Low-Bit N-Gram Embeddings for Language Models

Stephen Chung, Wenyu Du1 min readarXivOct 22, 2026

Key Takeaway for Students

Many recent open-weight language models use n-gram embedding tables to improve performance. These tables store learned representations of short token sequences, such as pairs or triples of consecutive tokens, which the model retrieves during processing. However, they can require substantial memory: DeepSeek-V4.1-Flash, for example, allocates 196 billion parameters to its Engram

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Reducing the memory footprint of n-gram embeddings is crucial for deploying large language models more efficiently, especially on resource-constrained hardware. This research directly addresses a significant cost component in modern LLMs, making them more accessible and scalable.

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Career Insight

high demand

Why it matters

Skills to learn

Implement various quantization schemes (e.g., 4-bit, 8-bit) for model parametersAnalyze memory usage of different model components (e.g., embedding tables, attention weights)Evaluate trade-offs between model performance and memory reduction from quantizationUtilize quantization-aware training or post-training quantization techniquesBenchmark inference speed and memory footprint of quantized models

Interview questions

Explain the concept of n-gram embeddings in language models and why they consume significant memory. How does low-bit quantization address this?
Describe different quantization methods (e.g., QAT, PTQ, integer-only) and their respective advantages and disadvantages when applied to embedding tables.
Design a system for serving a large language model with low-bit n-gram embeddings, considering memory constraints, inference latency, and maintaining model accuracy. What are the key components and potential bottlenecks?

Relevant for ~20 months · generated by the enrichment pipeline