Match each long context strategy to what it modifies.
Long context techniques sit at different layers, positions, attention pattern, I/O, GPU sharding, KV-cache windowing, and stack rather than compete.
Imagine speeding up a busy restaurant. You could change the recipes (that's PI and YaRN, fixing positions). You could decide not every table needs every dish (sparse and sliding window attention). You could rearrange how the kitchen moves food between the fridge and the stove (FlashAttention's memory trick). You could open extra kitchens connected in a ring (ring attention across GPUs). You could decide what stays warm on the counter and what gets cleared (attention sinks, KV cache windowing). Each fix lives in a different part of the restaurant. The big trick is they stack, you can do all of them at once.
Detailed answer & concept explanation~6 min readEverything you need to truly understand this topic: intuition, mechanics, step by step explanation, code, formulas, and worked example. Click to expand.
Everything you need to truly understand this topic: intuition, mechanics, step by step explanation, code, formulas, and worked example. Click to expand.
Everything you need to truly understand this topic: intuition, mechanics, step by step explanation, code, formulas, and worked example.
Everything important, quickly.
Walk all five layers with examples, distinguish exact (FlashAttention, ring) from approximate (sparse, sliding, sinks) techniques, explain how a production long context system combines them, and answer when/why each can or can't be stacked.
| Technique | Stack layer | Same math? |
|---|---|---|
| PI / YaRN | Positional encoding | Yes (after fine tune) |
| Sliding window attention | Attention pattern | No (approximation) |
| Sparse attention | Attention pattern | No (approximation) |
| FlashAttention | I/O pattern | Yes (exact) |
| Ring attention | GPU partitioning | Yes (exact) |
| Attention sinks | KV cache windowing | No (approximation at serving) |
Real products, models, and research that use this idea.
- Llama 4 Maverick stacks RoPE base scaling, FlashAttention 3, and ring attention for its long context pretraining run.
- Mistral Large 3 retains sliding window attention plus FlashAttention as its layer-2 plus layer-3 combination.
- Gemini 3.1 Pro relies on ring style sequence parallelism to serve multi million token contexts.
- DeepSeek V4 long context releases combine YaRN-derived RoPE scaling with FlashAttention 3 kernels.
- Claude Opus 4.7 long context serving uses position scaling, FlashAttention, and sequence parallelism in combination.
What an interviewer would ask next. Try answering before peeking at the approach.
QWhy does StreamingLLM need the FIRST tokens specifically as sinks?
Don't say thisRed flags and common mistakes that signal junior thinking. Click to expand.
Red flags and common mistakes that signal junior thinking. Click to expand.
Lumping all 'long context' techniques together as if they were alternatives, they're not. FlashAttention doesn't 'compete' with sparse attention; they modify different layers and can stack.
The night-before-the-interview bullets. Scan these on the way to the call.
Primary sources. Skim if you want the original framing.
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