If you're deploying an LLM for the first time, you've probably searched for something like:
What's the best GPU for AI?
It's a common question, but it's usually the wrong place to start.
Before comparing A100s, H100s, or Blackwell GPUs, answer one question first:
How much VRAM does your workload actually need?
Choosing a GPU without first sizing VRAM often results in either out-of-memory errors or paying for memory you don't need. That's why infrastructure planning should start with the workload, not the hardware.
Why VRAM Comes First
For LLM inference, GPU memory is usually the first constraint.
Your VRAM budget isn't just the model itself. It also includes:
- Model weights
- KV cache
- Framework overhead
- Production headroom
Even two deployments running the same model can require very different amounts of memory depending on context length, concurrency, and inference settings.
That's why choosing a GPU before understanding memory requirements often leads to poor infrastructure decisions.
*Model Size Doesn't Tell the Whole Story
*
Many developers assume parameter count directly determines GPU requirements.
It doesn't.
The same model can require dramatically different amounts of VRAM depending on precision and quantization.
A model running in FP16 may require multiple high-memory GPUs, while the same model using 4-bit quantization can often fit on a single accelerator.
Understanding this difference is one of the easiest ways to avoid over provisioning infrastructure.
Don't Forget the KV Cache
A deployment that works perfectly during testing can run into memory limits once it reaches production.
One of the biggest reasons is the KV cache.
Every active request stores key-value tensors in GPU memory. As context length and concurrency increase, so does memory consumption.
Ignoring KV cache is one of the most common mistakes when sizing GPUs for production inference.
Mixture-of-Experts Models Still Need Careful Planning
Another misconception is that Mixture-of-Experts (MoE) models only require memory for the experts used during inference.
While only a subset of experts is activated for each token, the model's weights still need to be loaded into GPU memory when the model starts.
That means VRAM planning remains just as important for MoE deployments.
A Better Way to Choose a GPU
Instead of asking:
Which GPU should I rent?
Start with these questions:
Which model am I deploying?
What precision or quantization will I use?
How many concurrent requests do I expect?
What context length will I support?
How much VRAM will the complete workload require?
Once those answers are clear, selecting the right GPU becomes much more straightforward.
Final Thoughts
Choosing AI infrastructure isn't about renting the newest GPU.
It's about matching your workload to the right amount of memory, compute, and infrastructure.
Starting with VRAM helps avoid deployment failures, improves GPU utilization, and prevents unnecessary infrastructure costs.
Packet.ai's guide walks through VRAM sizing for today's leading open-source models, explains common sizing mistakes, and maps workloads to GPU configurations to help teams make better infrastructure decisions.
Read the full guide:
https://packet.ai/blog/rent-gpu-for-ai-vram-requirements-guide
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