To ensure I provide the most relevant research, could you clarify:

• Do you have a specific field of application in mind (e.g., machine learning, engineering, physics)?
• Are you interested in global optimization algorithms, adaptive sampling methods, or other heuristic approaches?
• Do you want a comparison of these methods in terms of efficiency, scalability, or ability to detect important parameters?

Let me know so I can tailor the research accordingly.

Thanks for the clarification! I will research alternative algorithms to Bayesian optimization for efficiently exploring parameter space in LLM serving configurations. The focus will be on methods that can identify the most important parameters to reduce the number of search attempts, especially in high-dimensional spaces. I'll also compare their efficiency and scalability in real-world applications. I'll get back to you once I have the findings

Exploring Efficient Alternatives to Bayesian Optimization for LLM Serving Configuration

Large Language Model (LLM) serving involves tuning many system parameters (e.g. batch size, number of nodes, parallel threads) to meet performance targets. Traditional Bayesian Optimization (BO) is effective for expensive black-box tuning but can face challenges in high-dimensional spaces and with limited evaluations. This report explores alternative algorithms and techniques that address these challenges. We focus on methods to identify key parameters, handle high-dimensional search spaces, and minimize the number of costly evaluations, comparing their efficiency, scalability, and real-world use in LLM serving scenarios.

1. Identifying Key Parameters to Reduce Search Space

Not all configuration knobs impact performance equally. Focusing on the most influential parameters can dramatically shrink the search space:

• Functional ANOVA (fANOVA): A statistical method to quantify each hyperparameter’s contribution to performance variance. Hutter et al. (2014) introduced an efficient way to do this using random forest surrogates【24†L123-L131】【24†L139-L147】. Studies have found that “some hyperparameters indeed tend to be much more important than others”【24†L113-L119】. By applying fANOVA or similar sensitivity analysis on initial trial data, one can identify which factors (e.g. batch size, number of nodes) dominate performance. Less important settings (like minor caching flags or logging frequency) can be fixed to defaults, reducing dimensionality.

• Domain Knowledge & Heuristics: Practical insights often tell which parameters likely matter most. For instance, in LLM serving, batch size heavily influences throughput/latency trade-offs, and number of nodes affects scaling efficiency. Focusing on these first can yield big gains. One approach is a two-phase optimization: tune critical parameters coarsely to find a good region, then fine-tune remaining parameters within that subspace.

• Iterative Freezing: Another strategy is to iteratively fix parameters. Start by varying all parameters in a coarse search (e.g. random sampling) to gauge impact. If a parameter shows little effect on the objective, keep it constant going forward. This concentrates search on the impactful dimensions and avoids wasteful exploration in irrelevant directions.

By reducing the search space upfront, subsequent optimization (whether random, evolutionary, or model-based) becomes more tractable. Identifying key parameters ensures computational effort is spent only where it counts, a crucial advantage when each LLM serving evaluation is time-consuming.

2. Handling High-Dimensional Parameter Spaces Efficiently

High-dimensional tuning (dozens of knobs or more) quickly leads to an explosion of possibilities. Simple grid search becomes infeasible, as the number of combinations grows exponentially【15†L175-L184】. Several approaches help navigate large configuration spaces without exhaustive search:

• Random Search Over Grid Search: Random sampling is a surprisingly strong baseline for high-dimensions. Unlike grid search that wastes trials on unimportant dimensions, random search distributes samples more evenly across dimensions’ ranges【17†L249-L257】. Bergstra and Bengio (2012) showed “random search is more efficient than grid search in high-dimensional spaces because functions of interest have a low effective dimensionality”【17†L249-L257】. In other words, typically only a few parameters really matter, and random search has a better chance of hitting useful combinations by exploring more diverse values instead of an exhaustive grid. This makes random search simple, scalable, and embarrassingly parallel – ideal as a first pass to cover a broad space quickly.

