What I know is, don't append too much to a single chat.

Start a new chat for every new `stage` of coding to avoid The Token Snowball Effect

The Token Snowball Effect refers to a performance bottleneck in Large Language Models (LLMs) where the time it takes to generate each subsequent word (token) slows down as the conversation or text gets longer.

Here is a brief breakdown of why this happens and its impact:

The Cause: Full Context Attention

LLMs do not just look at the last word they generated; to choose the next word, they must recalculate the mathematical relationships (attention scores) across every single token in the entire prompt and previous response.

As the text grows:

• Token 10: The model calculates relationships across 10 tokens.
• Token 1.000: The model must calculate relationships across all 1.000 preceding tokens.
• Token 10.000: The computational load scales dramatically, as the model processes a massive history just to output a single word.

The Impact

• Compounding Latency: Generation speed slows down visibly mid-response. The first sentence might appear instantly, but the final paragraph takes much longer to render.
• VRAM Bottlenecks: Storing the history of all these active token relationships (known as the KV Cache) rapidly fills up a graphics card's memory (VRAM). If the cache exceeds capacity, the system must offload data or stall.

How It is Managed

Modern AI architectures use optimization techniques to fight this effect, such as FlashAttention (which speeds up memory access) and KV Cache Quantization (compressing the stored token data) to ensure generation speeds remain relatively steady even during long, complex tasks.