Do these 3 things before closing this tab:
1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsSpeculative decoding can make a local model feel faster, but only conditionally. It helps when a cheap proposer guesses tokens that the main model accepts, and it can add overhead when those guesses are slow or often rejected. My preference for running a model locally is a judgment from daily use, not a benchmark result. This article separates what I observed from what the technical evidence supports, and it lays out how you can test the same claim on your own machine.
What speculative decoding changes
A normal language model produces one token per forward pass of the large “target” model. Speculative decoding adds a proposer that drafts several candidate tokens ahead. The target then checks those candidates together, in a single pass that looks more like processing a short batch than generating one token at a time. Candidates the target accepts are kept; the first rejected candidate is replaced by the target’s own choice, and drafting resumes from there.
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The llama.cpp project documentation summarizes the idea this way: “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.” The phrase that matters is “frequently correct.” The target still decides every token that reaches the output, and a correctly implemented verification step is designed to preserve the target’s output behavior. Speculative decoding is therefore not a way to swap in a smaller model and accept its answers.
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Most people picture a second, smaller model when they hear “draft model,” but that is only one option. The llama.cpp documentation lists standalone draft models, EAGLE-3, DFlash, DSpark, an n-gram cache, and n-gram map methods. The vLLM documentation lists EAGLE, multi-token prediction (MTP), draft models, PARD, MLP proposers, n-gram methods, and suffix decoding. These are different implementations with different model and compatibility requirements, so the method you pick determines what you have to install and maintain.
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| Proposer family | Methods named in the project docs | Needs a separate draft model? | Trade-off the docs describe |
|---|---|---|---|
| Standalone draft model | Draft models (llama.cpp, vLLM) | Yes | Proposer speed and acceptance rate decide the gain; more memory and a second model to manage |
| Model-internal proposers | EAGLE-3, DFlash, DSpark (llama.cpp); EAGLE, MTP, PARD, MLP (vLLM) | Not stated in the cited docs; check each method’s requirements | vLLM notes that model-based methods can offer stronger latency reduction in some settings |
| Token-history methods | N-gram cache, n-gram map (llama.cpp); n-gram, suffix decoding (vLLM) | No | vLLM notes simpler methods can offer modest gains without the workload of a separate draft model |
Standalone draft models
A small model from the same family drafts tokens for the larger one. This is the setup most people mean when they say they added a second model. It requires memory for both models and a draft that runs fast enough that its cost does not eat the savings.
Model-internal proposers
EAGLE-3 uses the target model’s hidden states to propose tokens, which means the proposer is tied to the target rather than being an independent model. DFlash drafts a block of tokens in one forward pass. DSpark adds a semi-autoregressive Markov component. Each has its own compatibility requirements, so check the documentation for the exact target model you run before assuming support.
Token-history methods
N-gram methods look for repeated patterns in the tokens already generated or in the prompt, and they need no separate model. They work best when the output repeats material that appears earlier, such as code edits, quoted passages, or structured text with recurring phrases. For open-ended writing where little repeats, they tend to propose little that the target accepts.
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Why proposer speed matters more than proposer intelligence
It is tempting to assume a better draft model gives a bigger speedup. A 2024 study by Minghao Yan, Saurabh Agarwal, and Shivaram Venkataraman, “Decoding Speculative Decoding,” analyzed more than 350 experiments and found that draft-model latency mattered substantially, while the draft model’s language-modeling capability did not strongly predict speculative decoding performance. The abstract states that “the speedup provided by speculative decoding heavily depends on the choice of the draft model.” A fast proposer whose tokens are often accepted can help. A slow proposer, or one whose tokens are often rejected, can erase the gain or add overhead.
The same paper reports 111% higher throughput for its proposed draft model over existing draft models in its sampling-based experiments. That number compares draft models against each other in that study’s setup, which used four Nvidia 80GB A100 GPUs. It is not a typical speculative decoding speedup and it is not a prediction for a laptop or a consumer GPU. I cite it only to show how strongly the choice of proposer can change results.
