Quantization Comparison: Q4, Q5, Q8 in Practice

What This Article Covers

• How quantization levels Q4_K_M, Q5_K_M, Q6_K, and Q8_0 differ in real-world scenarios
• The impact of quantization on quality, speed, and memory usage
• A practical comparison table using the same model across different quantization levels
• When to choose each quantization level
• How to find the sweet spot between quality and memory requirements

Introduction

Quantization is the key technique for running large AI models on consumer hardware. But which level should you pick? Q4_K_M is the standard recommendation, but is Q5_K_M really that much better? Is Q8_0 worth it if you have the VRAM? And what about Q6_K?

Anyone running local AI faces these questions. The theory behind quantization is covered in depth in Quantization. This article takes a step further: it compares these levels directly in practice and helps you choose the right one for your use case.

We’ll take a concrete model, run it at different quantization levels, and measure quality, speed, and memory usage. The result is a practical guide that saves you time and effort when selecting your next model.

Why Do You Need a Quantization Comparison?

Imagine you have a graphics card with 12 GB of VRAM and want to run Llama 3 8B. In Q4_K_M, the model needs about 5 GB; in Q5_K_M, about 6 GB; in Q8_0, about 8 GB. All three fit on your card. Which should you choose?

Without a comparison, you’d probably go with Q4_K_M because it’s the standard recommendation. But if you have 12 GB of VRAM, you’re wasting memory that could go toward a longer context window or a larger model. Q5_K_M or even Q6_K might be the better choice.

The flip side: if you only have 8 GB of VRAM and choose Q8_0, you’d have almost no room for the context. The model would start, but crash during longer conversations. Here, Q4_K_M would be the safe choice.

A quantization comparison helps you find the optimal level for your hardware and use case. It’s not about choosing the highest or lowest level, but the one that best fits your setup.

Quantization Levels Explained

Before we dive into comparisons, here’s a quick recap of the main levels. For a more detailed explanation, see Quantization.

Q4_K_M is the standard level for most local users. It uses 4 bits per weight with K-quantization, which stores sensitive layers with more bits. The “M” stands for “Medium,” a compromise between size and quality. Q4_K_M reduces memory requirements to about one-third or one-quarter of the original.

Q5_K_M is the next step up. It uses 5 bits per weight and delivers noticeably better quality than Q4, but requires about 25 percent more memory. For many users, Q5_K_M is the sweet spot when they have a bit more VRAM available.

Q6_K uses 6 bits per weight and is qualitatively very close to the original. Memory usage is about 50 percent higher than Q4_K_M. Q6_K is a good choice when quality is the top priority and storage isn’t a constraint.

Q8_0 uses 8 bits per weight and is virtually indistinguishable from the original (FP16). Memory usage is roughly double that of Q4_K_M. Q8_0 is for maximum quality when storage is no concern.

Who Is This Article For?

This article is for you if you:

• already run a model locally and want to know if a different quantization level would work better
• have a new graphics card and want to find the optimal level for it
• need to understand how much quality you lose with lower quantization
• are deciding whether to test multiple models at different quantization levels
• want to know if switching from Q4 to Q5 or Q6 is worth the extra memory

You should have a basic understanding of quantization. If not, read Quantization first.

Key Terms

Term	Definition
Q4_K_M	4-bit K-quantization, Medium variant, standard choice for most users
Q5_K_M	5-bit K-quantization, Medium variant, higher quality than Q4
Q6_K	6-bit K-quantization, very close to the original
Q8_0	8-bit quantization, virtually identical to the original
FP16	16-bit floating point, the original format of trained models
GGUF	File format for quantized models, used by llama.cpp and Ollama
K-quantization	Mixed-bit quantization that stores sensitive layers with more bits
Bits per Weight	Number of storage bits per parameter value
Tokens per Second	Metric for model execution speed
KV-Cache	Buffer for attention values, grows with context length

Practical Comparison: The Same Model at Different Levels

To make this concrete, we’ll use Llama 3 8B as our example. It has 8 billion parameters and is one of the most popular models for local use. The table below shows it across different quantization levels:

