You'll see:
- How to launch separate vLLM servers for different models
- How to split GPU memory between them without running out of VRAM
This approach works when you want to serve several smaller models concurrently on limited hardware.
Thursday, April 2, 2026
Running Multiple Models on One GPU with vLLM and GPU Memory Utilization
In this video I show how to run multiple vLLM model instances on the same GPU (Nvidia) in parallel by adjusting the --gpu-memory-utilization flag.
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1 comment:
Really useful walkthrough — the KV cache memory math is the part most vLLM guides skip, and it is exactly what decides whether you can run multiple models side by side on one card. One thing I would add is that for local LLM development, the 24GB cards like the RTX 3090 hit a sweet spot here: big enough to serve a couple of quantized models concurrently through vLLM without paying datacenter prices. The memory utilization flags you listed deserve more attention — most people just let vLLM grab everything and then wonder why a second model will not fit.
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