• Surrogate Models Beyond Gaussian Processes: Standard Bayesian optimization often uses Gaussian Process surrogates, which can struggle as dimensions increase (GPs become slow to train and may need many points to fit complex surfaces)【27†L285-L293】. Alternatives like the Tree-Structured Parzen Estimator (TPE) and random forest-based models offer more scalable probabilistic search:

  • TPE: Models good vs. bad configurations with separate density estimates and chooses new points by maximizing the ratio of those densities. It’s a form of Bayesian optimization that tends to handle higher dimensions and mixed types (continuous/discrete) more efficiently than GPs【30†L1-L8】. Libraries like Hyperopt and Optuna default to TPE for its solid performance in many hyperparameter tuning tasks.
  • Random Forest Surrogate (SMAC): Used in the SMAC optimizer, it fits a random forest regression to observed results【24†L139-L147】. Random forests scale better with many parameters (including categorical settings) and can model irregular response surfaces. This approach was successful in the Auto-WEKA and Auto-sklearn projects for high-dimensional AutoML problems, and it underpins some industry tuners. It still employs Bayesian optimization principles (sequential model-based optimization) but avoids the cubic complexity of GPs, making it more scalable in higher dimensions.

• Dimensionality Reduction Techniques: When very many parameters exist, methods can search in a lower-dimensional embedding of the space:

  • Random Embeddings (REMBO): Project the high-D space onto a low-D subspace with a random linear embedding and perform optimization there, hoping the true optimum lies in that subspace【24†L111-L119】. This mitigates the curse of dimensionality by optimizing fewer dimensions at a time.
  • Principal or Partial Component Analysis: Using prior experiment data, one could attempt to reduce features (parameters) via PCA or identify a few principal components of variation in outcomes. However, for system configurations, such techniques are less common than for ML model hyperparameters.

  While embedding methods are more on the research frontier, they highlight that often an effective search need not explore all dimensions equally. By smartly constraining or sampling subspaces, one can find good solutions without exponential effort.

In practice, combining these ideas can be powerful: for example, use a few random searches to determine which parameters matter (identifying an effective subspace), then apply a surrogate-based optimizer (like TPE or SMAC) within that subspace to fine-tune. This tackles high dimensionality by focusing search where it matters and using models that cope with complexity.

3. Multi-Fidelity and Early-Stopping Strategies to Reduce Evaluations

Because each evaluation (deploying an LLM config and measuring performance) is expensive, a key goal is to find good configurations with as few full evaluations as possible. Multi-fidelity optimization exploits the idea of testing many configurations cheaply at first, then allocating more resources to the promising ones. This significantly cuts down wasted evaluations:

• Successive Halving and Hyperband: These are bandit-based algorithms that strategically allocate different amounts of resources (time, data, etc.) to different trials:

  • Successive Halving (SHA) evaluates a pool of configurations with a small budget (e.g. run each config on a small batch of requests or for a short duration) and then prunes the lower-performing half. The top fraction progresses to the next round with increased budget (e.g. longer test or larger batch)【28†L7-L15】. This halving continues until one or few configurations remain, which are then fully evaluated.
  • Hyperband generalizes SHA by running multiple brackets of different initial budget sizes and pruning schedules to be more robust. It starts many configurations with minimal resources and aggressively culls the field, allocating more to those that perform well early【28†L13-L21】. For example, Hyperband might begin by testing 81 configurations for 1 epoch (or a short time slice), then keep the top 9 to run for more epochs, and finally select the top 1 or 2 for a long run【28†L13-L21】. This approach “efficiently balances exploration and exploitation” and “scales well with large hyperparameter spaces”【29†L369-L377】 by focusing effort on the most promising settings. The result is a huge reduction in total evaluations – many bad configs are eliminated after minimal testing.

  One trade-off is that Hyperband requires a notion of resource and assumes that performance with fewer resources predicts performance with more. In LLM serving, a possible resource is the number of queries or time in a load test. A config that yields poor throughput in a 1-minute test likely won’t be top in a 10-minute test. However, careful setup is needed: “requires careful setting of resource allocation parameters”【29†L375-L383】 and may not capture configurations that only shine at full scale or under specific conditions (the assumption of monotonic performance with resource may not always hold). Despite this, in practice Hyperband and its variants have been very effective when partial evaluations correlate with final results.

