---

license: apache-2.0 tags: - gguf - qwen - qwen3 - qwen3-4b - qwen3-4b-gguf - llama.cpp - quantized - text-generation - reasoning - agent - chat - multilingual - imatrix - q3_hifi - q4_hifi - q5_hifi base_model: Qwen/Qwen3-4B author: geoffmunn pipeline_tag: text-generation language: - en - zh - es - fr - de - ru - ar - ja - ko - hi

---

Qwen3-4B-f16-GGUF

This is a GGUF-quantized version of the Qwen/Qwen3-4B language model — a powerful 4-billion-parameter LLM from Alibaba's Qwen series, designed for strong reasoning, agentic workflows, and multilingual fluency on consumer-grade hardware.

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

Why Use a 4B Model?

The Qwen3-4B model strikes a powerful balance between capability and efficiency, offering:

• Strong reasoning and language understanding—significantly more capable than sub-1B models
• Smooth CPU inference with moderate hardware (no high-end GPU required)
• Memory footprint under ~8GB when quantized (e.g., GGUF Q4KM or AWQ)
• Excellent price-to-performance ratio for local or edge deployment

It’s ideal for:

• Local chatbots with contextual memory and richer responses
• On-device AI on laptops or mid-tier edge servers
• Lightweight RAG (Retrieval-Augmented Generation) applications
• Developers needing a capable yet manageable open-weight model

Choose Qwen3-4B when you need more intelligence than a tiny model can provide—but still want to run offline, avoid cloud costs, or maintain full control over your AI stack.

Qwen3 4B Quantization Guide: Cross-Bit Summary & Recommendations

Executive Summary

At 4B scale, quantization quality is exceptional—multiple variants achieve near-lossless or even better-than-F16 perplexity under specific conditions. However, imatrix interactions are uniquely counterintuitive at this scale: it harms certain variants (Q4KHIFI, Q5KS) while helping others. This makes quantization selection critically dependent on whether imatrix is used.

Quantization	Best Variant (+ imatrix)	Quality vs F16	File Size	Speed	Memory
Q5K	Q5K_HIFI + imatrix	-0.76% (better than F16!)	2.67 GiB	182.7 TPS	2,734 MiB
Q4K	Q4K_M + imatrix	+2.75%	2.32 GiB	200.2 TPS	2,376 MiB
Q3K	Q3K_HIFI + imatrix	+5.9%	2.15 GiB	151.3 TPS	2,202 MiB

💡 Critical insight: 4B is the "sweet spot" where Q5KS without imatrix achieves -0.68% vs F16 (better than full precision!) while being 65% smaller and 124% faster. This is a rare case where quantization acts as beneficial regularization.

---

Bit-Width Recommendations by Use Case

✅ Quality-Critical Applications

→ Q5KHIFI + imatrix

• Best perplexity at 14.2321 PPL (-0.76% vs F16) — statistically indistinguishable from (or better than) F16
• Only 1.4% slower than fastest variant (182.7 TPS)
• Requires custom llama.cpp build with Q5KHIFIRES8 support
• ⚠️ Never use Q5K_S + imatrix — quality degrades severely (+0.94% vs F16)

⚖️ Best Overall Balance (Recommended Default)

→ Q4KM + imatrix

• Excellent +2.75% precision loss vs F16 (PPL 14.2865)
• Strong speed (200.2 TPS, +143% vs F16)
• Compact size (2.32 GiB, 69% smaller than F16)
• Standard llama.cpp compatibility — no custom build required
• Ideal for most development and production scenarios

🚀 Maximum Speed / Minimum Size

→ Q5KS (no imatrix)

• Fastest at 184.65 TPS (+124% vs F16)
• Smallest at 2.62 GiB (5.60 BPW)
• Best quality without imatrix at -0.68% vs F16 (beats F16!)
• ⚠️ Critical: Do NOT use imatrix with Q5KS — it degrades quality by 1.63%

📱 Extreme Memory Constraints (< 2.2 GiB)

→ Q3KS + imatrix

• Absolute smallest footprint (1.75 GiB file, 1,792 MiB runtime)
• Acceptable +16.6% precision loss with imatrix
• Fastest Q3 variant (223.5 TPS)
• Only viable Q3 option under 1.8 GiB VRAM

---

Critical Warnings for 4B Scale

⚠️ imatrix is NOT universally beneficial at 4B scale — it exhibits paradoxical behavior:

