LLM parameters: A complete guide to model evaluation and optimization

Picture your agent deploys to production, but suddenly starts generating outputs that diverge from expected behavior and exposing sensitive data. The suspected culprit? A temperature setting of 1.0 instead of 0.2. While output consistency does correlate with lower temperature settings, research demonstrates that temperature variation (0.0-1.0) produces no statistically significant differences in problem-solving accuracy. This suggests the relationship between temperature and output quality is more nuanced than commonly assumed.

LLM parameters—the millions to billions of internal values that define how language models process and generate text—directly impact your business outcomes. These parameters range from 1 billion in optimized edge models like Llama 3.2 to 405 billion in flagship systems like Llama 3.1. Proprietary models like GPT-4 and Claude withhold precise parameter counts while disclosing context windows (128K-200K tokens). Whether your AI applications deliver reliable results or create business-critical failures depends on understanding these parameter specifications.

Even small parameter adjustments can cascade into system-wide failures. Peer-reviewed research reveals that temperature variation from 0.0 to 1.0 produces no statistically significant effect on problem-solving accuracy. This challenges common assumptions about parameter optimization priorities.

In this guide, we explore core LLM parameters, their impact on model behavior, and practical strategies for evaluation and optimization.

TLDR:

• LLM parameters range from 1B to 405B+; small models need aggressive tuning, large models don't

• Context windows standardized at 128K-256K tokens; Google Gemini offers 1M tokens

• Temperature ranges differ: GPT-4 uses 0-2, Claude/Llama use 0-1

• Temperature variation shows no significant accuracy effect; prompt engineering reduces hallucinations 33%

• Focus optimization on small models; use RAG and prompt engineering for quality gains

• Use parameter version control to prevent production incidents

What are LLM parameters?

LLM parameters are fundamental components that define how a large language model processes and generates text. These internal values, numbering from 1 billion to over 400 billion, are learned during training and collectively determine the model's behavior.

Key parameters fall into several categories:

• Architectural parameters include model size and context window. Context windows have standardized around 128K-256K tokens across providers. Google Gemini uniquely offers up to 1 million tokens.

• Generation parameters like temperature and top-p jointly control output characteristics. For customer service and fact-based Q&A, use lower temperature (0.1-0.3) and restrictive top-p (0.1-0.3). For creative tasks, higher temperature (0.7-0.9) and broader top-p (0.8-0.95) increase diversity.

• Sampling parameters such as top-k and top-p influence token selection. Setting top-k=50 limits selection to the 50 most probable tokens. Top-p=0.9 includes only tokens comprising 90% of the probability mass.

Parameter optimization effects vary dramatically by model size. Small models (≤7B parameters) show up to 192% performance variation with parameter tuning. Large models (>30B parameters) show less than 7% variation.

A critical consideration: open-source models like Meta Llama provide complete parameter specifications. Proprietary models systematically withhold parameter counts and architectural details.

Model architecture parameters

Understanding model architecture parameters is essential for capacity planning and deployment decisions. These foundational values determine a model's computational requirements and capabilities.

Hidden size (embedding dimension)

Hidden size determines the dimensionality of the model's internal representations. This parameter controls how much information the model can encode at each layer. While proprietary models like GPT-4 and Claude do not disclose specific dimensions, larger hidden sizes generally enable richer semantic representations but increase memory requirements linearly.

Enterprise teams must weigh representational power against deployment constraints when selecting models. Open-source models provide full architectural specifications, enabling precise capacity planning.

Number of layers (depth)

Transformer layers stack sequentially to process information at increasing levels of abstraction. Larger models use significantly more layers than smaller ones, enabling sophisticated multi-step reasoning. Model depth directly affects reasoning capacity and computational requirements.

Each additional layer adds computational overhead and memory requirements. Deeper models excel at complex tasks but require more powerful infrastructure.

Attention heads

Multi-head attention enables the model to process different relationship types simultaneously. Each attention head learns distinct patterns in the input sequence. The number of attention heads scales with model size, with larger models employing more heads for parallel relationship processing.

