,大模型推理加速技术的学习路线是什么? (English)

Generated: 2026-06-21 23:29:50

---

Oh man, guess what happened the day before yesterday? A buddy of mine who works on inference algorithms frantically sent me a PDF titled “2025 Ultimate Accelerated Inference Learning Roadmap,” packed with twenty papers, four frameworks, five quantization methods, and a requirement to learn CUDA programming. I handed him a glass of water on the spot and said, “Dude, you’re not trying to learn inference acceleration—you’re trying to weld yourself to a GPU!”

I’ve personally tested nearly a dozen frameworks, and I’ve stepped in more pitfalls than I’ve eaten meals in the cafeteria. So let me spell it out straight: In 2025, 90% of inference scenarios don’t need some god-tier roadmap. Three moves will handle it. The remaining 10% isn’t something you learn—it’s something you lose money to. Once you’ve watched your VRAM explode and your queues jam up with your own eyes, that’s how you’ll learn.

---

Step 1: First, get a solid grasp on that “VRAM black hole” called KV Cache

You think KV Cache is just “saving historical Keys and Values”? Way too naïve—I thought the same thing at first.

The first time I set up an inference service for someone, the prefill stage ran like a rocket. Then the moment it hit decode, it froze into a slideshow. Can you imagine? Staring at the server lights blinking like crazy while words squeezed out one by one—more painful than constipation.

I spent two days crouched in the server room investigating, and finally uncovered a shocking truth: KV Cache isn’t an “optimization” at all—it’s the lifeline of the decoding paradigm.

Think about it: without it, you’d have to recompute the historical Key/Value from scratch every time you generate a token. That means one conversation would cost you ten prefill runs. With it, you’re just paying the bill you were going to have to pay anyway—but the payment spot is VRAM. Stuff those historical KV pairs into memory, and every read eats bandwidth. The longer the sequence, the deeper this pit gets.

At this point, I can’t help slamming the table: Don’t dive headfirst into piles of papers! First, open the vLLM docs and understand what PagedAttention is. Then grab a demo on GitHub, change --max-model-len from 4096 to 40960, run it twice, and watch the VRAM usage and time-to-first-token—you’ll see with your own eyes how KV Cache opens its giant mouth and swallows your VRAM.

See? Counterintuitive, right? You think latency is solved by algorithms, but the first bottleneck is VRAM bandwidth. That one insight alone is worth a week of mindlessly flipping through papers.

---

Step 2: Pick a framework? No—pick “which one you can afford to wrestle with”

I tested all three hottest frameworks back in the day. Let me give it to you straight:

vLLM is the most solid floor. By 2025, its PagedAttention + continuous batching + FlashInfer has become the industry standard. Just last week, I switched a service from a custom scheduler to vLLM 0.6.2—same QPS, P99 latency dropped 40%! And its KV Cache offload is a lifesaver for ultra-long contexts—imagine writing a story 50,000 tokens long without getting killed by an OOM. That feeling is something else.

SGLang is the synonym for “wild tricks.” RadixAttention in agent scenarios feels like cheating—reuse the same prefix and VRAM usage drops by 70%! But can you handle the cost? Version updates are a rollercoaster. Last week I upgraded one commit and parameters that worked before broke. This framework suits geeks who live to tinker with code every day, not teams that want to “deploy and forget.” Before you choose, ask yourself: is your heart strong enough?

TensorRT-LLM I actually wouldn’t recommend for beginners. Unless your whole stack is NVIDIA cards and your versions match perfectly, the C++ errors during compilation will have you throwing things from your desk to the rooftop. But in 2025, it has a killer feature—native FP4 support for Blackwell. If you have a B200, that’s a game changer.

Someone might argue back: “You need to learn from the bottom up—write your own kernels for real optimization!” My reaction is blunt: If you can’t even explain where the bottleneck is in existing frameworks, what are you optimizing? First, go shed some blood and tears with these frameworks—memory fragmentation, the trade-off between batch size and latency, the scheduling logic when mixing prefill and decode—once you’ve tuned these by hand, then go read papers like FlashDecoding++ and RocketKV. You’ll exclaim: “Oh! So that’s the knot the author was trying to untie that I ran into last week!”

---

Step 3: Don’t be an information squirrel—pick one of two directions and master it

In 2025, papers on inference acceleration are everywhere, but only two directions are truly useful: KV compression and speculative decoding.

Let’s start with KV compression. The materials mention RocketKV (ICML 2025) and DecoQuant—I’ve done reproduction comparisons myself. DecoQuant’s data-free decomposition quantization is genuinely great—no calibration dataset needed, one click down to 2-bit. I tested it on Qwen2.5-72B: long-sequence throughput improved 1.8x, perplexity dropped less