For local LLM workloads, the first bottleneck is often not raw silicon but the memory feeding it. A comparison posted on Reddit by u/jacek2023 tries to bring order: three charts based on the GPUs most discussed in the LocalLLaMA, LowEndLocalAI, and LocalLLM subreddits. The first shows gigabytes of VRAM per dollar; the second shows on-paper bandwidth, not tokens per second; the third, in the comments, shows bandwidth divided by price.

It is a deliberately rough comparison, as the author acknowledges. Prices were collected with ChatGPT and may contain inaccuracies; where possible, new prices were used, otherwise second-hand ones. But the value is not accounting precision: it shifts the comparison from marketing specs to the real constraints of self-hosted inference.

Using rated bandwidth instead of tokens per second is more clever than it looks. Tokens per second depends on too many variables: serving framework, quantization level, context length, batch size, and drivers. Bandwidth, on the other hand, is a physical figure declared by the manufacturer and says a lot about how quickly a GPU can move model weights between VRAM and compute units. For models that do not fit on a single GPU, or for inference with a long context cache, this parameter often becomes the limiting factor.

The 3090 as an informal yardstick

The fact that the chart uses the RTX 3090 as an implicit reference is not a detail. In local communities, this card has become a measuring stick: the chart lets you see whether another GPU is, on paper, faster or slower than the 3090. It turns scattered data into a concrete question: is it worth paying more for a newer model, or does the previous-generation card still offer the best balance?

This is where pricing comes in. The ChatGPT-assisted collection mixes new list prices and used-market quotes, with an acknowledged margin of error. It is not a basis for a corporate purchase order, but it reflects a reality: the GPU pool for on-premise inference is often assembled through unofficial channels, where real prices fluctuate and availability changes quickly. Anyone trying to calculate the TCO of a small local node has to deal with an opaque market.

For teams evaluating on-premise deployments, there are trade-offs between consumer cards and datacenter hardware: warranty, power delivery, cooling, and operational continuity are not secondary parameters. AI-RADAR offers analytical frameworks at /llm-onpremise to read these constraints in a structured way.

The comparison from jacek2023 does not replace a serious technical evaluation. But it asks the right question: for a local LLM, how much real memory do you get for every euro spent, and how fast can you actually use it? If you cannot answer, you may be looking at the wrong GPU.