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license: apache-2.0 tags: - gguf - qwen - qwen3 - qwen3-8b - qwen3-8b-gguf - llama.cpp - quantized - text-generation - reasoning - agent - chat - multilingual - matrix - q3_hifi - q4_hifi - q5_hifi base_model: Qwen/Qwen3-8B author: geoffmunn pipeline_tag: text-generation language: - en - zh - es - fr - de - ru - ar - ja - ko - hi

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Qwen3-8B-f16-GGUF

This is a GGUF-quantized version of the Qwen/Qwen3-8B language model - an 8-billion-parameter LLM from Alibaba's Qwen series, designed for advanced reasoning, agentic behavior, and multilingual tasks.

Converted for use with llama.cpp and compatible tools like OpenWebUI, LM Studio, GPT4All, and more.

Why Use an 8B Model?

The Qwen3-8B model represents a significant leap in capability while remaining remarkably accessible for local and edge deployment. It offers:

• Near-state-of-the-art reasoning, coding, and multilingual performance among open 8B-class models
• Smooth inference on a single consumer GPU (e.g., 16–24 GB VRAM) or fast CPU runtime with quantization
• Quantized versions (e.g., GGUF Q4KM, AWQ) that fit within ~6–8 GB of memory, enabling use on mid-range hardware
• Strong performance on complex tasks like document summarization, structured output generation, and agentic workflows

It’s ideal for:

• Local AI assistants that handle nuanced, multi-turn conversations
• Self-hosted RAG pipelines with deep document understanding
• Developers building production-grade on-prem AI features without cloud dependencies
• Researchers and tinkerers seeking a capable yet manageable open-weight foundation

Choose Qwen3-8B when you need high-quality output and robust general intelligence - but still value efficiency, privacy, and full control over your deployment environment.

Qwen3 8B Quantization Guide: Cross-Bit Summary & Recommendations

Executive Summary

At 8B scale, quantization quality is exceptional—all bit widths deliver production-ready fidelity with imatrix, and even Q3K achieves near-F16 quality (+3.5% loss). Unlike smaller models (0.6B–1.7B), 8B models are inherently resilient to quantization, making imatrix beneficial but not strictly essential. The sweet spot is Q5KHIFI + imatrix for quality-critical work (+0.27% vs F16) and Q4K_M + imatrix for balanced deployments (+1.3% vs F16).

Quantization	Best Variant (+ imatrix)	Quality vs F16	File Size	Speed	Memory
Q5K	Q5K_HIFI + imatrix	+0.27% (best)	5.62 GiB	109.65 TPS	5,754 MiB
Q4K	Q4K_M + imatrix	+1.3%	4.68 GiB	125.51 TPS	4,792 MiB
Q3K	Q3K_HIFI + imatrix	+3.5%	4.49 GiB	111.61 TPS	4,598 MiB

💡 Critical insight: 8B is the "Goldilocks scale" for quantization—large enough to tolerate aggressive compression yet small enough to benefit dramatically from speed/size gains. All three bit widths are viable for production use with imatrix.

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Bit-Width Recommendations by Use Case

✅ Quality-Critical Applications

→ Q5KHIFI + imatrix

• Best perplexity at 10.1377 PPL (+0.27% vs F16) — near-lossless fidelity
• Only 0.27% precision loss represents the closest approach to F16 quality across all quantization levels
• Requires custom llama.cpp build with Q6KHIFIRES8 support
• ⚠️ Never use Q5K_S without imatrix — quality degrades to +1.62% vs F16

⚖️ Best Overall Balance (Recommended Default)

→ Q4KM + imatrix

• Excellent +1.3% precision loss vs F16 (PPL 10.2384)
• Strong 125.51 TPS speed (+171% vs F16)
• Compact 4.68 GiB file size (69.3% smaller than F16)
• Standard llama.cpp compatibility — no custom build required
• Ideal for most development and production scenarios

