Efficient Memory Management for MoE LLMs
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On this page 4 sections
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Key Takeaways
- PagedWeight achieves FP16-equivalent accuracy while reducing GPU memory usage by up to 72.0% and increasing throughput by 1.94 times.
- The system uses a quality-aware runtime planner to adjust bitwidths based on KV cache pressure, routing statistics, and prompt-specific residuals.
- It implements a custom fused mixed-precision kernel to read weights directly from bit-plane and lookup table buffers, supporting variable bitwidths per linear-block.
- Compared to standard quantization methods, it improves output quality by up to 39.3% under similar memory constraints.
Summary & Methodology Analysis
PagedWeight addresses the memory competition between model weights and the KV cache (the key-value state of previous tokens stored for faster attention calculation) in Mixture-of-Experts (MoE) architectures, which use sparsely activated sub-networks to increase model capacity. The method treats Any-Precision LLM (APL) bit-plane and lookup table buffers as GPU-resident pages that can be reloaded or offloaded asynchronously to hide latency. The system relies on a runtime planner that performs greedy selection of weight quantization actions, balancing the target memory reduction against damage metrics derived from offline global sensitivity, online routing mass (how frequently an expert is used), and runtime prompt residuals (corrections for quantization error on specific inputs).
Interactive System Flowchart
Cross-Examination & FAQs
A deeper dive clarifying mechanics, constraints, and baseline evaluations.
Q1. What is the primary problem this paper solves?
It addresses the tension between GPU memory usage for model weights and the growth of the KV cache in MoE model serving scenarios.
Q2. How does PagedWeight improve performance?
It uses dynamic quantization and asynchronous page management to free GPU memory while maintaining accuracy, resulting in up to 72.0% memory savings and 1.94 times higher throughput.
Q3. Is the accuracy impacted by this quantization?
PagedWeight can achieve FP16-equivalent accuracy; it outperforms alternative quantization methods, improving quality by up to 39.3% at a similar memory budget.
Q4. What happens if routing statistics are ignored?
Removing routing statistics from the planner degrades perplexity to 7.26/10.13 on Wikitext2/C4 for the Qwen1.5-MoE-A2.7B model.
Q5. How does the system handle different bitwidths for different layers?
It uses a fused mixed-precision MoE kernel that reads bit-planes and lookup tables directly, allowing different bitwidths for each linear-block while fusing routing, expert activation, and output accumulation.
Q6. What datasets and models were used for evaluation?
The paper evaluated models including Qwen1.5-MoE-A2.7B, Mixtral-8x7B-v0.1, and Gemma-4-26B-A4B on benchmarks such as Wikitext2, C4, GSM8K, MATH-500, LongBench, Passage Retrieval, NarrativeQA, and QMSum.
Q7. Are there any performance trade-offs when using this method?
Yes, there is a minor throughput loss of at most 4.1% compared to a uniform quantization baseline.
Q8. What is the role of the runtime planner?
The planner identifies the target number of bytes to free and selects the cheapest actions based on damage per released byte, incorporating offline sensitivity, routing mass, and prompt residuals.
Q9. What are the limitations of the current implementation?
The system is currently tied to specific quantization formats and kernels, and further research is needed to explore new methods for estimating prompt-wise expert sensitivity.