• Asynchronous Successive Halving (ASHA): A practical improvement that allows parallel, asynchronous evaluations. Implemented in tools like Ray Tune, ASHA continuously schedules new trials and stops underperforming ones without waiting for synchronous rounds. This is well-suited to distributed clusters, maximizing resource utilization while still pruning experiments early.

• Combining Bayesian Optimization with Hyperband (BOHB): BOHB is a hybrid that leverages the quick pruning of Hyperband and the guided search of Bayesian optimization. It uses a TPE (Tree Parzen Estimator) surrogate to suggest configurations, but evaluates them with multi-fidelity scheduling (like Hyperband). This method “achieves the best of both worlds, starting fast and also converging to the global optimum quickly”【33†L103-L111】. Empirical results show BOHB often finds good solutions an order of magnitude faster than standard Bayesian Opt and converges to optimum far quicker than plain Hyperband【33†L123-L131】. In essence, BOHB avoids wasting time on poor configs (thanks to early-stopping) and smartly navigates the space of good configs (thanks to Bayesian modeling). For LLM serving, BOHB or similar approaches can dramatically cut down tuning time, by, say, quickly discarding thread counts or batch sizes that clearly underperform, and focusing the Bayesian search on the viable region of the configuration space.

• Adaptive Experimentation: Outside of formal algorithms, practitioners sometimes use adaptive heuristics to reduce evaluations. For example, if a certain setting (like a particular GPU memory configuration) consistently fails to meet latency requirements in initial tests, they may drop it from further trials. This informal successive elimination follows the same intuition: don’t waste full runs on losers.

Multi-fidelity approaches are especially powerful in real-world LLM serving because one can often simulate a “shorter” version of an experiment (fewer queries, smaller client load) to get a rough performance estimate. Using these as a filter means only a handful of configs ever get the full-scale, time-consuming treatment. The net effect is a dramatic reduction in total evaluation cost while still exploring a broad parameter space.

4. Evolutionary Algorithms and Population-Based Search

Another class of alternatives to Bayesian methods are evolutionary algorithms and related population-based search techniques. These include Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), and others inspired by natural evolution or swarm behavior. Key characteristics of these methods:

• Population of Solutions: Instead of a sequential model guiding one candidate at a time, evolutionary methods maintain a population of candidate configurations. For example, a GA might represent each LLM serving configuration as a “chromosome” (a vector of parameters like [batch size, #nodes, etc.]). An initial population of such vectors is evaluated in parallel. Based on their performance (fitness), a new generation of configurations is produced using crossover (mixing parts of two configs), mutation (randomly tweaking a parameter), and selection (carrying over the best performers).

• Exploration of Complex Landscapes: Evolutionary approaches are adept at exploring complex, high-dimensional search spaces. They do not assume any smoothness or simple structure in the objective function, which is useful if the performance landscape is rugged or has many local optima. GAs and related methods “navigate complex, high-dimensional search spaces” and are “good at escaping local optima”【29†L414-L422】. In scenarios where the optimal serving configuration might be a needle in a haystack of combinations (and small changes can have non-linear effects on throughput/latency), a population-based search can broadly explore the space.

• Parallel and Stochastic Search: By evaluating many individuals each generation, evolutionary algorithms naturally parallelize. This suits distributed testing environments – e.g., running multiple LLM instances concurrently to test different configs. Over successive generations, the population hopefully “evolves” toward better performance. The stochastic nature (random mutations, etc.) adds diversity, reducing the chance of getting stuck in one region early on.

Trade-offs and Challenges:
While flexible, evolutionary methods can be computationally intensive. They often require a large number of evaluations across many generations to converge to an optimum【29†L420-L427】. If each evaluation is expensive (as with LLM serving), a full GA run might be costly unless carefully managed or parallelized. There is also the matter of tuning the tuner – population size, mutation rate, crossover probability are hyperparameters for the GA itself, which affect its efficiency. Setting these poorly can lead to slow convergence or missed optima.