🚀 Maximum Speed / Minimum Size

→ Q3KHIFI + imatrix

• Unique win-win at 8B scale: fastest Q3 variant (111.61 TPS) AND best Q3 quality (+3.5% vs F16)
• Smallest footprint at 4.49 GiB file / 4,598 MiB runtime
• Surprisingly good quality for 3-bit quantization — production-ready even without imatrix (+8.6% loss)
• ⚠️ Avoid Q3KS without imatrix — suffers +12.6% quality loss

📱 Extreme Memory Constraints (< 4.6 GiB)

→ Q3KS + imatrix

• Absolute smallest footprint (3,594 MiB runtime)
• Acceptable +9.0% precision loss with imatrix (unusable at +12.6% without imatrix)
• Only viable Q3 option under 4.6 GiB budget

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Critical Warnings for 8B Scale

⚠️ imatrix is strongly recommended but not mandatory — Unlike 0.6B/1.7B where imatrix is essential, 8B models maintain good quality even without it (Q5KHIFI: +1.11%, Q4KHIFI: +2.4%, Q3KHIFI: +8.6%). However, imatrix still provides meaningful gains (0.8–3.7% PPL improvement).

⚠️ Q5K quality ranking reversal with imatrix — Q5KS + imatrix (10.1538 PPL) actually beats Q5KM + imatrix (10.1612 PPL) by 0.07 PPL points. This makes Q5K_S + imatrix viable for speed-constrained deployments where the 3.2% speed advantage matters.

⚠️ Q4KS without imatrix is unusable — Suffers +5.7% precision loss (10.6893 PPL) — the highest degradation of any Q4 variant at 8B scale. Always pair Q4KS with imatrix (reduces loss to +1.9%).

⚠️ Q3KHIFI requires no special handling — Unlike at 0.6B/1.7B scales, Q3KHIFI at 8B delivers substantial quality gains (+3.5% vs F16 with imatrix) that justify its 13.5% memory premium over Q3KM.

⚠️ All Q3 variants are production-ready — Even Q3KS with imatrix (+9.0% loss) remains usable for non-critical tasks — a dramatic improvement over smaller scales where Q3 quantization often fails.

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Memory Budget Guide

Available VRAM	Recommended Variant	Expected Quality	Why
< 4.6 GiB	Q3KS + imatrix	PPL 11.02, +9.0% loss	Only option that fits; quality acceptable for non-critical tasks
4.6 – 5.5 GiB	Q3KHIFI + imatrix	PPL 10.46, +3.5% loss ✅	Best Q3 quality; production-ready even without imatrix
5.5 – 6.5 GiB	Q4KM + imatrix	PPL 10.24, +1.3% loss ✅	Best balance of quality/speed/size; standard compatibility
6.5 – 8.0 GiB	Q5KHIFI + imatrix	PPL 10.14, +0.27% loss ✅	Near-lossless quality; requires custom build
> 15.3 GiB	F16	Best quality (baseline)	Only if absolute precision required

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Cross-Bit Performance Comparison

Priority	Q3K Best	Q4K Best	Q5_K Best	Winner
Quality (with imat)	Q3KHIFI (+3.5%)	Q4KM (+1.3%)	Q5KHIFI (+0.27%) ✅	Q5KHIFI
Quality (no imat)	Q3KHIFI (+8.6%)	Q4KHIFI (+2.4%)	Q5KHIFI (+1.11%) ✅	Q5KHIFI
Speed	Q3KS (153.55 TPS)	Q4KS (130.98 TPS)	Q5KS (113.33 TPS) ✅	Q3KS
Smallest Size	Q3KS (3.51 GiB) ✅	Q4KS (4.47 GiB)	Q5KS (5.32 GiB)	Q3KS
Best Balance	Q3KM + imat	Q4KM + imat ✅	Q5KHIFI + imat	Q4KM

✅ = Recommended for general use ✅✅ = Exceptional result (near-lossless or best-in-class)

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Scale-Specific Insights: Why 8B Quantizes So Well

