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Local LLM Inference: The 24GB VRAM Wall

Running Large Language Models locally is the dream. Total privacy, zero API costs, and the ability to hack the internal weights.

However, anyone who has tried to run a 70B parameter model knows the painful reality: consumer hardware hits a hard wall at 24GB of VRAM (the maximum capacity of an RTX 3090 or 4090).

The Allocation Problem

When you load a model, you aren’t just loading the weights. You also need space for the KV Cache, which grows linearly with your context window.

Below is a live simulation of what happens when you load a heavily quantized 34B model and start feeding it a 32K context window. Watch the allocation spike.

VRAM Allocation Tracker

Active Inference
Allocated: 0 GB Total: 24 GB

As the widget shows, you hover dangerously close to the 24GB limit. If you exceed it, the system spills over into system RAM (CPU memory).

When that happens, your inference speed drops from 40 tokens/sec to 0.5 tokens/sec. The model is essentially dead in the water. This is why techniques like EXL2 quantization and flash attention are so critical for local hackers.