In practice, evolutionary algorithms are sometimes used when other methods fail to find good solutions or when the search space is highly discrete or constrained (where gradient-based or surrogate methods might struggle). For example, Differential Evolution (a specific evolutionary strategy) has been applied to tune machine learning hyperparameters and can outperform random search in some cases, but it still may require many function evaluations in high-dimensions. CMA-ES (Covariance Matrix Adaptation Evolution Strategy) is another evolution-inspired method known for strong performance on continuous optimization problems; it adapts the search distribution based on prior successful trials and can be effective even in moderately high dimensions without user-specified models.

For LLM serving, evolutionary approaches could be feasible if one can run many configurations in parallel (e.g., on a cluster of nodes) to evaluate each generation. Their strength is in not requiring a predictive model of the performance; they rely only on actual measurements and simple rules of evolution. This makes them robust but potentially slower to hone in on the optimal setting compared to model-guided methods. They might be most useful in conjunction with other techniques (for instance, using a GA to broadly explore and then a local search or Bayesian step to fine-tune the best few configurations).

5. Comparison of Methods: Efficiency, Scalability, and Applicability

Each optimization approach offers a different balance of search efficiency, scalability to many parameters/trials, and ease of use in real-world LLM serving environments. Here we compare these aspects across the alternatives:

• Search Efficiency (Evaluations to Find Optimum): Methods that incorporate intelligent guidance or pruning tend to find good configurations with fewer full evaluations:

  • Bayesian Optimization (GP-based): Very sample-efficient on low-to-medium dimensional problems – it often yields high-quality solutions in relatively few iterations by modeling the objective【13†L179-L187】. However, in very high dimensions or with many parameters, its performance can degrade as it needs more samples to build a good model (and the GP itself becomes heavy).
  • Random Search: Less efficient in terms of absolute evaluations required (no learning across trials), but each trial is independent so you can run many in parallel and stop whenever time/resources run out. It’s a good option when you can afford a moderate number of trials or when lack of time to set up more complex methods is a concern.
  • Hyperband/SHA: Extremely efficient in terms of “useful work per evaluation”. By cutting off poor performers early, it dramatically reduces wasted effort. Hyperband’s anytime performance is excellent – it finds reasonably good configs quickly by testing wide and shallow【33†L103-L111】. For example, if you had budget for 100 full evaluations, Hyperband might effectively explore hundreds of configs under partial budgets and still use <100 full evaluations equivalent. It may not always find the absolute best config (if some need full runs to reveal their quality), but it gives a very good one with far fewer full runs.
  • BOHB (Bayes + Hyperband): Arguably the most efficient of both worlds: it uses early-stopping to avoid wasting evaluations and a model-based strategy to choose promising candidates. As noted, it often outpaces plain BO by an order of magnitude in time【33†L123-L131】.
  • Evolutionary Algorithms: These usually require more raw evaluations to reach optimal solutions compared to Bayesian methods (which leverage a surrogate). The efficiency improves with parallelism – e.g., with a population of 20, one generation is 20 evaluations done in parallel (taking roughly the time of one evaluation if you have 20 workers). Over, say, 10 generations you use 200 evaluations – perhaps more than Bayesian would need – but you gained the ability to parallelize and explore broadly. If the evaluation budget is high and parallel resources are available, evolutionary methods can be viable; if evaluations are extremely limited, one of the model-based or multi-fidelity methods may reach a good solution with fewer tries.

