Sustained 70B-Class AWQ Inference on a Single NVIDIA L20: Throughput, Stability, Energy, and Quality Characterization

Abstract

Serving 70B-class open-weight language models is usually associated with 80GB accelerators, tensor-parallel multi-GPU systems, or vendor-managed inference profiles. This technical report evaluates whether a single NVIDIA L20 48GB GPU can sustain a useful 70B-class quantized serving workload. We measure Qwen2.5-72B-Instruct-AWQ served with vLLM 0.8.5.post1 and AWQ Marlin on one L20. Under a fixed workload of approximately 512 input tokens and 256 output tokens, a 24-hour concurrency-10 soak completed 36,740/36,740 requests with no request failures and no vLLM CUDA OOM, traceback, or killed-process signatures. The system sustained 108.84 output tokens/s, with p95 time-to-first-token of 6.61s and p95 end-to-end latency of 23.54s. GPU-board power sampled through nvidia-smi produced an estimated 7.92 kWh over the run, corresponding to 0.330 output tokens/J and 1.008 total tokens/J. Repeated fixed-shape runs at concurrency 1, 4, 8, and 16 completed 12/12 runs successfully; the concurrency-16 condition averaged 127.22 +/- 12.68 output tokens/s over three runs. The same AWQ endpoint also produced absolute quality scores of 0.8130 on MMLU, 0.8309 on CMMLU, and 0.8082 on GSM8K, plus 80/80 MT-Bench answer generations and a 60-item 8K LongBench subset. The evidence supports a narrow claim: a carefully configured single L20 can serve Qwen2.5-72B-Instruct-AWQ as a throughput-oriented 70B-class endpoint under the tested fixed-shape workload. It does not prove lossless AWQ quality retention, low-latency interactive serving, broad production SLA coverage, or equivalence to a BF16/FP16 baseline.