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license: apache-2.0 tags: - gguf - qwen - qwen3 - qwen3-32b - qwen3-32b-gguf - llama.cpp - quantized - text-generation - reasoning - agent - multilingual - imatrix - q3_hifi - q4_hifi - q5_hifi base_model: Qwen/Qwen3-32B author: geoffmunn pipeline_tag: text-generation language: - en - zh - es - fr - de - ru - ar - ja - ko - hi

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Qwen3-32B-f16-GGUF

This is a GGUF-quantized version of the Qwen/Qwen3-32B language model — a 32-billion-parameter LLM with state-of-the-art reasoning, research capabilities, and enterprise-grade performance. Converted for use with llama.cpp, LM Studio, OpenWebUI, GPT4All, and more.

Why Use a 32B Model?

The Qwen3-32B model represents the pinnacle of locally runnable intelligence, delivering near-flagship reasoning and generation capabilities while remaining feasible to deploy on dual consumer GPUs or single professional accelerators. It's the definitive choice when you demand maximum fidelity—where every percentage point of precision matters for complex reasoning, nuanced language tasks, and production-grade code generation—without surrendering to cloud dependency or vendor lock-in.

Highlights:

• Best-in-class open 32B performance, excelling in multi-step reasoning, advanced mathematics, professional-grade coding, and nuanced multilingual understanding
• Unprecedented quantization resilience: achieves statistically F16-equivalent quality with Q5KM/HIFI + imatrix (within ±0.056 measurement noise) while using only 36% of F16's memory and running 2.5× faster
• Production-ready even at aggressive compression: Q4K variants maintain near-lossless fidelity (+0.5–0.7% loss with imatrix); even Q3K_HIFI delivers exceptional 3-bit quality (+2.2% loss)
• Fully open weights with commercial rights, enabling complete control over deployment, fine-tuning, and integration into sensitive workflows

It's ideal for:

• Quality-critical production systems where output precision directly impacts user trust—medical, legal, financial, or engineering applications
• Research and development environments requiring near-F16 fidelity at dramatically reduced infrastructure costs (64% memory savings with zero quality penalty)
• Enterprise RAG and agentic workflows demanding maximum comprehension of complex documents, precise tool use, and reliable multi-hop reasoning
• Developers pushing quantization boundaries, leveraging 32B's unique resilience to deploy massive models on constrained hardware without perceptible degradation

Choose Qwen3-32B when smaller models consistently miss subtle nuances, hallucinate on complex tasks, or fail to maintain coherence across extended reasoning chains—delivering flagship-grade intelligence with the sovereignty, privacy, and cost control of local deployment. With intelligent quantization, you gain 99.9% of F16's capability at one-third the resource footprint: the ultimate balance of quality, efficiency, and independence.

Qwen3 32B Quantization Guide: Cross-Bit Summary & Recommendations

Executive Summary

At 32B scale, quantization achieves near-miraculous fidelity—Q5KHIFI and Q5KM with imatrix deliver statistically F16-equivalent quality (within ±0.056 measurement noise) while using only 36% of F16's memory and running 2.5× faster. Even Q4K variants achieve near-lossless quality (+0.5–0.7% loss with imatrix), and Q3K_HIFI reaches production-ready fidelity (+2.2% loss). This represents the pinnacle of quantization resilience across the Qwen3 family.

Quantization	Best Variant (+ imatrix)	Quality vs F16	File Size	Speed	Memory
Q5K	Q5K_HIFI + imatrix	-0.073% (F16-equivalent ✅✅✅)	21.84 GiB	28.22 TPS	22,364 MiB
Q4K	Q4K_S + imatrix	+0.7% (near-lossless ✅✅)	17.48 GiB	35.00 TPS (fastest)	17,900 MiB
Q3K	Q3K_HIFI + imatrix	+2.2% (exceptional for 3-bit ✅✅)	~17.0 GiB*	32.00 TPS	17,807 MiB

💡 Critical insight: 32B models exhibit unprecedented quantization resilience—even aggressive 3-bit compression maintains production quality. However, Q3KS catastrophically fails (+120–155% precision loss), making variant selection critically important at this scale.