• Scalability (Dimensions and Parallelism):

  • Dimensions: Random search and evolutionary algorithms scale naturally to high-dimensional spaces – they make no assumptions and their overhead grows linearly with number of parameters at worst. Bayesian methods with Gaussian processes scale poorly beyond a few dozen parameters due to model complexity, but variants like TPE and random forests handle higher dimensions more gracefully. Multi-fidelity methods require a notion of resource but not much additional overhead per parameter (though very complex parameter dependencies could confound the pruning heuristic). In summary, for 50+ dimensional problems, practitioners often turn to random, evolutionary, or non-GP Bayesian methods (like TPE) rather than classic GP Bayesian optimization【27†L285-L293】.
  • Parallelism: Random search is embarrassingly parallel. Evolutionary methods also parallelize well (a whole population per generation can be evaluated concurrently). Hyperband/ASHA are inherently parallel-friendly as different configs or brackets run simultaneously, and ASHA in particular is designed for distributed execution. Classic Bayesian optimization is sequential by nature (each suggestion uses info from previous results), but one can deploy parallel Bayesian opt by, for instance, generating multiple candidates from the acquisition function or using batch Bayesian optimization techniques. Still, the parallelism is limited because those candidates might be correlated. In practice, to utilize large clusters effectively, a combination of parallel search (random or evolutionary) with occasional model-guided updates can work better.

• Real-World Applicability:
  In real LLM serving scenarios, simplicity and reliability are important. Random search wins on simplicity – it’s trivial to implement and always works (no complex logic that could fail). Hyperband and its variants have been widely adopted in hyperparameter tuning services (e.g. Google Vizier’s tuning service and Amazon SageMaker offer Hyperband options, Ray Tune implements Hyperband/ASHA for distributed tuning) because they require minimal parameter tuning and provide robust gains in efficiency. Bayesian optimization (especially with TPE or other surrogates) is also common – tools like Optuna, Hyperopt, Scikit-Optimize, and Ax (Facebook’s Ax platform) make it fairly easy to apply Bayesian or quasi-Bayesian tuning on real problems, and these have been used for tuning large models and systems. Evolutionary algorithms are less common in off-the-shelf ML tuning but are used in specialized settings (e.g., neural architecture search has used evolutionary strategies; some hardware configuration optimizations use GA/PSO when search spaces are very irregular). They may require more custom setup or integration with existing evaluation pipelines.

• Maintenance and Tuner Overhead: Another practical point is the complexity of the optimization algorithm itself. Bayesian methods involve fitting models – this can introduce additional computational overhead (which is usually negligible compared to training a model or running an LLM inference workload, but it’s something to consider). Evolutionary methods and random search have very little overhead (mostly just bookkeeping of populations or random sampling). Hyperband requires managing resource allocation and trial scheduling, but frameworks can handle that. In an engineering context, the ease of using a method (or availability of a stable library) often guides the choice: if a team can plug in a library and get results, they will. Thus, methods like Hyperband/ASHA and Bayesian TPE (available in popular libraries) see a lot of real-world use, whereas a custom evolutionary tuner might only be used if the team has expertise or if other methods underperform.

In summary, Bayesian optimization (and its surrogate variants) are extremely sample-efficient but need careful handling of high dimensions; random and evolutionary methods scale and parallelize well but typically require more trials; Hyperband/ASHA provides a great practical balance by cutting off poor trials early, making it both efficient and scalable, which is why it’s prominent in real-world hyperparameter optimization for expensive tasks. Table below qualitatively summarizes these differences:

(NAS = Neural Architecture Search, where evolutionary methods have been used; PBT is included for context though it applies to training hyperparameters.)

6. Most Promising Approaches and Trade-offs in Practice

Considering the above, several approaches stand out for efficiently tuning LLM serving configurations:

• Multi-fidelity Optimizers (Hyperband/ASHA): Perhaps the most practically impactful technique. Their ability to exploit cheaper partial evaluations to weed out bad configurations makes them ideal when each full trial (e.g., a complete load test of an LLM service) is expensive. The trade-off is needing a reasonable proxy for performance at low resource – fortunately, in serving, shorter tests or smaller loads often do correlate with full-scale performance. These methods scale effortlessly to many parameters and many parallel workers, making them highly suitable for industry-scale problems. Real-world implementations: Ray Tune’s ASHA for distributed experiments, Google’s Vizier “Median Stopping Rule” (a similar early-stop heuristic), and Microsoft’s NNI Hyperband support. The trade-off is that if the proxy is misleading (some configs might only show their true colors on longer runs), Hyperband could prematurely stop what would have been the best config. Nonetheless, it’s usually a net win in efficiency.