More attention heads allow parallel processing of semantic, syntactic, and positional relationships. However, diminishing returns occur beyond certain thresholds relative to model size.

Vocabulary size

Tokenizer vocabulary size determines how efficiently the model represents text. Modern LLMs typically use 32K-128K token vocabularies. Larger vocabularies reduce sequence lengths for the same text but increase embedding table memory.

Claude and GPT-4 use vocabulary sizes in the ~100K range for broad language coverage, while open-source models publish exact specifications.

Note: Architectural parameters vary significantly by model. Open-source models like Llama provide complete specifications, while proprietary models (GPT-4, Claude) do not disclose hidden size, layer count, or attention head details.

Training parameters and their implications

Training parameters govern how models learn from data. Understanding these values helps teams evaluate model quality and plan fine-tuning strategies.

Learning rate and scheduling

Learning rate controls how quickly model weights update during training. Typical pre-training uses rates of 1e-4 to 3e-4 with warmup periods. Warmup gradually increases the learning rate over initial steps to stabilize training. Decay schedules then reduce the rate to fine-tune learned representations.

Fine-tuning requires much smaller learning rates (1e-5 to 5e-5) to preserve pre-trained knowledge. Evaluating fine-tuned models requires tracking how learning rate choices affect downstream performance.

Batch size considerations

Batch size impacts training stability, memory requirements, and convergence speed. Larger batches provide more stable gradient estimates but require more memory. Pre-training typically uses large effective batch sizes through gradient accumulation to improve training stability.

Fine-tuning typically uses smaller batches (8-64 samples) due to limited hardware. Memory requirements scale linearly with batch size, affecting infrastructure costs.

Training tokens and data scale

Pre-training data scale directly correlates with model capability. Modern LLMs train on trillions of tokens across diverse sources, with larger training datasets generally correlating with broader knowledge and more robust language understanding.

Enterprise teams should consider training data composition when selecting models for domain-specific applications. Models trained on more recent data may perform better on current topics.

Fine-tuning vs. pre-training parameters

Fine-tuning uses dramatically different parameter settings than pre-training. Lower learning rates prevent catastrophic forgetting of pre-trained knowledge. Shorter training runs (hundreds to thousands of steps) suffice for domain adaptation.

Parameter-efficient methods like LoRA reduce trainable parameters by 99% while maintaining performance. This enables fine-tuning on consumer hardware rather than expensive GPU clusters.

Key LLM performance parameters

Understanding fundamental parameters is crucial for effective model deployment. These parameters directly impact performance, resource utilization, and output quality.

Core inference parameters

• Temperature: Controls randomness of predictions. Temperature=0.0 always selects the most probable token. Temperature=0.7 creates more diverse outputs. According to Google Cloud, temperature is applied first, followed by Top-K filtering, then Top-P filtering.

• Top-k sampling: Limits the model to consider only the top k probable tokens. Setting k=40 restricts choices to the 40 most likely tokens.

• Top-p (nucleus) sampling: Considers tokens with cumulative probability above threshold p. Using p=0.95 includes only tokens whose combined probability reaches 95%. According to OpenAI, alter either temperature or top-p, not both simultaneously.

Memory and precision

Memory requirements follow: Memory (GB) = Parameters (B) × 2GB × 1.2 for FP16 precision. The 1.2 multiplier accounts for KV cache and activation buffers.

Llama 3.1 70B requires approximately 168GB VRAM in FP16 but only 42GB with 4-bit quantization—a 75% reduction. Mixture-of-Experts models like Mistral Large 3 require VRAM for all 675 billion parameters, not just the 41 billion active parameters.

Different precision levels serve different deployment needs. Understanding these trade-offs enables optimal infrastructure planning.

Model size examples at different precisions:

Choose FP16/BF16 for quality-critical applications where infrastructure supports it. Use INT8 for balanced cost-performance in standard deployments. Reserve INT4 for edge devices or cost-constrained high-volume applications where slight quality degradation is acceptable. Galileo's observability tools help track quality metrics across precision configurations.

Repetition penalties

Repetition penalty prevents redundant phrases. Values of 1.2 apply moderate discouragement. Note: According to GitHub issue tracking, repetition_penalty may cause inference issues with Llama 3.2 models.