\ Q3KHIFI file size estimated from memory footprint; actual size ~16.8–17.2 GiB*

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Bit-Width Recommendations by Use Case

✅ Quality-Critical Applications (Research, Content Generation)

→ Q5KHIFI + imatrix

• Statistically indistinguishable from F16 (7.8975 PPL vs 7.9033, difference within ±0.056 measurement noise)
• 64.2% memory reduction (22.4 GiB vs 62.5 GiB)
• 157% faster than F16 (28.22 TPS vs 10.96 TPS)
• ⚠️ Requires custom llama.cpp build (8037+) with Q6KHIFI_RES8 support

→ Q5KM + imatrix (standard alternative)

• Also F16-equivalent quality (7.8995 PPL, -0.048% vs F16)
• Standard GGUF compatibility — works with all recent llama.cpp builds
• Only 0.025 PPL points worse than HIFI (within measurement noise)
• Recommended default for quality-critical work requiring standard tooling

⚖️ Best Overall Balance (Recommended Default)

→ Q4KM + imatrix

• Excellent +0.6% precision loss vs F16 (PPL 7.9488) — imperceptible in practice
• Strong 33.36 TPS speed (+193% vs F16)
• Compact 18.40 GiB file size (70% smaller than F16)
• Standard llama.cpp compatibility — no custom builds needed
• Ideal for most development and production scenarios where 5-bit overhead isn't justified

🚀 Maximum Speed / Minimum Size

→ Q4KS + imatrix

• Fastest variant at 35.00 TPS (+208% vs F16)
• Smallest viable footprint at 17.48 GiB (71.4% memory reduction)
• Surprisingly good quality at +0.7% loss — only 0.2% worse than Q4KHIFI with imatrix
• ⚠️ Never use without imatrix — quality degrades to +3.5% loss

💎 Near-Lossless 3-Bit Option

→ Q3KHIFI + imatrix

• Remarkable +2.2% precision loss — exceptional for 3-bit quantization
• 71.5% memory reduction (17,807 MiB vs 62,495 MiB)
• Unique value: When you need maximum compression but cannot accept Q3KS's catastrophic failure
• ⚠️ 22% slower than Q3KM and requires careful validation for quality-sensitive tasks

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Critical Warnings for 32B Scale

⚠️ Q3KS is catastrophically broken at 32B scale:

• Without imatrix: +155% precision loss (PPL 20.19 vs F16 7.90) — completely unusable
• With imatrix: +120% precision loss (PPL 17.40) — still unusable despite imatrix guidance
• NEVER use Q3KS for 32B models — this failure mode does not occur at smaller scales (8B/14B)
• Minimum safe Q3 variant: Q3KM + imatrix (+3.7% loss, production-ready)

⚠️ Q5KHIFI provides negligible advantage over Q5KM:

• Quality difference: 0.025 PPL points (within ±0.056 measurement noise)
• Costs +235 MiB memory (+1.1% overhead) and requires custom build
• Prefer Q5KM + imatrix for standard compatibility unless you specifically need HIFI tensor types

⚠️ imatrix effectiveness plateaus at 32B:

• Q5K variants: Already near-F16 quality without imatrix (+0.06% loss); imatrix provides marginal gains
• Q4KS: Most dramatic imatrix benefit — closes 2.8% quality gap (from +3.5% → +0.7%)
• Q3K_HIFI: Minimal imatrix benefit (+0.18 PPL improvement) — already excellent without it

⚠️ VRAM requirements are substantial:

• Minimum viable: ~18 GiB (Q4KS)
• Comfortable deployment: 24+ GiB (RTX 3090/4090) for context headroom
• Dual-GPU recommended for production workloads (tested on 2× L40S)

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Memory Budget Guide

Available VRAM	Recommended Variant	Expected Quality	Why
< 18 GiB	Not feasible	—	32B models require minimum ~18 GiB even with aggressive quantization
18 – 20 GiB	Q4KS + imatrix	PPL 7.9627, +0.7% loss ✅	Tight fit; leaves minimal headroom for KV cache at longer contexts
20 – 24 GiB	Q4KM + imatrix	PPL 7.9488, +0.6% loss ✅	Comfortable fit on RTX 3090/4090 (24 GiB) with context headroom
24 – 48 GiB	Q5KM + imatrix	PPL 7.8995, F16-equivalent ✅	Room for larger context windows; near-perfect quality
> 48 GiB	Q5KHIFI + imatrix or F16	PPL 7.8975, F16-equivalent ✅	Maximum quality with standard tooling (M) or absolute precision (F16)

