--- title: "A Practical Guide to LLM Model Naming Conventions" description: "Learn how to read LLM naming conventions and choose the right model for your hardware. Discover what Q4, INT8, GPTQ, and GGUF mean and why models appear in multiple variants." url: https://www.callstack.com/blog/how-quantization-and-naming-conventions-shape-llm-deployment updated: 2026-10-24T08:39:30.673Z --- # A Practical Guide to LLM Model Naming Conventions Learn how to read LLM naming conventions and choose the right model for your hardware. Discover what Q4, INT8, GPTQ, and GGUF mean and why models appear in multiple variants. _By Lech Kalinowski, Senior AI System Engineer @ Callstack · Published 2026-03-18 · Category: AI_ _“Which model should I download?”_ _“Is_ `q4_k_m` _better than_ `int8`_?”_ _“Why does the same model exist ten times?”_ These are questions developers often ask when browsing **Hugging Face** and when they encounter a model released in multiple variants. At first glance, the list looks overwhelming: multiple sizes, quantization levels and formats, all for _one_ model family. This article explains why this is intentional, how to interpret these model names, and why lower quantization levels still matter, even when higher precision appears better. We’ll also take a deeper look at what quantization actually is, what these configuration names mean, and how different quantization setups impact model performance and hardware requirements. ## The moment of confusion: one model, many names For example, when searching for a model like _Bielik 7B,_ you might see something like: - `bielik-7b-fp16` - `bielik-7b-int8` - `bielik-7b-q4` - `bielik-7b-q4_k_m` - `bielik-7b-q5` - `bielik-7b-gptq` - `bielik-7b-gguf-q4` - `bielik-7b-gguf-q8` It _feels_ like ten different models. In reality, this is **one core model** published as **multiple deployment variants**. ## How to read a model name Most modern LLM names follow this structure: ```plaintext --- ``` Using Bielik as an example: | Part | Example | Meaning | | ------------ | -------------- | ---------------------------- | | Model family | bielik | Base architecture & training | | Size | 7b | 7 billion parameters | | Precision | fp16, int8, q4 | Numerical precision | | Runtime | gguf, gptq | How the model is executed | ## What Q4, Q8, and Q16 mean in practice Quantization controls **how many bits are used to store each model weight**: | Label | Bits | What it Optimizes | | ---------- | ------ | ------------------------ | | FP16 / Q16 | 16-bit | Maximum quality | | INT8 / Q8 | 8-bit | Best cost–performance | | Q4 | 4-bit | Minimal memory footprint | In practice, **quantization** means reducing the precision of the numbers that represent the model’s weights and sometimes activations. Instead of storing each weight as a 16-bit or 32-bit floating-point number, we compress it into a lower-bit format such as 8-bit, 6-bit, or 4-bit integers. The structure of the neural network, its learned patterns, and its training data do not change. The only part that does change is how precisely those learned parameters are stored and computed. However, quantization introduces approximation error. When weights are compressed into fewer bits, small numerical differences are rounded. Individually, these errors are tiny, but across billions of parameters they can accumulate and slightly distort probability distributions during inference. Well-designed quantization schemes preserve most of the model’s capability while drastically reducing hardware requirements. In many practical scenarios, the trade-off is worth it, especially when deployment constraints matter more than marginal accuracy gains. ## Why a model like Bielik publishes so many variants It is common for models like **Bielik** to be released in multiple variants. This does not mean the model is unstable or experimental. In fact, multiple variants are a normal part of modern model distribution. The real reason is **inference hardware constraints**. Different environments require different trade-offs between memory usage, latency, and quality. A research lab running GPUs with large VRAM can serve a full-precision model, while local deployments or CPU environments may require aggressive quantization to reduce memory usage. Because of this, the same base model is often published in several formats, such as FP16, INT8, or 4-bit quantized versions, so it can run efficiently across different hardware setups, from high-end GPUs to consumer laptops. In other words, model variants exist not because the model is unstable, but because **deployment environments vary dramatically**. It publishes many variants because inference on specific hardware is the real bottleneck. | Name | What It Actually Is | Why It Exists | | -------- | ------------------------------------------------- | ------------------------------------------------------ | | FP16 | 16-bit floating point weights | Reference quality, baseline evaluation, high-VRAM GPUs | | INT8 | 8-bit quantized weights (various implementations) | Reduce memory \~2× while keeping strong quality | | Q4 / Q5 | Generic 4-bit / 5-bit quantization | Run large models on consumer hardware | | Q4\_K\_M | GGUF (llama.cpp) mixed 4-bit scheme | Better stability than plain Q4 in CPU inference | | GPTQ | Post-training GPU quantization algorithm | Fast 4-bit GPU inference with minimal accuracy drop | | GGUF | Model file format for llama.cpp | Efficient CPU-friendly distribution format | Q16 offers excellent quality but requires GPUs with large VRAM and higher infrastructure costs. In contrast, lower quantization levels address environments where memory bandwidth is the bottleneck, CPUs dominate deployments, edge and on-device AI are required, and infrastructure costs scale nonlinearly. ## Quantization levels are **deployment tiers** The tiers above represent practical deployment environments, not model quality rankings. Each tier corresponds to a different hardware constraint and operational goal. | Deployment Tier | Typical Precision | Target Hardware | Memory Impact | When to Use | | --------------------------------- | ----------------------------- | ----------------------- | ------------- | -------------------------------------------- | | **Tier 0 – Research / Reference** | FP16 / BF16 | Multi-GPU (A100, H100) | Very High | Benchmarking, fine-tuning, maximum fidelity | | **Tier 1 – High-End GPU** | INT8 / GPTQ 8-bit | RTX 4090 / A6000 | High | Production GPU inference with strong quality | | **Tier 2 – Consumer GPU** | GPTQ 4-bit / AWQ | 12–24GB GPUs | Medium | Fast inference with good reasoning retention | | **Tier 3 – CPU / Laptop** | Q4\_K\_M / Q5\_K\_M (GGUF) | Modern CPU, 16–32GB RAM | Low | Local inference without GPU | | **Tier 4 – Constrained / Edge** | IQ3 / IQ2 / Q4 small variants | Low-RAM systems | Very Low | Max compression, acceptable degradation | The key takeaway is that quantization levels are not competing versions of a model. They are deployment adaptations of the same model designed for different hardware environments. A research team may run the FP16 version on large GPUs to achieve maximum numerical fidelity. A production system might deploy an INT8 or 4-bit quantized version to reduce GPU memory usage and increase throughput. Meanwhile, developers experimenting locally often rely on GGUF variants that allow the same model to run entirely on a CPU. In other words, quantization creates a hardware compatibility ladder. Each step trades a small amount of numerical precision for improvements in memory footprint, cost, or latency. Understanding this helps avoid a common misconception: the “best” quantization level is not universal. The best version is simply the one that **fits** your hardware and performance requirements. ## Conclusions When developers ask _“Which Bielik model should I use?”_, the correct answer is: Tell me your hardware, latency target, and cost constraints. The naming convention already encodes the rest.