Recently I did a little performance test of several LLMs on PC with 16GB VRAM
A viral benchmark shows how four leading open-source models handle increasing context lengths on consumer hardware.
A viral performance test conducted by a developer on Reddit has provided valuable real-world data for users running large language models on consumer hardware. The benchmark compared four prominent open-source models—Qwen 2.5 7B, Gemma 2 9B, Nemotron Cascade 2, and GLM 4.7 Flash—on a system equipped with an NVIDIA RTX 4080 GPU featuring 16GB of VRAM. Using the llama.cpp inference engine with carefully selected quantizations (like Q4_K_M), the test was designed to see how each model's generation speed, measured in tokens per second, degrades as the input context length increases, simulating real-world usage from short queries to lengthy documents.
The results, shared in a detailed comparison table, revealed clear performance leaders. Qwen 2.5 7B consistently delivered the highest tokens-per-second rate across varying context lengths, demonstrating superior efficiency on the tested hardware setup. This test is significant because it moves beyond theoretical benchmarks to show practical performance on the exact type of system—a powerful consumer GPU with 16GB VRAM—that many developers and enthusiasts are using for local AI experimentation, chatbot deployment, and coding assistants. It provides a crucial reference for choosing a model that balances speed, capability, and hardware constraints.
- Qwen 2.5 7B outperformed Gemma 2 9B, Nemotron Cascade 2, and GLM 4.7 Flash in tokens-per-second on an RTX 4080.
- The test used llama.cpp with optimized quantizations to fit models within the 16GB VRAM constraint.
- Performance was measured specifically for speed degradation as context length increased, a key metric for real-world use.
Why It Matters
Provides a practical hardware guide for developers choosing the fastest local LLM for coding, chatbots, and research on consumer GPUs.