What Is a Normal GPU Utilization Rate: Metrics and Benchmarks for Inference Scenarios

GPU utilization has no unified benchmark value in inference scenarios, and directly comparing numbers often leads to misleading conclusions. The reasonable range depends on concurrency levels, batching strategies, and KV Cache memory access efficiency, rather than purely computational saturation. This article breaks down the differences in utilization metrics across inference scenarios and provides a reusable evaluation framework.

Why GPU Utilization Is Difficult to Compare Horizontally in Inference Scenarios

GPU utilization metrics in training scenarios are relatively mature—large-scale matrix multiplication can continuously saturate computational capacity. However, the memory-access-intensive nature of inference workloads changes this dynamic. According to FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness, attention computation is limited by HBM bandwidth rather than compute capacity, and IO-aware optimization is the primary source of gains. This means that in inference, GPUs spend a significant amount of time waiting for data movement rather than executing computations.

In Mingxin FX100's measured KV hierarchical acceleration with a 480B production deployment, throughput improvements fell within the +29–40% range (measured, reports R2/R3). The mechanism involves offloading KV Cache from HBM to an NVMe-oF array to alleviate the memory bandwidth bottleneck. This data point demonstrates that the utilization bottleneck in inference scenarios often lies not in GPU cores but in the data path.

Additionally, utilization values fluctuate significantly depending on the statistical metric used. Time-averaged utilization masks burst characteristics, while SM occupancy-based statistics ignore memory access latency. According to Efficient Memory Management for Large Language Model Serving with PagedAttention, KV Cache paging management directly improves memory fragmentation and utilization—but the throughput improvement metrics in that paper differ from Mingxin's measured storage-side optimizations, and the two cannot be directly converted.

Key Variables in Inference Utilization: Concurrency, Batching, and TTFT Constraints

GPU utilization in inference scenarios is primarily determined by three variables, which interact with one another:

Mingxin's measured results in report R2 show that with a 480B model at TP8 across three concurrency levels, TTFT p50 decreased from 10.17–35.73s to 7.53–26.35s (a reduction of 26–32%). Lower TTFT means higher concurrency can be accommodated under the same SLA, thereby increasing batch size and utilization. In report R3, full-machine throughput at TP4×2 improved by 35–36%, confirming the impact of concurrency patterns on overall efficiency.

It is worth emphasizing that utilization and TTFT involve a direct trade-off. If the SLA requires TTFT below a certain threshold, batch size is constrained and the GPU cannot reach full load. Conversely, relaxing TTFT can improve throughput and utilization but at the expense of user experience. According to Mooncake: A KVCache-centric Disaggregated Architecture for LLM Serving, a KVCache-centric disaggregated architecture is specifically designed to decouple this trade-off.

How to Build a Reproducible Utilization Evaluation Framework

For procurement and operations decision-makers, the following steps are recommended to establish an organization-specific utilization baseline:

1. Fix the workload profile: Define model size, context length, concurrency levels, and TTFT SLA. Mingxin's measured results in reports R2/R3 both use a 480B MoE model (weights approximately 450GB) with fixed concurrency levels to ensure data comparability.
2. Distinguish compute utilization from memory-access utilization: The former is measured by SM occupancy, while the latter is measured by HBM/storage bandwidth. According to NVIDIA GPUDirect Storage documentation, GPU direct storage can bypass CPU bounce buffers, but applicability is limited—the data path must be verified as truly connected.
3. Calibrate with end-to-end metrics: Throughput (tok/s) and TTFT carry more decision value than raw utilization. In Mingxin's measured results in report R2, the recomputation baseline TTFT p50 was 149.5s (conc16), which FX100 reduced to 11.85s, with throughput improving from 4.1 to 74.9 tok/s—these end-to-end numbers are more instructive for capacity planning than a "85% utilization" figure.

According to Epoch AI's public research, AI compute scale and costs show a continuous growth trend, but that database does not provide specific values for direct citation and serves only for trend assessment. For public cloud inference costs billed by the hour, AWS EC2 On-Demand pricing pages can be referenced, but specific prices change frequently and should not be written into documentation.

Conclusion

GPU utilization in inference scenarios is not a single number but the combined result of concurrency, batching, memory-access paths, and SLA constraints. It is recommended to prioritize end-to-end metrics (throughput, TTFT) and treat utilization as a supplementary diagnostic signal. Mingxin offers approximately 10-week gated joint testing, which can measure TTFT reduction and throughput improvement within the G3 main gate, helping establish a reproducible capacity model.

Key Q&A

Q: What is a normal GPU utilization rate in inference scenarios? A: There is no unified benchmark. The reasonable range depends on concurrency levels, batching strategies, and KV Cache memory access efficiency; directly comparing numbers can easily lead to distortion.

Q: Why is inference utilization lower than training? A: Inference is limited by HBM bandwidth rather than compute capacity (per the FlashAttention paper), with GPUs spending significant time waiting for data movement. KV Cache hierarchical acceleration can alleviate this bottleneck; Mingxin's measured throughput improvement is 29–40% (measured, reports R2/R3).

Q: How to evaluate whether inference GPU utilization is healthy? A: Fix the workload profile and SLA, distinguish compute utilization from memory-access utilization, and use throughput (tok/s) and TTFT as end-to-end calibration metrics rather than relying solely on SM occupancy.

References

1. MLPerf Inference: Datacenter Benchmark Suite Results — https://mlcommons.org/benchmarks/inference-datacenter/
2. Epoch AI — https://epoch.ai/
3. FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness — https://arxiv.org/abs/2205.14135
4. Mooncake: A KVCache-centric Disaggregated Architecture for LLM Serving — https://arxiv.org/abs/2407.00079
5. Efficient Memory Management for Large Language Model Serving with PagedAttention — https://arxiv.org/abs/2309.06180
6. NVIDIA GPUDirect Storage Documentation — https://docs.nvidia.com/gpudirect-storage/index.html
7. PyTorch Documentation — https://pytorch.org/docs/stable/index.html
8. EC2 On-Demand Instance Pricing — https://aws.amazon.com/ec2/pricing/on-demand/