Benchmark Overview
Code completion tools demand sub-300ms latency for inline suggestions to feel responsive. Developers abandon completions that take longer than 500ms. We benchmarked coding-optimised models across GPU tiers, measuring both inline completion (10-30 tokens) and function generation (50-150 tokens) latency on dedicated GPU hosting.
Test Configuration
Models: DeepSeek-Coder-V2 16B (INT4), CodeLlama 34B (INT4), Qwen 2.5 Coder 32B (INT4), StarCoder2 15B (INT4). GPUs: RTX 5090, RTX 6000 Pro, RTX 6000 Pro 96 GB, RTX 6000 Pro. Served via vLLM with prefix caching enabled. Three scenarios: inline completion (15-token output, 500-token context), function generation (100-token output, 2K context), multi-file context (100-token output, 8K context). See token benchmarks for raw throughput.
Inline Completion Latency (15 Tokens)
| Model | RTX 5090 | RTX 6000 Pro | RTX 6000 Pro 96 GB | RTX 6000 Pro |
|---|---|---|---|---|
| DeepSeek-Coder-V2 16B | 142ms | 155ms | 118ms | 78ms |
| StarCoder2 15B | 135ms | 148ms | 112ms | 72ms |
| CodeLlama 34B | 285ms | 252ms | 195ms | 128ms |
| Qwen 2.5 Coder 32B | 268ms | 240ms | 188ms | 122ms |
Function Generation Latency (100 Tokens, 2K Context)
| Model | RTX 5090 | RTX 6000 Pro | RTX 6000 Pro 96 GB | RTX 6000 Pro |
|---|---|---|---|---|
| DeepSeek-Coder-V2 16B | 1,050ms | 1,150ms | 880ms | 560ms |
| StarCoder2 15B | 980ms | 1,080ms | 820ms | 520ms |
| CodeLlama 34B | 2,150ms | 1,880ms | 1,420ms | 910ms |
| Qwen 2.5 Coder 32B | 2,020ms | 1,780ms | 1,350ms | 870ms |
Context Length Impact
Increasing context from 500 tokens to 8K tokens adds 80-150ms to first-token latency depending on GPU and model size. The prefill stage (processing input context) scales linearly with context length. vLLM’s prefix caching eliminates this overhead for repeated contexts (e.g., the same file being edited). Enable prefix caching in the vLLM production setup. Compare engines at vLLM vs Ollama or use Ollama for simpler setups.
Multi-Developer Scaling
A single GPU serving inline completions handles 15-25 developers concurrently with 15B models before latency exceeds 300ms. For 34B models, this drops to 8-15 developers. A development team of 50 engineers needs 2-3 GPUs for responsive completions. See the GPU guide for capacity planning and LLM hosting for deployment strategies.
Recommendations
For inline completions under 300ms, deploy 15-16B models on RTX 5090 or better. For 32-34B models delivering higher code quality, RTX 6000 Pro or RTX 6000 Pro is required. Larger models produce better completions but must meet the latency budget. Deploy on GigaGPU dedicated servers with private hosting for code security. Visit the benchmarks section for more data.
