Introduction: The Importance of LLM Inference Choices

Large Language Models, built on transformer architectures, have become the backbone of modern AI. Models like GPT-4, Claude, and open-source alternatives power everything from virtual assistants to automated content generation. However, deploying these models for inference is not a one-size-fits-all process. The choice of base model, its size, quantization level, and fine-tuning approach significantly impacts performance, cost, and feasibility on specific hardware.

Inference involves running a trained LLM to generate outputs, such as text or code, based on input prompts. The goal is to balance performance (accuracy, coherence, and task-specific capabilities) with efficiency (speed, memory usage, and energy consumption). With dozens of base models and countless variations, understanding the trade-offs is crucial.

"Deploying LLMs for inference requires navigating a complex landscape of choices, from selecting the right base model to optimizing for your specific hardware constraints."

This article explores over a dozen base models (including Llama, Gemma, Phi, Mistral, DeepSeek, and others), techniques to reduce model size, quantization methods, and fine-tuning approaches. Whether you're a developer, researcher, or AI enthusiast, this guide will help you make informed decisions for your LLM deployment.

Base Models: The Starting Point for LLM Inference

The base model is the pre-trained LLM that serves as the starting point for inference or further customization. Each model has unique characteristics, such as parameter count, training data, architecture, and intended use cases. Let's explore over a dozen prominent base models.

Fig 1: Comparison of popular LLM base models by parameter size and capabilities

Llama Series (Meta AI)

The Llama family, developed by Meta AI, is a cornerstone of open-source LLMs. Models like Llama 3.1 (8B, 70B, 405B parameters) and Llama 3.2 (1B, 3B) are optimized for research and commercial use under permissive licenses. Llama models excel in natural language tasks, with larger variants offering superior performance in reasoning and generation, while smaller ones are ideal for edge devices.

Gemma Series (Google)

Google's Gemma models, including Gemma 2 (9B, 27B) and Gemma 3 (1B, 4B, 12B, 27B), are lightweight, open-source LLMs designed for efficiency. Built on the same research as Google's Gemini models, Gemma supports multimodal inputs (text, vision) and excels in tasks like summarization and reasoning. The quantization-aware training (QAT) variants of Gemma 3 reduce memory usage while preserving quality.

Phi Series (Microsoft)

Microsoft's Phi models, such as Phi-3 (3.8B, 14B) and Phi-4 (multimodal), are compact yet powerful. Phi-3 Mini, with 3.8B parameters, supports a 128k-token context length and outperforms many larger models in reasoning tasks. Phi-4 introduces multimodal capabilities, processing text, images, and audio. These models are ideal for resource-constrained environments.

Mistral Series (Mistral AI)

Mistral AI's models, including Mistral 7B, Mixtral 8x7B, and Mistral Large 2, are known for efficiency and performance. Mistral 7B is a dense model suitable for general tasks, while Mixtral 8x7B, a Mixture-of-Experts (MoE) model, activates only a subset of parameters (e.g., 12B per token) for faster inference. Mistral Large 2 competes with proprietary models like GPT-4.

DeepSeek Series (DeepSeek AI)

DeepSeek, a Chinese AI company, offers models like DeepSeek V3 (671B, MoE) and DeepSeek R1 (distilled variants at 1.5B, 8B, 70B). DeepSeek V3 uses Multi-head Latent Attention for efficient inference, while DeepSeek R1, trained via reinforcement learning, rivals OpenAI's o1 in reasoning tasks. These models are open-source and support commercial use.

Other Notable Base Models

• Qwen Series (Alibaba): Multilingual models supporting 29+ languages, excelling in mathematical reasoning and coding.
• Grok Series (xAI): Designed for conversational tasks and reasoning, with Grok 3 Mini optimized for low-latency inference.
• Command R (Cohere): Optimized for retrieval-augmented generation (RAG) with a 128k-token context length.
• StarCoder 2 (BigCode): Code-specific LLM supporting 338 programming languages.
• Falcon (TII): Open-source models trained on massive datasets for general-purpose tasks.
• Yi Series (01.AI): Designed for efficiency and multilingual capabilities.
• CodeLlama (Meta AI): Specialized for code generation with up to 128k-token context.

Size Variations: Shrinking Models for Efficiency

LLMs often have billions of parameters, requiring significant memory and computational power. To make them viable for smaller GPUs or edge devices, developers create size-reduced variants through several techniques.

Pruning

Pruning removes less critical parameters or layers from a model, reducing its size while aiming to preserve performance. For example, pruning Llama 3.1 70B can yield a smaller model (e.g., 40B equivalent) with minimal accuracy loss. Pruning is less common than other methods but is gaining traction for edge deployment.

Knowledge Distillation

Knowledge distillation transfers knowledge from a large "teacher" model to a smaller "student" model. DeepSeek R1 Distill models (e.g., 1.5B, 8B) are distilled from DeepSeek R1, retaining strong reasoning capabilities in a compact form. Distilled models like Gemma 3 1B or Qwen 1.5B are optimized for speed and low memory usage.

Fig 2: Knowledge distillation process from large to small models

Model Scaling

Many model families offer scaled-down variants. For instance, Llama 3.2 includes 1B and 3B models alongside 70B and 405B versions. Similarly, Mistral's Ministral 3B and 8B cater to lightweight inference, while Gemma 3 ranges from 1B to 27B. These smaller models are designed for specific hardware constraints, such as GPUs with 4–8 GB VRAM.