1. Model redundancy effect: 8B parameter count provides sufficient weight redundancy that quantization errors average out rather than accumulating catastrophically (unlike 0.6B/1.7B)
2. imatrix effectiveness plateau: imatrix recovers 62–76% of precision loss at 8B — less dramatic than at 1.7B (70–78%) but more consistent across bit widths
3. Residual quantization sweet spot: Q5KHIFI's Q6KHIFIRES8 tensors provide maximal benefit at 8B scale — the 5 residual tensors capture precisely the right amount of quantization error without overhead
4. Q4KHIFI behavior shift: Unlike at 14B where imatrix harms Q4KHIFI, at 8B imatrix helps it (-1.1% PPL improvement) — demonstrating non-linear scale effects
5. Q3K viability threshold: 8B is the smallest scale where Q3KHIFI achieves truly production-ready quality (+3.5% with imatrix) — below this, Q3 quantization requires careful validation

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Practical Deployment Recommendations

For Most Users

→ Q4KM + imatrix Delivers excellent quality (+1.3% vs F16), strong speed (125.51 TPS), compact size (4.68 GiB), and universal llama.cpp compatibility. The safe, practical choice for 95% of deployments.

For Quality-Critical Work

→ Q5KHIFI + imatrix Achieves near-lossless quantization (+0.27% vs F16) with 64% memory reduction and 2.4× speedup. Requires custom build but worth it for research, content generation, or any task where output fidelity is non-negotiable.

For Edge/Mobile Deployment

→ Q3KHIFI + imatrix Best Q3 quality (+3.5% vs F16) with smallest viable footprint (4.49 GiB). Production-ready even without imatrix (+8.6% loss) — valuable for environments where imatrix generation isn't feasible.

For High-Throughput Serving

→ Q5KS + imatrix Fastest Q5 variant (113.33 TPS) with surprisingly good quality (+0.42% vs F16) that actually beats Q5KM with imatrix. Ideal when every TPS matters and marginal quality differences are acceptable.

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Bottom Line Recommendations

Scenario	Recommended Variant	Rationale
Default / General Purpose	Q4KM + imatrix	Best balance of quality, speed, size, and compatibility
Maximum Quality	Q5KHIFI + imatrix	Near-lossless (+0.27% vs F16) with 64% memory reduction
Minimum Size	Q3KHIFI + imatrix	Best Q3 quality (+3.5%) with 71% memory reduction
Maximum Speed	Q5KS + imatrix	Fastest Q5 (113.33 TPS) with excellent quality (+0.42%)
No imatrix available	Q5KHIFI (no imat)	Still excellent (+1.11% vs F16); all variants usable
Extreme constraints	Q3KS + imatrix	Only if memory < 4.6 GiB; +9.0% loss acceptable

⚠️ Golden rule for 8B: Unlike smaller models where quantization choices are constrained by quality cliffs, all three bit widths are viable at 8B scale with imatrix. Choose based on your specific constraints (quality vs speed vs size) rather than avoiding certain bit widths entirely.

✅ 8B is the quantization sweet spot: Large enough for robustness, small enough for dramatic efficiency gains. This scale demonstrates that intelligent quantization can deliver near-F16 quality at 1/3 the memory with 2.4× speed — a compelling value proposition for nearly all deployments.

Non-technical model anaysis and rankings

NOTE: This analysis does not include the HIFI models.

There are numerous good candidates - lots of different models showed up in the top 3 across all the quesionts. However, Qwen3-8B-f16:Q3KM was a finalist in all but one question so is the recommended model (or Qwen3-8B-f16:Q3HIFI). Qwen3-8B-f16:Q5K_S did nearly as well and is worth considering,

The 'hello' question is the first time that all models got it exactly right. All models in the 8B range did well and it's mainly a question of what one works best on your hardware.

You can read the results here: Qwen3-8B-analysis.md

If you find this useful, please give the project a ❤️ like.