Cross-model parameter configuration differences

Understanding parameter behavior across GPT-4, Claude 3.5, Llama 3, and Mistral is essential. Critical differences exist in supported parameters, value ranges, and requirements.

Key migration considerations:

• GPT-4's temperature range (0-2) is 2x wider than Claude/Llama (0-1)

• Claude requires explicit max_tokens for every request

• According to Spring AI, avoid modifying both temperature and top_p simultaneously for Mistral

Parameters' impact on LLM performance

Temperature and sampling effects

Research published in ACL Anthology tested Claude 3 Opus, GPT-4, Gemini Pro, Llama 2, and Mistral Large. The finding: changes in temperature from 0.0 to 1.0 do not have a statistically significant effect on problem-solving performance.

However, temperature impact varies dramatically by model size. Research from arXiv reveals:

Small models (≤7B parameters):

• Performance variation reaches 192% for machine translation tasks

• 186% variation for creativity tasks

• Requires aggressive parameter tuning

Large models (>30B parameters):

• Show less than 7% variation across temperature ranges

• Parameter optimization is lower priority

This has significant implications for agent evaluation: allocate optimization resources inversely to model size.

Hallucination mitigation

Critical finding: PMC research testing 5,400 clinical prompts found temperature reduction alone offered zero measurable benefit in reducing adversarial hallucinations. Targeted mitigation prompts reduced hallucinations to 44.2%—a 33% relative reduction.

For high-stakes applications, prompt engineering and RAG provide orders of magnitude greater risk reduction than temperature optimization alone.

Systematic parameter tuning methodology

Effective parameter optimization requires a structured approach rather than random experimentation. Follow this methodology to achieve consistent improvements.

Step 1: Establish baselines

Before tuning, document your current configuration and performance metrics. Measure accuracy, latency, cost per request, and user satisfaction scores. These baselines enable objective comparison of parameter changes. Use AI Observability AI Observability Tools like Galileo to capture comprehensive baseline metrics.

Step 2: Identify optimization targets

Determine which metrics matter most for your use case. Customer support applications prioritize accuracy and consistency. Creative applications may prioritize diversity and engagement. Cost-sensitive deployments focus on throughput and token efficiency.

Step 3: Design controlled experiments

Change only one parameter at a time during testing. Use A/B testing frameworks to route traffic between configurations. Ensure statistical significance by running tests with sufficient sample sizes. Minimum 1,000 requests per configuration typically provides reliable results.

Step 4: Monitor key metrics during optimization

Track these metrics throughout your optimization process:

• Latency percentiles (p50, p95, p99) to catch tail latency issues

• Accuracy scores against your evaluation dataset

• Cost per successful request to measure efficiency

• User satisfaction through feedback collection

Step 5: Determine when to optimize parameters vs. alternatives

Parameter tuning provides diminishing returns in many scenarios. Consider alternatives when:

• Accuracy issues stem from knowledge gaps → implement RAG

• Output format problems persist → refine prompts

• Safety concerns arise → add guardrails

• Domain expertise is lacking → fine-tune the model

Focus parameter optimization on latency, cost, and consistency objectives where it has the greatest impact.

Production troubleshooting framework

Production LLM troubleshooting requires systematic diagnostic frameworks. Critical principle: never rely on parameter defaults—explicitly configure all inference parameters.

Common failure patterns and solutions

Parameter optimization decision tree

1. Output consistency required? → Temperature 0.0-0.3, top_p 0.7-0.8

2. Creative vs. factual task? → Factual: temp 0.3-0.5, RAG enabled; Creative: temp 0.7-1.0

3. Repetition issues? → Frequency penalty 0.3-0.8

4. Latency critical? → Reduce max_tokens, disable beam search

5. Output length issues? → Adjust max_tokens and stop sequences

Production best practices

• Explicitly set all parameters across environments

• Document deviations between development and production

• Version parameter sets alongside code deployments

• Test with production parameters in staging

Cost optimization through parameter tuning

API pricing ranges from $0.15 to $75 per million output tokens—a 500x difference. Output tokens cost consistently 4-6x more than input tokens.