Level	File Size	VRAM Required	Quality	Speed	Best For
FP16	~16 GB	~18 GB	Original	Baseline	Servers, high-end workstations
Q8_0	~8 GB	~10 GB	Virtually identical	Very fast	12+ GB VRAM, maximum quality
Q6_K	~6.5 GB	~8.5 GB	Excellent	Very fast	12+ GB VRAM, quality-first
Q5_K_M	~5.7 GB	~7.5 GB	Very good	Very fast	8+ GB VRAM, solid compromise
Q4_K_M	~4.7 GB	~6.5 GB	Good	Very fast	8 GB VRAM, standard choice
Q4_0	~4.4 GB	~6 GB	Slightly weaker	Very fast	When every GB counts
Q3_K_M	~3.5 GB	~5 GB	Noticeably weaker	Very fast	Last resort for very little VRAM
Q2_K	~2.7 GB	~4 GB	Significantly limited	Very fast	Experiments only

The VRAM values include a buffer of roughly 2 GB for KV-cache and overhead. Actual values vary slightly depending on context length and framework.

For more on memory requirements, see RAM and VRAM Requirements and Model Size and Storage.

Quality: How Much Do You Really Lose?

The most important question is: how much quality do you lose with lower quantization? The answer depends on your task.

For chat and general text the difference between Q4_K_M and Q8_0 is barely noticeable for most users. Q4_K_M produces fluent, coherent responses that work fine for everyday conversation. If you’re only using the model for chatting or simple summarization, you probably won’t notice the difference.

For coding tasks the gap becomes more apparent. Q4_K_M can make mistakes on complex code work that don’t occur at Q5_K_M or Q6_K. If you’re using the model for programming, at least Q5_K_M is recommended, preferably Q6_K.

For math and logic the difference is most pronounced. Lower quantizations can fail at multi-step calculations because precision in intermediate results drops. For mathematical work, choose Q5_K_M or higher.

For multilingual tasks Q4_K_M occasionally confuses languages or produces unusual word choices. Q5_K_M and higher are more stable, especially for less common languages.

In summary: Q4_K_M is fine for simple tasks. For demanding work like coding, math, or multilingual text, choose Q5_K_M or Q6_K. Q8_0 is only necessary when you need maximum quality and storage is not an issue.

Speed: Is Less Bits Faster?

A surprising effect of quantization is that lower bit depths are often faster. This happens because less data needs to move between memory and the processor. VRAM is fast, but bandwidth is limited. Fewer bits per weight means less data traffic, which speeds up execution.

In practice, it looks like this:

Bit Depth	Relative Speed (GPU)	Relative Speed (CPU)
FP16	1.0x	1.0x
Q8_0	~1.5x	~1.3x
Q6_K	~1.8x	~1.5x
Q5_K_M	~2.0x	~1.7x
Q4_K_M	~2.2x	~2.0x
Q3_K_M	~2.3x	~2.1x

These are reference values and vary depending on your GPU, CPU, and framework. On an RTX 3060, Q4_K_M can be roughly 50 percent faster than Q8_0. On CPU, the difference is even larger because memory bandwidth becomes the main bottleneck.

The takeaway: lower quantization not only saves memory but can also increase speed. If speed matters more to you than maximum quality, Q4_K_M is the better choice, not Q8_0.

Memory Requirements: The Deciding Factor

For most local users, memory is the deciding factor. Your GPU has a fixed amount of VRAM, and the model must fit. Here’s an overview of which model in which bit depth fits on which GPU:

VRAM	Q4_K_M	Q5_K_M	Q6_K	Q8_0
8 GB	up to 8B	up to 7B	up to 7B	up to 4B
12 GB	up to 13B	up to 13B	up to 10B	up to 8B
16 GB	up to 14B	up to 14B	up to 13B	up to 13B
24 GB	up to 30B	up to 27B	up to 27B	up to 22B

These values include roughly 2 GB of buffer for KV-cache and overhead. With longer contexts, you need more buffer, which reduces maximum model size.

