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Speculative decoding can make a local language model feel noticeably faster, but whether it feels instant depends on three things: how quickly the proposer drafts tokens, how often the main model accepts those drafts, and which latency number you are actually watching. This article explains the mechanism, the proposer options that current inference projects document, and how to test the claim on your own machine.
The “feels instant” and “I prefer it” parts of the title are a personal account. This article does not include a timed benchmark of that setup. The figures below come from project documentation and a 2024 study, and they describe those tests, not the machine described here.
What speculative decoding does
A language model normally writes one token at a time, and each token requires a full pass through the model. Speculative decoding speeds this up by letting a cheaper proposer guess several upcoming tokens, then asking the main model, called the target, to check all of those guesses together. The check resembles processing a short batch, which hardware can do more efficiently than producing the same tokens one by one.
The llama.cpp project’s documentation on speculative decoding states the core idea directly: “By generating draft tokens quickly and then verifying them with the target model in a single batch, this approach can achieve substantial speedups when the draft predictions are frequently correct.”
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Two points follow from that description. First, the target model stays in charge. Correctly implemented verification is designed to preserve the target’s output behavior, so speculative decoding is not a way to swap in a smaller model and accept its answers. Second, the speedup depends on the guesses being right often enough to be worth the overhead of making them.
The proposer is a choice, not a fixed part of the method
People often assume speculative decoding requires a second, smaller model. That is one option, but not the only one. The llama.cpp documentation describes several approaches, and the vLLM documentation lists a similar range. The table below summarizes what the two projects document; where a source does not describe a requirement, the cell says so.
| Proposer option | Documented by | Needs a separate model? | What the documentation describes |
|---|---|---|---|
| Standalone draft model | llama.cpp; vLLM “draft model” | Yes, a smaller model alongside the target | A separate model proposes candidate tokens, and the target verifies them. |
| EAGLE-3 | llama.cpp; vLLM lists EAGLE | Not stated as a separate general-purpose model; compatibility requirements differ | Uses the target model’s hidden states to propose tokens. |
| DFlash | llama.cpp | Not stated | Drafts a block of tokens in one forward pass. |
| DSpark | llama.cpp | Not stated | Adds a semi-autoregressive Markov component to the drafting step. |
| N-gram cache | llama.cpp; vLLM “n-gram” | No | Proposes tokens from patterns in the existing token history. Detailed mechanics are not stated in these sources. |
| N-gram map | llama.cpp | No | Looks for patterns in token history and needs no separate model. |
| Multi-token prediction (MTP) | vLLM | Not stated | A model-based method listed alongside EAGLE and draft models. |
| PARD; MLP | vLLM | Not stated | Model-based methods listed in the vLLM documentation. |
| Suffix decoding | vLLM | Not stated | Listed as a simpler method alongside n-gram decoding. |
The vLLM guide offers a useful rule of thumb. Model-based methods can give stronger latency reduction in some settings, while simpler methods can give modest gains without the extra workload of running a second model. That is project guidance about typical trade-offs, not a result guaranteed for any particular setup.
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsEach option has its own model and compatibility requirements. Check the current documentation for the exact model files and backend version before you try one, because a method that works with one target model may not load with another.
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What decides whether it helps
The question is not simply whether the proposer is “good.” Two factors matter most: how fast the proposer produces its guesses, and how many of those guesses the target accepts.
A 2024 study by Minghao Yan, Saurabh Agarwal, and Shivaram Venkataraman, titled “Decoding Speculative Decoding,” analyzed more than 350 experiments. Its authors found that draft-model latency mattered substantially, while a draft model’s language-modeling capability did not strongly predict speculative decoding performance. Their abstract puts it this way: “The speedup provided by speculative decoding heavily depends on the choice of the draft model.”
The same study reports a result of 111% higher throughput for the authors’ proposed draft model over existing draft models in its sampling-based experiments. That figure compares draft models within those tests, and the experiments ran on a setup of four Nvidia 80GB A100 GPUs. It is not a typical speedup, and it does not predict what a single consumer machine will see.