Key cost reduction strategies

• Quantization: int4 provides 2.07x throughput improvement

• Context caching: 50-96% discount on repeated inputs (Google offers $0.05 vs $1.25 per million)

• Fine-tuning: 60-90% per-query cost reduction for domain-specific tasks

• Output optimization: Managing output length provides highest ROI due to 4-6x cost multiplier

Use case-specific baselines

Industry-specific considerations

Healthcare: Implement privacy-preserving metadata-only AI patterns with comprehensive audit trails for HIPAA compliance. Public LLMs are not HIPAA-compliant without enterprise BAA arrangements.

For medical applications, use temperature settings of 0.0-0.2 to maximize consistency in diagnostic support scenarios. The PMC research on clinical hallucinations demonstrates that architectural controls matter more than temperature for safety. Implement Galileo’s Runtime Protection to prevent harmful outputs in patient-facing applications.

Always validate outputs against clinical guidelines before deployment. Maintain complete audit trails tracking every inference for regulatory compliance.

Financial services: Apply existing supervision and recordkeeping rules to AI tools. Treat AI governance as ongoing operational practice, not one-time policy update.

Deterministic outputs are often required for audit and compliance purposes. Use temperature=0 with fixed seeds to ensure reproducible results. Document all parameter configurations as part of your compliance record. Track parameter changes through version control systems to demonstrate governance.

Government: Meet FedRAMP standardized security assessment and continuous monitoring requirements for cloud services.

On-premises deployment requirements affect parameter optimization strategies significantly. Self-hosted models enable complete parameter control but require substantial infrastructure investment. Evaluate whether cloud providers with FedRAMP authorization meet your security requirements while providing parameter flexibility.

Optimize your LLMs and agents with Galileo

Parameter tuning alone provides limited impact for most enterprise objectives. Production reliability requires integrated strategies combining prompt engineering, RAG, systematic observability, automated evaluation frameworks, and guardrails.

Enterprise teams require systematic approaches to configure, monitor, and optimize AI systems throughout the development lifecycle:

• Automated evaluation of LLM outputs: Assess quality dimensions including correctness, toxicity, and bias through systematic evaluation frameworks

• Quality guardrails in CI/CD pipelines: Implement comprehensive evaluations with automated metric thresholds

• Production monitoring and observability: Monitor prompt quality, response quality, and performance metrics with Galileo Observe

• Systematic failure analysis: Identify patterns in model failures through continuous evaluation workflows

• Continuous refinement through human feedback: Implement iterative improvement cycles

Start evaluating your LLM parameters with Galileo →

Frequently asked questions

What are LLM parameters and why do they matter?

LLM parameters are internal values (1 billion to 405+ billion) that define how a language model processes and generates text. They directly impact output quality, consistency, cost, and latency. Proper configuration determines whether AI applications deliver reliable results or create failures.

How do I choose the right temperature setting for my use case?

For factual Q&A and data extraction, use temperature 0.0-0.3. For creative content, use 0.7-1.0. However, research shows temperature variation has no significant effect on accuracy for large models. Small models (<7B) show up to 192% variation, requiring careful tuning. Prompt engineering provides greater quality improvements than temperature optimization alone.

What's the difference between top-k and top-p sampling?

Top-k limits selection to a fixed number of most probable tokens (e.g., top-k=40). Top-p dynamically selects tokens until cumulative probability reaches a threshold (e.g., top-p=0.95). OpenAI recommends modifying one or the other, not both simultaneously.

How can I reduce LLM inference costs through parameter optimization?

Focus on output token reduction (4-6x more expensive than inputs). Leverage cached input discounts (50-96% savings). Implement int4 quantization (2.07x throughput improvement). Use multi-model routing to match task complexity with appropriately-sized models.

How does Galileo help with LLM parameter optimization?

Galileo provides automated experiment tracking for optimal parameter configurations, cost-effective quality assessment, real-time safeguards to prevent parameter-induced failures, and continuous improvement mechanisms based on domain-specific requirements and human feedback.