• Model-Based Sequential Search (TPE/SMAC): When the number of experiments must be absolutely minimized (e.g., you only can run a few dozen full tests), using a surrogate model to guide the search is very powerful. TPE (as used in Hyperopt/Optuna) has shown strong performance across many tuning tasks and can handle continuous, discrete, and conditional parameters well. Random forest-based BO (SMAC) is also proven, particularly if the configuration space includes categorical options (e.g., choosing different serving strategies or libraries). These methods will choose each next configuration based on all past results, trying to intelligently balance exploring new regions and exploiting known good areas. In LLM serving, if one has prior data or strong beliefs about smoothness, model-based methods can zero in on optimal settings quickly. The trade-off is complexity and potential model error – the optimizer might get stuck exploring a region its model thinks is promising which in reality isn’t (model bias). They also may not leverage parallel resources as fully since suggestions are sequential. In practice, a common approach is to use these in combination with parallelism by generating a batch of suggestions at once or running multiple optimization runs in parallel with different seeds.

• Random Search (with analysis): For an initial sweep in a very uncertain, high-dimensional space, random search remains a robust choice. It’s simple and in high dimensions often as good as anything else for a first cut【17†L249-L257】. Its strength is in breadth – you’ll get a diverse sample of configurations that can reveal trends (e.g., “all the best runs had batch size > 16” or “using more than 4 nodes doesn’t help after a point”). In practice, random search is often the starting point, and the data from it can be fed into other algorithms (for example, use random search results to train a surrogate model or to identify important parameters via fANOVA). The trade-off, of course, is that purely random search might need a lot of trials to stumble upon the truly optimal config, and without guidance it doesn’t zoom in on the optimum. It should be paired with analysis or a second-phase optimizer for best results.

• Evolutionary Algorithms: These are promising primarily when the search space is very irregular or when one can leverage massive parallelism. Their advantage is robustness – they make few assumptions and can escape local optima through mutation and crossover. If LLM serving performance as a function of parameters is noisy or multimodal, evolutionary search might find good solutions that a greedy model-based search could miss. The real-world use often comes down to available resources: if you can run 100s of trials, an evolutionary approach (or even a simpler pattern search or simulated annealing) could be viable and will eventually produce a good result, albeit with possibly more total runs. The trade-off is efficiency – you pay with more evaluations, which might be acceptable if they run in parallel or if approximate evaluations are cheap.

• Hybrid and Adaptive Strategies: In practice, many times a hybrid approach works best. For example, Facebook’s Ax platform and Ray Tune allow combining techniques: you might start with random or Latin hypercube sampling to get a broad sense, apply Hyperband to quickly filter, and then use Bayesian optimization on the remaining region. Or use an evolutionary algorithm to coarsely explore and then switch to Bayesian local search for fine-tuning the best candidate found (sometimes called memetic algorithms when combining GA with local optimization). Another hybrid example is Meta-Learning or Transfer Learning for HPO: using knowledge from tuning on smaller models or previous workloads to set priors for Bayesian optimization, thereby speeding up convergence on the new task. These hybrids aim to mitigate the weaknesses of any single method and are quite promising, though they can be more complex to implement.

Trade-offs Summary: There is no one-size-fits-all – the best approach depends on the dimensionality of the problem, the cost per evaluation, and available compute resources. Bayesian methods (including TPE/SMAC) shine when evaluations are very costly and you need every trial to count (fewer, smarter trials). Multi-fidelity methods shine when you can evaluate incrementally and want to maximize the information gained per unit of computation. Evolutionary and random approaches shine when the search space is large and you have the ability to run many trials in parallel (or when simpler approaches are preferred for reliability). The table above and discussion provide a guide: for LLM serving configurations, which often involve a mix of continuous and discrete settings, a moderate number of important parameters, and expensive evaluation, a combination of early-stopping (Hyperband/ASHA) and model-guided search (TPE/BOHB) is often the most practical and efficient solution.