Variant	imatrix Effect	Recommendation
Q5KS	❌ Harmful: +1.63% PPL degradation	Never use imatrix — quality drops from -0.68% to +0.94% vs F16
Q4KHIFI	❌ Severely harmful: +4.4% PPL degradation	Never use imatrix — quality drops from +0.29% to +4.72% vs F16
Q4KM	✅ Beneficial: -0.34% PPL improvement	Always use imatrix — best Q4 quality at +2.75% vs F16
Q5KHIFI	✅ Beneficial: -0.80% PPL improvement	Always use imatrix — achieves -0.76% vs F16 (best overall)
Q3_K variants	✅ Beneficial: 6-12% PPL improvement	Always use imatrix — essential for production quality

⚠️ Q4KHIFI without imatrix is remarkable: Achieves +0.29% precision loss — the closest to lossless 4-bit quantization observed across all tested scales. This makes it ideal for deployments where imatrix generation overhead is undesirable.

⚠️ Q5KS without imatrix is the 4B anomaly: Wins all three dimensions simultaneously (quality, speed, size) without imatrix — a rare quantization "free lunch" that only occurs at this specific model scale.

---

Decision Flowchart

Need best quality?
├─ Yes → Using imatrix?
│        ├─ Yes → Q5KHIFI + imatrix (-0.76% vs F16)
│        └─ No  → Q4KHIFI (no imatrix, +0.29% vs F16)
│
Need best balance?
├─ Yes → Using imatrix?
│        ├─ Yes → Q4KM + imatrix (+2.75% vs F16, standard build)
│        └─ No  → Q5KS (no imatrix, -0.68% vs F16, fastest/smallest)
│
Need max speed?
├─ Yes → Q5KS (no imatrix) — 184.65 TPS
│        ⚠️ Never pair with imatrix!
│
Memory constrained (< 2.2 GiB)?
└─ Yes → Q3KS + imatrix — 1,792 MiB runtime
         Accept +16.6% quality loss for extreme footprint reduction

---

Cross-Bit Performance Comparison

Priority	Q3K Best	Q4K Best	Q5_K Best	Winner
Quality (no imat)	Q3KHIFI (+12.6%)	Q4KHIFI (+0.29%) ✅	Q5KS (-0.68%) ✅✅	Q5KS
Quality (with imat)	Q3KHIFI (+5.9%)	Q4KM (+2.75%)	Q5KHIFI (-0.76%) ✅	Q5KHIFI
Speed	Q3KS (223.5 TPS)	Q4KS (206.7 TPS)	Q5KS (184.6 TPS)	Q3KS
Smallest Size	Q3KS (1.75 GiB) ✅	Q4KS (2.21 GiB)	Q5KS (2.62 GiB)	Q3KS
Best Balance	Q3KM + imat	Q4KM + imat ✅	Q5KM + imat	Q4KM

✅ = Recommended for general use ✅✅ = Exceptional result (better than F16 or near-lossless)

---

Memory Budget Guide

Available VRAM	Recommended Variant	Expected Quality	Why
< 1.8 GiB	Q3KS + imatrix	+16.6% loss	Only option that fits; quality acceptable for non-critical tasks
1.8 – 2.5 GiB	Q4KS (no imatrix)	+4.9% loss	Good speed/size balance; avoid imatrix (degrades Q4KS slightly)
2.5 – 3.0 GiB	Q4KM + imatrix ✅	+2.75% loss	Best balance of quality/speed/size; standard compatibility
3.0 – 4.0 GiB	Q5KHIFI + imatrix ✅	-0.76% loss	Near-F16 quality; requires custom build
> 7.5 GiB	F16 or Q5KHIFI + imatrix	0% or -0.76% loss	F16 if absolute precision required; Q5KHIFI if speed/memory matter

---

Bottom Line Recommendations

For Most Users

→ Q4KM + imatrix Delivers excellent quality (+2.75% vs F16), strong speed (200 TPS), compact size (2.32 GiB), and universal llama.cpp compatibility. The safe, practical choice for 90% of deployments.

For Quality-Critical Work

→ Q5KHIFI + imatrix Achieves perplexity better than F16 (-0.76% vs F16) with 64% memory reduction. Requires custom build but delivers maximum fidelity.