The table shows this clearly: with 8 GB VRAM you’re limited to Q4_K_M for 8B models. With 12 GB VRAM, you can step up to Q6_K or Q8_0 on 8B models. With 24 GB VRAM you have full flexibility on 8B models and can even run 30B models in Q4_K_M.

How Do I Choose the Right Bit Depth?

Here’s a practical decision guide:

Choose Q4_K_M if:

• You have 8 GB VRAM or less
• You use the model for chat, summaries, or simple text tasks
• Speed matters more to you than maximum quality
• You want to run multiple models simultaneously

Choose Q5_K_M if:

• You have 12 GB VRAM or more and run an 8B to 13B model
• You use the model for coding or math tasks
• You want a good balance between quality and memory
• You need longer contexts and want VRAM left over

Choose Q6_K if:

• You have 16 GB VRAM or more
• Quality is your top priority but you don’t need Q8_0
• You’re solving complex coding or logic tasks
• You use a Mac with 24 GB or more unified memory

Choose Q8_0 if:

• You have 16 GB VRAM or more and run an 8B model
• You need maximum quality and memory isn’t a constraint
• You use the model for fine-tuning or as a base for further training
• You want to reproduce benchmark results achieved with FP16

As a general rule: when in doubt, choose Q4_K_M. If you have more VRAM than Q4_K_M needs, step up to Q5_K_M. If Q5_K_M isn’t good enough, use Q6_K. Q8_0 is reserved for special cases.

Switching Quantization in Ollama

With Ollama you can install and run different quantization levels of the same model. Here are the key commands:

# Default installation (Q4_K_M)
ollama run llama3:8b

# Choose specific quantization
ollama run llama3:8b-q5_K_M
ollama run llama3:8b-q6_K
ollama run llama3:8b-q8_0

# List installed models
ollama list

# Remove model if no longer needed
ollama rm llama3:8b-q8_0

Ollama automatically downloads the corresponding GGUF file. For more on model management, see the Managing Models guide.

Common Pitfalls

1. Running Q8_0 on underpowered hardware: Q8_0 needs twice as much memory as Q4_K_M. If the model barely fits in VRAM, there’s no room left for context. The model crashes during longer conversations. Choose a lower bit depth.

2. Choosing Q4_0 instead of Q4_K_M: Q4_0 is the older, simpler variant. Q4_K_M delivers significantly better quality with nearly identical memory requirements. If you use Q4, always use Q4_K_M, never Q4_0.

3. Not accounting for context requirements: VRAM use isn’t just the model itself, but also the KV-cache. At 8192 tokens of context, you add 1 to 3 GB. Plan accordingly and choose a lower bit depth if context grows large.

4. Underestimating quality loss at Q3 and Q2: Q3_K_M and Q2_K are last-resort options for very limited VRAM. The quality loss is noticeable to significant. The model can produce hallucinations or make logical errors. Use these levels only when no other option remains.

5. Ignoring the speed advantage of lower quantization: Lower quantization isn’t just smaller, it’s faster. If speed matters to you, Q4_K_M can outperform Q8_0, even if you had the VRAM for Q8_0.

6. Comparing different models at different quantization levels: If you compare Model A in Q4_K_M with Model B in Q8_0, the comparison is unfair. Always compare models at the same quantization level, otherwise you won’t know if differences come from the model or the quantization.

7. Reading benchmark results without quantization context: Most benchmark results use FP16 or Q8_0. A Q4_K_M model might score 1 to 3 percentage points lower. Always compare at the same quantization level. See the Benchmarks article for more.

Hardware, Costs, and Security

Hardware: Your choice of quantization level determines what hardware you need. With Q4_K_M you can go far with an RTX 3060 with 12 GB VRAM. With Q8_0 you need at least 16 GB VRAM for the same model. Apple Silicon users especially benefit from Q4_K_M since unified memory is limited.