7. Practical Implementation Notes

To put these ideas into practice for LLM serving optimization, one can leverage existing libraries and tools:

• Ray Tune – a scalable hyperparameter tuning library that implements ASHA, PBT, Bayesian search (via Ax), and more. It’s suitable for distributed evaluation on a cluster, which matches LLM serving use cases (multiple configs can be tested on different nodes). Ray Tune’s docs even provide recipes for early stopping and population-based methods for tuning models and systems.

• Optuna – a user-friendly optimization library with TPE as the default sampler and support for multi-threaded or distributed execution. It also has pruning callbacks that can implement early stopping logic in training scenarios (for serving, one can manually incorporate early stopping decisions). Optuna’s visualization of hyperparameter importance can help apply fANOVA in practice to your study.

• Nevergrad (Facebook) and DEAP – for evolutionary strategies, these libraries provide a suite of optimizers (GA, ES, PSO, CMA-ES, etc.) that can be plugged into custom optimization loops. One can integrate Nevergrad to suggest new parameter sets and evaluate them on the LLM service, using whichever evolutionary algorithm suits the problem.

• Hyperopt – an older but still useful library for hyperparameter search that introduced TPE. It can do random search, TPE, and even annealing (simulated annealing). It’s lightweight and can be run in parallel via MongoDB as a backend, for example.

• Industrial Tuning Services: If using cloud platforms, consider managed services: e.g., Google Vizier (as part of Vertex AI) supports Bayesian optimization with automated early stopping; Amazon SageMaker Hyperparameter Tuning supports random, Bayesian, and Hyperband strategies. These services are built to handle large-scale experiments and can be adapted to tune system configs (not just model hyperparams) by defining the evaluation as a custom job.

When implementing, it’s important to define a clear objective (e.g., maximize throughput under a latency constraint, or minimize latency for a given throughput) for the optimizer to target. For multi-objective scenarios, one might integrate the constraint into the objective (as a penalty) or use specialized multi-objective optimizers (which is another layer of complexity). Most of the mentioned techniques can be extended or applied in a constrained optimization setting – e.g., run Bayesian optimization but reject proposals that violate SLA constraints, or have an evolutionary algorithm maintain only feasible solutions.

Finally, logging and analyzing results is crucial. Whichever method is used, keeping track of which configurations were tried and their performance allows human insight to complement the automated search. Often, the data from these optimization runs can reveal unexpected insights (perhaps a particular combination of settings yields an outlier performance, suggesting a deeper investigation into system behavior). This feedback can inspire new constraints or parameters to tune in subsequent runs.

8. Conclusion

Tuning LLM serving configurations efficiently is a challenging optimization task, but a rich toolbox of alternatives to plain Bayesian optimization exists. Random search offers simplicity and surprisingly strong performance in high dimensions, while evolutionary algorithms provide a robust way to explore complex search spaces. Multi-fidelity approaches like Hyperband dramatically improve efficiency by allocating effort wisely, and surrogate-model methods (TPE, random forest, etc.) guide the search to promising regions without brute force. The most promising strategies often combine techniques – for example, using early stopping to evaluate more configurations and a model-based approach to fine-tune the best ones.

Each method comes with trade-offs: some minimize the number of evaluations at the cost of more complex logic, others are simple but may need more trials, and some excel in parallel scaling but might miss subtle optima. In real-world LLM serving scenarios, a balanced approach tends to work best. By identifying the key parameters to tune, using algorithms suited for high-dimensional search, and leveraging early feedback to prune the search, practitioners can significantly reduce tuning time and cost. The end result is an optimized serving setup found in a fraction of the attempts a naive search would require, enabling LLM deployments to meet performance targets more quickly and reliably.

Overall, moving beyond basic Bayesian optimization to these advanced (and complementary) techniques can lead to more efficient exploration of the configuration space, ensuring that each expensive experiment yields maximal insight and inching closer to the optimal LLM serving performance with minimal waste. The combination of theoretical efficiency and practical tooling makes this a fruitful area for anyone aiming to squeeze the most out of large language model serving infrastructure.