For Speed-Critical Work

→ Q5KS (no imatrix) Fastest (184.7 TPS) AND highest quality (-0.68% vs F16) AND smallest size (2.62 GiB) — but never use imatrix with this variant.

For Edge/Mobile Deployment

→ Q3KM + imatrix Best Q3 balance: +11.0% quality loss but 40% faster and 10% smaller than Q3KHIFI. Fits in ~2.0 GiB with comfortable headroom.

---

Critical Implementation Notes

⚠️ imatrix paradox at 4B scale: Unlike other model sizes where imatrix universally improves quality, at 4B:

• Q5KS and Q4KHIFI suffer quality degradation with imatrix
• This is caused by interference between imatrix's importance weighting and these variants' outlier/residual preservation strategies
• Always verify imatrix impact before deploying — never assume it helps

⚠️ Build requirements:

• Q5KHIFI: Requires llama.cpp build 8037+ with Q5KHIFIRES8 support
• Q4KHIFI: Requires build 8025+ with Q4KHIFI/Q5KHIFIRES8 support
• Q4KM/Q5KS/Q3_K variants: Work with any recent standard llama.cpp build

⚠️ The 4B "sweet spot": This model size uniquely benefits from uniform quantization (Q5KS) without imatrix guidance. Larger models (8B+) require imatrix for optimal quality; smaller models (1.7B-) suffer severe degradation without imatrix. 4B sits in a Goldilocks zone where the weight distribution aligns perfectly with 5-bit uniform quantization.

---

Quick Reference Card

Scenario	Variant	PPL	vs F16	Speed	Size	Memory
Best quality	Q5KHIFI + imat	14.2321	-0.76% ✅	182.7 TPS	2.67 GiB	2,734 MiB
Best balance	Q4KM + imat	14.2865	+2.75% ✅	200.2 TPS	2.32 GiB	2,376 MiB
Fastest/smallest	Q5KS (no imat)	14.2439	-0.68% ✅✅	184.7 TPS	2.62 GiB	2,683 MiB
Near-lossless Q4	Q4KHIFI (no imat)	14.3832	+0.29% ✅	184.1 TPS	2.50 GiB	2,560 MiB
Smallest footprint	Q3KS + imat	16.7282	+16.6%	223.5 TPS	1.75 GiB	1,792 MiB

✅ = Excellent | ✅✅ = Better than F16 | ⚠️ = Avoid imatrix pairing

Golden rule for 4B:

• Q5KS → Never use imatrix
• Q4KHIFI → Never use imatrix
• Q4KM / Q5KHIFI → Always use imatrix
• Q3_K variants → Always use imatrix

Non-technical model anaysis and rankings

NOTE: This analysis does not include the HIFI models.

I have run each of these models across 6 questions, and ranked them all based on the quality of the anwsers.
Qwen3-4B-f16:Q3KM (or Qwen3-4B-f16:Q3HIFI) is the best model across all question types, but if you want to play it safe with a higher precision model, then you could consider using Qwen3-4B-f16:Q80.

You can read the results here: Qwen3-4b-f16-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	1.9 GB	🚨 DO NOT USE. Worst results from all the 4B models.
🥈 Q3KS	⚡ Fast	2.2 GB	🥈 Runner up. A very good model for a wide range of queries.
🥇 Q3KM	⚡ Fast	2.4 GB	🥇 Best overall model. Highly recommended for all query types.
Q4KS	🚀 Fast	2.7 GB	A late showing in low-temperature queries. Probably not recommended.
Q4KM	🚀 Fast	2.9 GB	A late showing in high-temperature queries. Probably not recommended.
Q5KS	🐢 Medium	3.3 GB	Did not appear in the top 3 for any question. Not recommended.
Q5KM	🐢 Medium	3.4 GB	A second place for a high-temperature question, probably not recommended.
Q6_K	🐌 Slow	3.9 GB	Did not appear in the top 3 for any question. Not recommended.
🥉 Q8_0	🐌 Slow	5.1 GB	🥉 If you want to play it safe, this is a good option. Good results across a variety of questions.

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 massive 9343 chunk imatrix file for extra precision. You can re-use it here: Qwen3-4B-f16-imatrix-9343-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/Qwen3-4B-f16/resolve/main/Qwen3-4B-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 ./Qwen3-4B-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 Qwen3-4B-f16:Q3KM -f Modelfile

You will now see "Qwen3-4B-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.