Non-HIFI recommentation table based on output

Level	Speed	Size	Recommendation
Q2_K	⚡ Fastest	3.28 GB	Not recommended. Came first in the bat & ball question, no other appearances.
🥉Q3KS	⚡ Fast	3.77 GB	🥉 Came first and second in questions covering both ends of the temperature spectrum.
🥇 Q3KM	⚡ Fast	4.12 GB	🥇 Best overall model. Was a top 3 finisher for all questions except the haiku.
🥉Q4KS	🚀 Fast	4.8 GB	🥉 Came first and second in questions covering both ends of the temperature spectrum.
Q4KM	🚀 Fast	5.85 GB	Came first and second in questions covering high temperature questions.
🥈 Q5KS	🐢 Medium	5.72 GB	🥈 A good second place. Good for all query types.
Q5KM	🐢 Medium	5.85 GB	Not recommended, no appeareances in the top 3 for any question.
Q6_K	🐌 Slow	6.73 GB	Showed up in a few results, but not recommended.
Q8_0	🐌 Slow	8.71 GB	Not recommended, Only one top 3 finish.

Build notes

All of these models were built using these commands:

mkdir build
cmake -B build -DCMAKEBUILDTYPE=Release -DGGMLNATIVE=ON -DGGMLAVX=ON -DGGMLAVX2=ON -DGGMLCUDA=ON -DGGMLVULKAN=OFF -DLLAMACURL=OFF
cmake --build build --config Release -j

NOTE: Vulkan support is specifically turned off here. Vulkan performance was much worse, so if you want Vulkan support you can rebuild these models yourself.

The HIFI quantization also used a very large 4697 chunk imatrix file for extra precision. You can re-use it here: Qwen3-8B-f16-imatrix-4697-generic.gguf

The imatrix was created as a generic mix of Wikipedia, mathmatics, and coding examples.

Source code

You can use the HIFI GitHub repository to build it from source if you're interested: https://github.com/geoffmunn/llama.cpp.

Build notes: HIFIBUILD_GUIDE.md

Improvements and feedback are welcome.

Usage

Load this model using:

• OpenWebUI – self-hosted AI interface with RAG & tools
• LM Studio – desktop app with GPU support and chat templates
• GPT4All – private, local AI chatbot (offline-first)
• Or directly via llama.cpp

Each quantized model includes its own README.md and shares a common MODELFILE for optimal configuration.

Importing directly into Ollama should work, but you might encounter this error: Error: invalid character '<' looking for beginning of value.
In this case try these steps:

1. wget https://huggingface.co/geoffmunn/Qwen3-8B-f16/resolve/main/Qwen3-8B-f16%3AQ3KM.gguf (replace the quantised version with the one you want)
2. nano Modelfile and enter these details (again, replacing Q3KM with the version you want):

FROM ./Qwen3-8B-f16:Q3KM.gguf

Chat template using ChatML (used by Qwen)
SYSTEM You are a helpful assistant

TEMPLATE "{{ if .System }}<|im_start|>system
{{ .System }}<|imend|>{{ end }}<|imstart|>user
{{ .Prompt }}<|im_end|>
<|im_start|>assistant
"
PARAMETER stop <|im_start|>
PARAMETER stop <|im_end|>

Default sampling
PARAMETER temperature 0.6
PARAMETER top_p 0.95
PARAMETER top_k 20
PARAMETER min_p 0.0
PARAMETER repeat_penalty 1.1
PARAMETER num_ctx 4096

The num_ctx value has been dropped to increase speed significantly.

1. Then run this command: ollama create Qwen3-8B-f16:Q3KM -f Modelfile

You will now see "Qwen3-8B-f16:Q3KM" in your Ollama model list.

These import steps are also useful if you want to customise the default parameters or system prompt.

Author

👤 Geoff Munn (@geoffmunn)
🔗 Hugging Face Profile

Disclaimer

This is a community conversion for local inference. Not affiliated with Alibaba Cloud or the Qwen team.