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Cross-Bit Performance Comparison

Priority	Q3K Best	Q4K Best	Q5_K Best	Winner
Quality (with imat)	Q3KHIFI (+2.2%)	Q4KHIFI (+0.5%)	Q5KHIFI (-0.073%) ✅✅✅	Q5KHIFI/M
Speed	Q3KS (40 TPS)*	Q4KS (35.00 TPS) ✅	Q5KS (29.62 TPS)	Q4KS
Smallest Size	Q3KS (13.40 GiB) ✅	Q4KS (17.48 GiB)	Q5KS (21.08 GiB)	Q3KS ⚠️
Best Balance	Q3KM + imat	Q4KM + imat ✅	Q5KM + imat	Q4KM

✅ = Recommended for general use ✅✅ = Near-lossless quality ✅✅✅ = Statistically F16-equivalent ⚠️ = Q3KS is broken despite small size — never use

\ Q3KS speed is misleading — quality is catastrophically degraded*

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Scale-Specific Insights: Why 32B Quantizes So Well

1. Parameter redundancy threshold: 32B represents the point where model architecture provides sufficient weight redundancy that quantization errors effectively cancel out rather than accumulating. This creates a "quantization sweet spot" where aggressive compression meets robust architecture.
2. imatrix saturation effect: At 32B scale, imatrix effectiveness plateaus — Q5K variants already achieve near-F16 quality without imatrix (+0.06% loss), unlike smaller models where imatrix recovers 40–78% of lost precision. The model's inherent robustness reduces dependence on importance weighting.
3. Q3K viability paradox: While Q3KHIFI achieves remarkable +2.2% loss (exceptional for 3-bit), Q3KS fails catastrophically (+120–155% loss). This demonstrates that intelligent tensor selection becomes critical at extreme compression levels on large models — uniform quantization strategies break down where mixed-precision approaches succeed.
4. Diminishing returns of residual quantization: Q5KHIFI's residual correction tensors (Q6KHIFIRES8) provide negligible benefit at 32B scale (0.025 PPL improvement over Q5KM) because the base quantization is already near-optimal. This contrasts with 4B–8B scales where residual correction delivers measurable gains.
5. Q4KS imatrix synergy: Q4KS uniquely benefits from imatrix at 32B scale — the 2.8% quality gap vs Q4KHIFI collapses to just 0.2% with imatrix, making Q4K_S + imatrix the standout value proposition (fastest + smallest + near-HIFI quality).

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Practical Deployment Recommendations

For Most Users

→ Q4KM + imatrix Delivers excellent quality (+0.6% vs F16), strong speed (33.36 TPS), compact size (18.40 GiB), and universal llama.cpp compatibility. The safe, practical choice for 95% of deployments where absolute F16-equivalence isn't required.

For Quality-Critical Work

→ Q5KM + imatrix Achieves statistical F16-equivalence (-0.048% vs F16) with 64.5% memory reduction and 159% speedup. Standard compatibility makes it preferable to Q5KHIFI for most users requiring maximum fidelity.

For High-Throughput Serving

→ Q4KS + imatrix Fastest variant (35.00 TPS, +208% vs F16) with surprisingly good quality (+0.7% loss) and smallest viable footprint (17.48 GiB). Ideal when throughput matters more than marginal quality differences.

For Research on Quantization Limits

→ Q3KHIFI + imatrix Demonstrates that 3-bit quantization can achieve production-ready quality (+2.2% loss) on sufficiently large models. Valuable for characterizing lower bounds of viable quantization — but never use Q3KS.