Costs: Quantized models are free. The cost savings come from hardware: Q4_K_M works with a budget GPU, Q8_0 requires a more expensive one. The difference can amount to hundreds of euros. An RTX 3060 with 12 GB costs under 300 EUR, an RTX 4080 with 16 GB costs over 900 EUR.

Security: Quantization has no security implications. The quantized model is a compressed version of the same model. When you run it locally, all data stays on your machine. No information is sent to cloud services. Quantization doesn’t change data security.

Further Reading

• Model Overview - All model articles at a glance
• Quantization - Detailed explanation of the basics
• Llama Models - Meta’s model family
• Benchmarks - Compare models objectively
• Ollama - The simplest way to run local models
• Managing Models - Manage models in Ollama
• Model Size and Memory Requirements - The relationship between size and memory
• RAM and VRAM Requirements - How much memory you need

FAQ

Is it worth switching from Q4_K_M to Q5_K_M?

If you have enough VRAM, yes. Q5_K_M delivers noticeably better quality for coding and math tasks, with only about 25 percent more memory overhead. If your model still fits comfortably in VRAM at Q5_K_M, go with it.

Is Q8_0 really better than Q6_K?

The difference is imperceptible for most use cases. Q8_0 is practically identical to the original, and Q6_K comes very close. Q6_K uses about 20 percent less memory. If you’re torn between Q6_K and Q8_0, choose Q6_K unless you need absolute maximum quality.

Can I switch quantization levels without re-downloading the model?

No. Each quantization level is a separate file. With Ollama you can install different levels and switch between them, but each one must be downloaded separately.

What’s the difference between Q4_0 and Q4_K_M?

Q4_0 quantizes all weights uniformly to 4 bits. Q4_K_M uses K-quantization, where sensitive layers are stored with more bits. Q4_K_M delivers significantly better quality at nearly the same memory footprint. Always choose Q4_K_M, never Q4_0.

How much VRAM do I need for Q5_K_M with an 8B model?

About 7 to 8 GB including the KV cache and overhead. With 8 GB VRAM you’ll be tight, but 12 GB VRAM gives you breathing room. Plan for about 2 GB of buffer for context.

Is Q3_K_M still usable?

Q3_K_M is a last-resort option for severely constrained hardware. The quality degradation is noticeable, especially for complex tasks. It might work for simple chat applications, but it’s not recommended for coding or math. A smaller model in Q4_K_M is better than a large model in Q3_K_M.

Which quantization level is fastest?

The lower the bit count, the faster the execution, because less data needs to be moved. Q4_K_M is roughly 50 percent faster than Q8_0 on most GPUs. Q3 and Q2 are even faster, but the quality loss outweighs the speed gains.

Should I use a different level on Apple Silicon?

The principles are the same, but Apple Silicon has Unified Memory, meaning you share memory with the operating system. Q4_K_M is the standard choice here too. On Macs with 16 GB RAM, use Q4_K_M. With 32 GB or more, you can go with Q5_K_M or Q6_K.

How do I compare two quantization levels myself?

Load both levels in Ollama, ask the same questions, and compare the answers. For more objective comparisons, use benchmarks like MMLU or HumanEval with lm-evaluation-harness. More on that in the Benchmarks article.

Does quantization make sense for very small models like 2B?

Yes, but the quality loss is more pronounced with small models because each weight carries more influence. For a 2B model, you should use at least Q5_K_M or Q6_K to avoid degrading quality too much. Q4_K_M is the bare minimum for 2B models.

Does quantization affect context length?

Indirectly, yes. Quantization reduces the model’s memory footprint, leaving more VRAM available for the KV cache. With Q4_K_M you can use a longer context with the same VRAM compared to Q8_0. If long contexts matter to you, a lower quantization level can be the better choice.

Sources

• llama.cpp quantization documentation on GitHub
• Hugging Face model hub and GGUF quantizations
• Ollama documentation for model management
• TheBloke and bartowski GGUF model collections on Hugging Face
• K-Quantization Technical Report