Comparison: vLLM 0.6 vs. Text Generation Inference 1.4 for Serving Code LLMs
vLLM 0.6 and Text Generation Inference (TGI) 1.4 are compared for serving code LLMs, with vLLM offering higher throughput for 13B models and better VRAM efficiency for 34B models, while TGI achieves lower latency for smaller 1B models. Both frameworks support major code LLMs and offer similar quantization and deployment options, with TGI planning speculative decoding to improve performance. The benchmarks were conducted on AWS EC2 p4d.24xlarge instances using standardized workloads and model configurations.
- ▪vLLM 0.6 achieves 1420 tokens/sec throughput for 13B code LLMs on A100 80GB, 22% higher than TGI 1.4's 1160 tokens/sec.
- ▪TGI 1.4 reduces p99 latency for 1B code LLMs to 87ms, 18% lower than vLLM 0.6's 106ms on identical hardware.
- ▪vLLM 0.6's PagedAttention reduces VRAM waste by 41% for 34B code LLMs, cutting per-hour inference cost by $0.18 on AWS EC2.
- ▪TGI 1.4 will add native speculative decoding for code LLMs in Q3 2024, aiming to close the throughput gap with vLLM for small models.
- ▪Both vLLM 0.6 and TGI 1.4 support Hugging Face Transformers models including CodeLlama and StarCoder, with optimizations for specific architectures.
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