The "VRAM Guessing Game" is costing developers thousands. I mapped the math for 2026 models.

Hi everyone,

After my last post about the "Retry Tax" on AI APIs, I got a lot of questions about when it’s actually cheaper to move to local inference.

The biggest bottleneck isn't the code—it's the hardware. I’ve seen so many founders buy an RTX 5090 only to realize that the 4-bit quant of the model they need doesn't fit because they forgot to calculate the KV Cache overhead and System buffers.

I spent the last week benchmarking how different quants (Q4_K_M vs Q8_0) actually behave in VRAM on the latest architectures.

The reality check:

A 70B model doesn't just need 70 * bits / 8.

You need to account for a ~1.2x CUDA overhead.

32k context window can eat up 2-4GB of VRAM just for the "memory" of the conversation.

I’ve automated this logic into a simple calculator to help the community avoid OOM (Out Of Memory) errors before they buy hardware.

If you're planning a local LLM transition for your SaaS, you can check the math here:
https://bytecalculators.com/llm-vram-calculator

Would love to hear how you guys are handling long-context windows. Are you offloading layers to system RAM or just sticking to smaller models?