynamics of long-context document translation. This paper investigates the trade-offs between quantization formats, text chunking strategies, and translation quality across varying model sizes and hardware constraints.

Methodology

Models and Hardware

The study evaluates two MT model families: EuroLLM (Martins et al., 2025) and Hy-MT2 (Zheng et al., 2026). Five models ranging from 1.7B to 22B parameters were tested on single A100 and H100 GPUs.

• EuroLLM variants: 1.7B (4K context), 9B, and 22B (32K context).
• Hy-MT2 variants: 1.8B and 7B (262K context).

Quantization Formats

Three quantization combinations were analyzed:

1. W8A8: 8-bit weights and 8-bit activations.
2. W4A8: 4-bit weights and 8-bit activations.
3. W4A16: 4-bit weights with 16-bit activations (weight-only).
   Post-training quantization (PTQ) was performed using GPTQ (Frantar et al., 2022), with SmoothQuant (Xiao et al., 2023) applied prior to quantization for W8A8 and W4A8 to mitigate activation outliers.

Evaluation Framework

The study employed a two-stage evaluation approach using the vLLM serving engine:

1. Offline Benchmarking: Measured throughput and wall time for fixed sequence lengths and batch sizes (up to 1024) to isolate hardware kernel performance and memory bandwidth effects.
2. Closed-Loop Online Benchmarking: Simulated a production environment with up to 500 concurrent users processing documents of ~1200 tokens. This stage varied document chunk sizes (50–400 tokens) to measure end-to-end (E2E) latency and throughput Pareto curves.

Translation Quality Metrics

• Segment-Level: Evaluated using xCOMET on standard WMT24++ benchmarks (EN↔DE, RU↔EN).
• Document-Level: Introduced a document-level evaluation where source segments were greedily concatenated to meet target prompt thresholds (up to 800+ tokens). Outputs were reconstructed and evaluated using chrF++ to assess quality retention over long contexts, addressing the limitations of segment-level metrics.

Key Contributions

1. Quantization and Chunking Synergy: The authors demonstrate that combining a document-chunking strategy (specifically 200–400 tokens) with W4A8 or W8A8 quantization significantly improves the latency-throughput Pareto curve across most medium-sized document workloads.
2. Identification of Benchmark Blind Spots: The paper identifies that standard segment-level MT benchmarks fail to predict the interaction between quantization and long-context document translation. These benchmarks often underestimate quality degradation in quantized models when context length increases.
3. Model-Specific Robustness: The study reveals a stark divergence in robustness between model families. While Hy-MT2 models remain robust under quantization (maintaining performance close to the BF16 baseline), EuroLLM models exhibit strong sensitivity, with translation quality collapsing rapidly as context length increases under quantization.

Results

Inference Efficiency

• Small Models (<2B): Quantization offered marginal benefits or even slight slowdowns compared to the BF16 baseline, primarily due to the overhead of dynamic activation quantization.
• Medium to Large Models (9B–22B): Quantization provided substantial efficiency gains.
  • On A100 GPUs, W8A8 generally provided the best performance, as the lower compute-to-memory-bandwidth ratio causes inference to become compute-bound sooner, making activation quantization critical.
  • On H100 GPUs, W4A8 often outperformed other formats due to higher compute capabilities.
• Chunking Strategy: For high concurrency, a chunk length of 200–400 tokens was found to be Pareto-optimal, balancing parallel processing benefits against the overhead of increased request scheduling.

Translation Quality

• Hy-MT2: Quantized checkpoints followed the BF16 baseline closely, showing negligible degradation in chrF++ scores even with longer context thresholds.
• EuroLLM: Quantized checkpoints showed drastic degradation. For example, on the EuroLLM-9B model, while xCOMET scores on short segments suggested minor degradation, the chrF++ scores on long documents (800 tokens) collapsed by up to 62% compared to the BF16 baseline. Failure modes included content omissions, source copying, and degenerate repetitions.
• Context Sensitivity: The degradation in EuroLLM was attributed to its training data (instruction-tuned on segment-level pairs) and context length limitations, contrasting with Hy-MT2's training on long-context data.

Significance and Claims

The paper argues that the trade-off between inference efficiency and translation quality is not determined solely by the quantization format but is heavily dependent on the choice of text chunking strategy and the underlying model architecture.

The authors claim that relying on standard segment-level benchmarks can lead to misleading conclusions about the deployability of quantized MT models, particularly for long-document translation. They posit that for efficient deployment, practitioners must jointly optimize quantization and chunking strategies. Furthermore, the results suggest that model families trained on long-context data (like Hy-MT2) are inherently more robust to quantization in document-level settings than those trained primarily on sentence-level pairs (like EuroLLM), highlighting a critical consideration for model selection in production environments.

The study concludes that while quantization is a powerful tool for efficiency, its application requires careful validation against document-level metrics to avoid severe quality collapse in long-context scenarios.