Mixture-of-Experts (MoE)

MoE models, like Mixtral 8x7B or DeepSeek V3, activate only a subset of parameters per token, reducing computational load. For example, DeepSeek V3 (671B total, 37B active) achieves efficiency comparable to smaller dense models. MoE is ideal for balancing performance and resource usage.

Quantization: Compressing Models for Speed and Efficiency

Quantization reduces the numerical precision of a model's weights and activations, shrinking its memory footprint and accelerating inference. Common quantization levels include 8-bit, 4-bit, and even 2-bit, with trade-offs in performance and quality.

Post-Training Quantization (PTQ)

PTQ applies quantization after training, converting weights from 32-bit floating-point (FP32) to lower-precision formats like INT8 or INT4. For example, Llama 3.3-70B Turbo uses FP8 quantization for faster inference with minimal accuracy loss. PTQ is simple but may degrade performance slightly.

Quantization-Aware Training (QAT)

QAT integrates quantization during training, allowing the model to adapt to lower precision. Gemma 3 QAT models (1B, 4B, 12B, 27B) reduce memory usage (e.g., 27B from 54GB to 14.1GB) while maintaining quality. QAT outperforms PTQ but requires more computational resources during training.

Common Quantization Formats

• GGUF: Used by llama.cpp, GGUF supports 4-bit (Q4_K_M) and 5-bit (Q5_K_M) quantization for efficient inference on consumer hardware.
• GPTQ/AWQ: These formats, supported by vLLM, optimize for GPU inference. GPTQ is used in models like Mixtral-8x7B-Instruct-v0.1-GPTQ.
• Bitsandbytes: Enables 4-bit and 8-bit quantization for models like Phi-2, reducing VRAM usage (e.g., Phi-2 from 5.19GB to 1.72GB).

Impact of Quantization

Quantization significantly reduces memory and speeds up inference. For instance, Llama-3.2-1B predictions are 4x faster in 16-bit than 32-bit, with 4-bit quantization offering further gains. However, aggressive quantization (e.g., 2-bit) can introduce syntax errors or performance degradation, as seen in CodeGemma.

Fine-Tuning: Tailoring Models for Specific Tasks

Fine-tuning adapts a pre-trained LLM to a specific task or domain, improving accuracy and relevance. Common fine-tuning methods include supervised fine-tuning (SFT), reinforcement learning, and parameter-efficient techniques.

Supervised Fine-Tuning (SFT)

SFT trains the model on a labeled dataset for a specific task, such as text classification or dialogue. For example, Llama 3.1 8B was fine-tuned for instruction-following, enhancing its conversational abilities. SFT is effective but requires substantial data and compute.

Reinforcement Learning from Human Feedback (RLHF)

RLHF aligns models with human preferences using reward models. DeepSeek R1 uses RLHF to boost reasoning capabilities, achieving performance comparable to OpenAI's o1. RLHF is computationally intensive but excels in tasks requiring nuanced outputs.

Fig 3: Reinforcement Learning from Human Feedback process

Parameter-Efficient Fine-Tuning (PEFT)

PEFT methods, like LoRA and QLoRA, update only a small subset of parameters, reducing resource demands. QLoRA, used in fine-tuning Llama 2 7B for travel chatbots, combines 4-bit quantization with LoRA for memory efficiency. PEFT is ideal for resource-constrained environments.

Retrieval-Augmented Fine-Tuning (RAFT)

RAFT integrates retrieval-augmented generation (RAG) into fine-tuning, enabling models to access external knowledge bases. Mistral 7B fine-tuned with RAFT for travel applications outperforms baselines in domain-specific tasks.

Challenges in Fine-Tuning

Fine-tuning can lead to catastrophic forgetting, where the model loses general capabilities. Mixing safety data with fine-tuning data mitigates this risk. Additionally, reproducibility challenges arise due to random elements in training, as seen in Llama fine-tuning experiments.

Comparing Models and Variations: A Practical Perspective

To illustrate the trade-offs, consider a scenario where you need an LLM for a multilingual chatbot on a 12GB GPU:

The choice of model and optimizations depends heavily on your specific requirements, hardware constraints, and quality expectations. For instance, if your application needs to run on mobile devices, you might prioritize models like Phi-3 Mini or MiniCPM-2.6 with aggressive quantization.

Recent Trends and Results

Recent advancements highlight the dynamic LLM landscape:

• DeepSeek R1: Achieves OpenAI o1-level reasoning via RL, with distilled variants outperforming larger models.
• Gemma 3 QAT: Reduces memory usage by up to 74% while maintaining quality.
• Mistral Large 2: Matches GPT-4 performance in benchmarks, with MoE variants like Mixtral 8x22B leading in efficiency.
• Llama 4 Scout: Supports a 10M-token context length, ideal for long-document processing.

These trends underscore the push toward efficiency, reasoning capabilities, and task-specific optimization in the LLM space.

Conclusion: Making the Right Choice

Choosing an LLM for inference involves balancing performance, efficiency, and task requirements. Base models like Llama, Gemma, Phi, Mistral, DeepSeek, and others offer diverse starting points. Size variations and quantization make these models accessible on consumer hardware, while fine-tuning tailors them to specific needs.

By understanding these options—over a dozen base models, multiple size reduction techniques, quantization formats, and fine-tuning methods—you can deploy LLMs effectively for your use case. The right combination of model, size, quantization, and fine-tuning will unlock the full potential of LLMs while working within your hardware constraints.

As the field evolves, tools like LM Studio, vLLM, and LLaMA-Factory simplify experimentation with these models. Whether you're building a chatbot, coding assistant, or research tool, the landscape of LLM inference choices offers unprecedented flexibility and power—if you know how to navigate it.