Speculative Decoding: Faster AI Is Here, But Is It Enough?

Everyone wants faster AI. The frustrating pause between your prompt and the first word from a model is the biggest UX killer in AI products today. So when new techniques promising 2-3x faster LLM inference pop up, it’s easy to get excited. But the engineering cost of that speed is almost always glossed over, and that's where teams get into trouble.

The latest example, as Hacker News reported, is a paper on DSpark, a method for accelerating LLM inference using speculative decoding. It’s a genuinely clever approach that tackles the sequential, one-token-at-a-time nature of autoregressive models. The performance gains are real. But implementing it isn't a simple library update. It’s an architectural shift with serious consequences for your serving stack, your hardware budget, and your team's focus.

The Latency We All Feel

Let's make this concrete. Imagine we're building an AI-powered code review assistant for our DevStack product. A developer pushes a commit, and our assistant is supposed to offer suggestions. If that feedback takes 20 seconds to appear, the developer has already moved on to the next task. The magic is gone. The tool feels slow and disruptive, not helpful.

Why is it so slow? It’s not the network. It's not our application code. It's the time-to-first-token (TTFT) and the subsequent token generation speed of the LLM. The model is thinking. For a moderately complex request to a 70-billion parameter model, we might see latencies like this:

• p50 (median): 4 seconds
• p95: 12 seconds
• p99: 20 seconds

That p99 means that one out of every 100 developers gets a truly frustrating wait time. You can’t build a fluid, real-time experience on top of that. This is the core problem that techniques like speculative decoding aim to solve.

How Speculative Decoding Changes the Game

At its core, LLM inference is slow because it’s a serial process. The model generates token #1, then uses token #1 to generate token #2, and so on. It can’t generate the whole sentence at once.

Speculative decoding hacks this process. It uses two models:

1. A large, powerful, but slow “verifier” model. This is your main model, like Llama 3 70B.
2. A smaller, less powerful, but very fast “draft” model. This could be a distilled version of the main model or just a much smaller one from the same family, like a 7B parameter model.

Here’s how it works. Instead of asking the big model for one token, you first ask the small, fast model to generate a sequence of several tokens, say, a 5-token draft. Then, you pass that entire 5-token draft to the big model. The big model can process the whole sequence in a single forward pass, which is much faster than running five separate passes. It effectively checks the draft model's work in parallel.

If the big model agrees with the draft, you just generated five tokens for the cost of one big model pass (plus a very cheap small model pass). That’s a huge win. If the big model disagrees at, say, token #3, you accept the first two correct tokens, discard the rest, and let the big model generate the correct token #3. Then the process repeats.

This is a classic CS tradeoff: you're using computation (running the draft model) to reduce latency. When it works, the speedup is dramatic.

The Hidden Infrastructure Tax

This is where the engineering reality sets in. The 2-3x speedup isn't free. It comes with a significant infrastructure and complexity cost that the research papers don't always emphasize. We call this the infrastructure tax, and you pay it in three main areas.

1. GPU Memory Pressure

Getting one large LLM to fit into GPU VRAM is already a challenge. A 70B parameter model at bfloat16 precision requires 140GB of VRAM. That means you already need at least two high-end GPUs like NVIDIA A100s (80GB) just to load the model, before you even consider the space needed for the KV cache during inference.

Now, with speculative decoding, you have to load a second model. Even a smaller 7B draft model needs another 14GB of VRAM. Suddenly, your two-GPU setup might not be enough. You might be forced to move to more expensive H100s or a larger multi-GPU node. This directly impacts your cloud bill and hardware procurement strategy. The cost-per-instance goes up, and you're betting that the increased throughput will make up for it. That's not always a winning bet.

2. Orchestration Complexity

Your inference logic just went from a simple model.generate() call to a complex, stateful orchestration loop. Your serving code now needs to:

1. Invoke the draft model.
2. Capture its proposed token sequence.
3. Pass that sequence to the main model for verification.
4. Parse the verification results.
5. Handle the fallback logic when the draft is rejected.
6. Manage the state of the token stream.

This isn't just a few extra lines of code. It's a new system. It needs to be robust, debuggable, and performant. What happens when the draft model crashes but the main one doesn't? How do you monitor the acceptance rate of the draft tokens to know if the process is even being effective? This is a non-trivial engineering effort that your team has to build and maintain. It's a distraction from building your actual product features.

For example, a simplified pseudo-code implementation might look like this:

# This is pseudocode to illustrate complexity

def speculative_generate(main_model, draft_model, prompt, max_len):
    tokens = tokenize(prompt)
    
    while len(tokens) < max_len:
        # 1. Get a draft sequence from the small model
        draft_tokens = draft_model.generate(tokens, draft_length=5)
        
        # 2. Verify the sequence with the large model
        verification_results = main_model.verify(tokens, draft_tokens)
        
        # 3. Process the results
        num_accepted = find_first_mismatch(verification_results)
        
        if num_accepted > 0:
            # Accept the correct part of the draft
            tokens.append(draft_tokens[:num_accepted])
        
        # 4. Fallback: let the main model generate the next single token
        next_token = main_model.generate(tokens, max_new_tokens=1)
        tokens.append(next_token)
        
    return detokenize(tokens)

This looks simple enough, but managing the KV caches for both models and optimizing the data flow between them is where the real work lies.

3. Model Compatibility and Drift

The whole system hinges on the draft model being a good, cheap predictor of the main model. If the draft is consistently wrong, your acceptance rate will be low. In the worst-case scenario, you're paying the cost of running two models just to get the performance of one, or even worse.

This introduces a new maintenance problem. You can't just fine-tune your main model without considering the draft model. If the fine-tuning causes the main model's behavior to drift too far from the draft model's, your performance will degrade. You might need to co-train or regularly re-distill a new draft model. This adds another step to your MLOps pipeline and requires specialized expertise.

So, Should Your Team Implement This?

This is a classic build vs. buy vs. wait decision, and the right answer depends on where you are in the stack.

If you're building a product using a third-party API like OpenAI or Anthropic, you should not be thinking about implementing this yourself. Your job is to build the best product on top of their platform. However, you should be aware that these techniques exist. When one provider offers a new

---

Building something in this space? AgileStack helps teams ship enterprise-grade software without the consulting-firm overhead. Book a 30-minute call and tell us what you're working on.