When the proposer is slow or the target rejects most of its guesses, speculation can add overhead without a corresponding gain. Expect a disappointing result when:
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- The proposer takes a meaningful share of the time that the target would have spent generating the same tokens.
- Your prompts produce text the proposer rarely predicts correctly, such as unusual code or highly varied creative writing.
- Sampling settings make the target’s outputs less predictable, which lowers acceptance.
- Memory pressure forces the proposer and target to compete for the same resources.
What “instant” means in measurable terms
“Fast” covers several different measurements, and a change can improve one while leaving another unchanged. Before you claim a difference, name the metric you measured.
| Metric | What it measures | Why it matters for a local single-user setup |
|---|---|---|
| Time to first token | The delay between sending a prompt and receiving the first output token | Determines how long the screen looks idle before a reply starts |
| Inter-token latency | The average gap between successive output tokens | Controls how smoothly a streamed reply appears; this is the number most related to the feeling of a fluent stream |
| Total completion time | The time from prompt to finished response | Matters most for long answers, code blocks, and agent loops with many calls |
| Throughput | Output tokens produced per second across requests | Matters more for servers and many concurrent users than for one person at a keyboard |
The sources do not establish how speculative decoding moves time to first token relative to the later tokens, so measure it rather than assuming. For a local single-user experience, the most informative numbers are usually inter-token latency and total completion time, with time to first token recorded alongside them.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to test the claim on your own machine
A credible before-and-after comparison needs a stated baseline and a named metric. vLLM’s documentation cautions that results vary with model family, traffic pattern, hardware, and sampling settings, so the only numbers that matter for your setup are the ones you measure on it. The steps below describe a comparison you can repeat.
- Record the backend name and exact version, the target model file, and the proposer method and model. Keep these fixed across both runs.
- Write a small prompt set that covers your real use: a short factual question, a long-context summary, a code generation task, and a creative prompt. Fix the prompt text and the expected output length.
- Fix the sampling settings for both runs, including temperature, top-p, and any seed the backend supports. Changing sampling parameters changes acceptance, so it changes the result.
- Run the baseline with speculation turned off. Discard the first few runs as warm-up, then record several runs per prompt.
- Run the same prompts with speculation on, using the same backend and hardware state.
- Log time to first token, inter-token latency, and total completion time for every run. If you serve several requests at once, also log throughput and the concurrency level.
- Check the outputs. With deterministic settings, the text should match between runs; with sampling enabled, judge quality rather than expecting identical text.
- For a formal baseline across engines, llama.cpp points users to its SPEED-Bench client, which the project describes as an end-to-end comparison tool.
If the speculative run is slower, look at three things first: the proposer’s own time, the acceptance rate if your backend reports it, and whether the proposer and target fit in memory together without swapping.
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Where the preference fits
The preference for a local model over a cloud API is a reasonable personal conclusion, but the sources reviewed here do not compare a specific local model with a specific cloud API. No measurement in this article shows that a local model with speculation beats a hosted service on latency.
If you want to test the comparison fairly, use the same prompts and the same metrics on both systems, and record the network path for the cloud run, since that is one of the factors that differ between them. Latency is only one axis. Output quality on your tasks, the cost per request, data handling, and the hardware you must maintain are all part of the trade-off, and this article does not measure them.
Hardware context
Speculative decoding needs enough memory to hold the target model and the proposer at the same time, and the proposer adds its own memory and compute cost. The 2024 study used four 80GB A100 GPUs, which is a description of that study’s scope, not a requirement for the technique. Your model size, quantization, and context length determine what fits, so check those figures against your own hardware before choosing a method.
This article does not recommend a specific GPU, because the right choice depends on the model you run and your memory needs.
Bottom line on speculative decoding
Speculative decoding is a real, conditional speedup. A fast proposer whose guesses are often accepted can make a local model feel noticeably quicker, while a slow proposer or low acceptance can erase the gain. Whether your setup feels instant is a measurable question, and the only reliable answer comes from running the same prompts with speculation on and off and reporting inter-token latency and total completion time. Your preference for the local setup is a legitimate experience to report, provided the comparison behind it is stated as clearly as the method.
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