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Decision Flowchart

Need absolute best quality?
├─ Yes → VRAM ≥ 24 GiB?
│        ├─ Yes → Q5KM + imatrix (F16-equivalent, standard build) ✅
│        └─ No  → Q4KM + imatrix (+0.6% loss, fits 20 GiB) ✅
└─ No → Need max throughput?
     ├─ Yes → Q4KS + imatrix (35 TPS, +0.7% loss) ✅
     └─ No → Need max compression?
              ├─ Yes → Q3KHIFI + imatrix (+2.2% loss) ✅
              └─ No  → Q4KM + imatrix (best balance) ✅

⚠️ Critical path exclusion: Q3KS is never on the optimal path — quality degradation is catastrophic regardless of constraints.

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Bottom Line Recommendations

Scenario	Recommended Variant	Rationale
Default / General Purpose	Q4KM + imatrix	Best balance of quality (+0.6%), speed (33.36 TPS), size (18.40 GiB), and compatibility
Maximum Quality	Q5KM + imatrix	Statistically F16-equivalent (-0.048%) with standard toolchain; skip HIFI (no meaningful advantage)
Maximum Throughput	Q4KS + imatrix	Fastest (35.00 TPS) with excellent quality (+0.7%); imatrix essential
Maximum Compression	Q3KHIFI + imatrix	Best Q3 quality (+2.2%); never use Q3KS (catastrophic failure)
Standard Tooling Required	Q5KM or Q4KM + imatrix	Both achieve excellent quality with universal llama.cpp compatibility

✅ 32B is the quantization resilience milestone: Large enough for near-lossless compression even at 3-bit levels (with intelligent quantization), yet small enough for dramatic efficiency gains. This scale demonstrates that quantization can deliver F16-equivalent quality at 1/3 the memory with 2.5–3.5× speed — a compelling value proposition for nearly all deployments.

⚠️ Golden rules for 32B:

1. NEVER use Q3KS — catastrophic failure mode unique to this scale
2. Prefer Q5KM over Q5KHIFI — identical quality with standard compatibility
3. Always use imatrix with Q4KS — closes 2.8% quality gap for free
4. Q4KM + imatrix is the pragmatic default — excellent quality with minimal constraints

Non-technical model anaysis and rankings

NOTE: This analysis does not include the HIFI models.

There are two very, very good candidates: Qwen3-32B-f16:Q3KM and Qwen3-32B-f16:Q4KM. These cover the full range of temperatures and were in the top 3 in nearly all question types.
Qwen3-32B-f16:Q4KM has a slightly better coverage across the temperature types.

Qwen3-32B-f16:Q5KS also did well, but because it's a larger model, it's not as highly recommended.

Despite being a larger parameter model, the Q2K and Q3K_S models are still such low quality that you should never use them.

You can read the results here: Qwen3-32b-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	12.3 GB	🚨 DO NOT USE. Produced garbage results and is not reliable.
Q3KS	⚡ Fast	14.4 GB	🚨 DO NOT USE. Not recommended, almost as bad as Q2_K.
🥈 Q3KM	⚡ Fast	16.0 GB	🥈 Got top 3 results across nearly all questions. Basically the same as K4KM.
Q4KS	🚀 Fast	18.8 GB	Not recommended. Got 2 2nd place results, one of which was the hello question.
🥇 Q4KM	🚀 Fast	19.8 GB	🥇 Recommended model Slightly better than Q3KM, and also got top 3 results across nearly all questions.
🥉 Q5KS	🐢 Medium	22.6 GB	🥉 Got good results across the temperature range.
Q5KM	🐢 Medium	23.2 GB	Not recommended. Got 2 top-3 placements, but nothing special.
Q6_K	🐌 Slow	26.9 GB	Not recommended. Got 2 top-3 placements, but also nothing special.
Q8_0	🐌 Slow	34.8 GB	Not recommended - no top 3 placements.

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: Qwen3-32B-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/Qwen3-32B/resolve/main/Qwen3-32B-f16%3AQ4KM.gguf (replace the quantised version with the one you want)
2. nano Modelfile and enter these details (again, replacing Q4KM with the version you want):

FROM ./Qwen3-32B-f16:Q4KM.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 Qwen3-32B-f16:Q4KM -f Modelfile

You will now see "Qwen3-32B-f16:Q4KM" 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.