What “instant” means in measurable terms
“Feels instant” bundles several metrics that can move independently. Report them separately:
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- Time to first token (TTFT): the wait before the first word appears after you send a prompt.
- Inter-token latency (ITL): the gap between successive tokens while the reply streams. For a single user reading as the text arrives, this shapes how smooth the stream feels.
- Total completion time: how long the full reply takes. This matters most for long answers and agent-style loops.
- Throughput: tokens generated per second across requests. This matters for a server handling several users, and matters less for one person at a keyboard.
Speculative decoding acts on the token-generation phase, so its effect shows up most clearly in streaming speed and total completion time. It is less likely to shorten the initial wait, which is shaped by other parts of the pipeline. If your complaint is a long pause before anything appears, a faster proposer may not fix it.
How to test the claim on your own setup
Any credible comparison needs a stated baseline and a named metric. Use this procedure:
- Fix the target model, its quantization, and the backend version. Write them down, because a version change can alter results.
- Choose 10 to 20 prompts that resemble your real use, including short replies and long generations.
- Fix the sampling settings for both runs: temperature, top-p, and maximum output length. Sampling changes how often a draft is accepted, so the same settings must apply to the baseline and the speculative run.
- Run the baseline with speculation off, then the same prompts with speculation on. Close other GPU-heavy applications during both runs.
- Record TTFT, ITL, and total completion time for every prompt. Add throughput if more than one user will hit the server.
- Check output quality. At greedy settings, compare outputs between the two runs directly. Under sampling, judge the outputs on the tasks you care about, since a sampled run will not match token for token.
- Repeat the runs and report the median and the spread, not a single best run.
Both major projects provide tooling for this. llama.cpp points users to its SPEED-Bench client for an end-to-end baseline comparison, and vLLM provides a reproducible offline example and a benchmark command-line tool. Results vary with model family, traffic pattern, hardware, and sampling settings, so treat a published speedup as a starting point for your own measurement rather than a promise.
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What my preference rests on
I used speculative decoding with a local model, and the experience of replies streaming with little perceptible delay is what I mean by “instant.” That is an account of how the setup felt to me, not a measured result. This article does not include a controlled benchmark of my machine, and I have not compared it against a specific cloud API under matched conditions. The steps above are how you would check the claim.
My preference for local over cloud rests on factors that do not depend on speed alone. The table below sets out the trade-offs as they generally apply, with the speed row left to your own measurements.
| Factor | Local model with speculative decoding | Cloud API |
|---|---|---|
| Response speed | Depends on your hardware, the proposer method, and the workload; measure with the steps above | Depends on network conditions and provider load; not measured in this article |
| Cost structure | Hardware and electricity up front; no per-token fee from a provider | Usually billed per token or per request |
| Data handling | Prompts stay on your machine | Prompts are sent to the provider; check its data terms |
| Model size | Limited by the memory you have | Access to the largest hosted models |
| Maintenance | You manage updates, drivers, and compatibility between the target and proposer | The provider manages infrastructure |
When a local setup with speculation makes sense
Speculative decoding is most likely to help a local setup when these conditions hold:
- You use the model mainly for interactive, single-user work, where streaming speed and total completion time are what you notice.
- You can run a target model that fits in memory and produces answers you accept for your tasks.
- Your proposer is fast enough that its own cost does not outweigh the accepted tokens. Measure this; do not assume it.
- Your output patterns suit the method you chose. Token-history methods need repetition in the output, and draft models need a close match with the target.
It is less likely to help if you need the largest models, serve many concurrent users with a throughput target, or depend on a model that your chosen method does not support. In those cases, the cloud API’s scale may matter more than the local speed gain.
For me, the combination of streaming speed I can live with, prompts that stay on my machine, and no per-token bill is what makes local the default. The speed improvement is real in the sense I experience it, but its size on your hardware is something only your own baseline can answer.
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