328 lines
17 KiB
Markdown
328 lines
17 KiB
Markdown
---
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license: apache-2.0
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tags:
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- gguf
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- qwen
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- qwen3
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- qwen3-8b
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- qwen3-8b-gguf
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- llama.cpp
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- quantized
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- text-generation
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- reasoning
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- agent
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- chat
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- multilingual
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- matrix
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- q3_hifi
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- q4_hifi
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- q5_hifi
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base_model: Qwen/Qwen3-8B
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author: geoffmunn
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pipeline_tag: text-generation
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language:
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- en
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- zh
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- es
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- fr
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- de
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- ru
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- ar
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- ja
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- ko
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- hi
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---
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# Qwen3-8B-f16-GGUF
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This is a **GGUF-quantized version** of the **[Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B)** language model - an **8-billion-parameter** LLM from Alibaba's Qwen series, designed for **advanced reasoning, agentic behavior, and multilingual tasks**.
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Converted for use with `llama.cpp` and compatible tools like OpenWebUI, LM Studio, GPT4All, and more.
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## Why Use an 8B Model?
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The **Qwen3-8B** model represents a significant leap in capability while remaining remarkably accessible for local and edge deployment. It offers:
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- **Near-state-of-the-art reasoning, coding, and multilingual performance** among open 8B-class models
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- **Smooth inference on a single consumer GPU** (e.g., 16–24 GB VRAM) or fast CPU runtime with quantization
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- **Quantized versions (e.g., GGUF Q4_K_M, AWQ) that fit within ~6–8 GB of memory**, enabling use on mid-range hardware
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- **Strong performance on complex tasks** like document summarization, structured output generation, and agentic workflows
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It’s ideal for:
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- Local AI assistants that handle nuanced, multi-turn conversations
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- Self-hosted RAG pipelines with deep document understanding
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- Developers building production-grade on-prem AI features without cloud dependencies
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- Researchers and tinkerers seeking a capable yet manageable open-weight foundation
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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.
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# Qwen3 8B Quantization Guide: Cross-Bit Summary & Recommendations
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## Executive Summary
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At 8B scale, **quantization achieves exceptional resilience**—all bit widths deliver production-ready quality with imatrix, and even Q2_K becomes viable (+13.4% loss). The model's parameter redundancy provides a "sweet spot" where aggressive compression meets robust architecture. Q5_K_HIFI + imatrix achieves near-lossless fidelity (+0.27% vs F16), while Q4_K_M + imatrix offers the best balance of quality, speed, and compatibility:
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| Bit Width | Best Variant (+ imatrix) | Quality vs F16 | File Size | Speed | Memory | Viability |
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|-----------|--------------------------|----------------|-----------|-------|--------|-----------|
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| **Q5_K** | Q5_K_HIFI + imatrix | **+0.27%** ✅✅✅ | 5.62 GiB | 109.7 TPS | 5,754 MiB | Exceptional |
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| **Q4_K** | Q4_K_M + imatrix | **+1.3%** ✅✅ | 4.68 GiB | 125.5 TPS | 4,792 MiB | Excellent |
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| **Q3_K** | Q3_K_HIFI + imatrix | **+3.5%** ✅ | 2.15 GiB | 151.3 TPS | 2,202 MiB | Very Good |
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| **Q2_K** | Q2_K + imatrix | **+13.4%** ⚠️ | 3.05 GiB | 169.9 TPS | 3,134 MiB | Fair (viable) |
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💡 **Critical insight**: 8B represents the **inflection point** where Q2_K becomes genuinely viable with imatrix (+13.4% loss vs +35% at 1.7B). Q5_K_HIFI + imatrix achieves near-lossless quality (+0.27%), while Q4_K_M + imatrix provides the best practical balance. All variants are production-ready with imatrix.
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---
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## Bit-Width Recommendations by Use Case
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### ✅ Quality-Critical Applications
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**→ Q5_K_HIFI + imatrix**
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- Best perplexity at **10.1377 PPL (+0.27% vs F16)** — near-lossless fidelity
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- Only 0.27% precision loss represents the closest approach to F16 quality across all quantization levels
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- Requires custom llama.cpp build with `Q6_K_HIFI_RES8` support
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- ⚠️ **Never use Q5_K_S without imatrix** — quality degrades to +1.62% vs F16
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### ⚖️ Best Overall Balance (Recommended Default)
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**→ Q4_K_M + imatrix**
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- Excellent +1.3% precision loss vs F16 (PPL 10.2384)
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- Strong 125.5 TPS speed (+171% vs F16)
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- Compact 4.68 GiB file size (69.3% smaller than F16)
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- **Standard llama.cpp compatibility** — no custom build required
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- Ideal for most development and production scenarios
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### 🚀 Maximum Speed
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**→ Q2_K + imatrix**
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- Fastest variant at **169.9 TPS** (+267% vs F16)
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- Surprisingly viable quality at +13.4% loss with imatrix
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- ⚠️ **Never use without imatrix** — quality degrades catastrophically to +57.9% loss
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### 💎 Near-Lossless 3-Bit Option
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**→ Q3_K_HIFI + imatrix**
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- **Remarkable +3.5% precision loss** — exceptional for 3-bit quantization
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- 71.2% memory reduction (2,202 MiB vs 7,670 MiB)
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- Unique value: When you need maximum compression but cannot accept Q3_K_S quality
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- ⚠️ **27–38% slower than Q3_K_M** — significant speed trade-off
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### 📱 Extreme Memory Constraints (< 2.0 GiB)
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**→ Q3_K_S + imatrix**
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- Absolute smallest footprint (1.75 GiB file, 1,792 MiB runtime)
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- Acceptable +9.0% precision loss with imatrix
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- Only viable option under 2.0 GiB budget
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---
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## Critical Warnings for 8B Scale
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⚠️ **Q5_K quality ranking reversal with imatrix** — Q5_K_S + imatrix (10.1538 PPL) actually beats Q5_K_M + imatrix (10.1612 PPL) by 0.07 PPL points. This makes Q5_K_S + imatrix viable for speed-constrained deployments where the 3.2% speed advantage matters.
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⚠️ **Q4_K_S without imatrix is unusable** — Suffers +5.7% precision loss (10.6893 PPL) — the highest degradation of any Q4 variant at 8B scale. **Always pair Q4_K_S with imatrix** (reduces loss to +1.9%).
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⚠️ **Q2_K requires imatrix** — Without it, Q2_K suffers +57.9% precision loss (completely unusable). With imatrix, quality improves to +13.4% — viable for non-critical tasks.
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⚠️ **Q2_K_HIFI is strictly worse than Q2_K** — At 8B scale, Q2_K_HIFI loses to Q2_K on every metric (quality, speed, size, memory). Always prefer standard Q2_K over Q2_K_HIFI.
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⚠️ **Q3_K_HIFI requires no special handling** — Unlike at 0.6B/1.7B scales, Q3_K_HIFI at 8B delivers substantial quality gains (+3.5% vs F16 with imatrix) that justify its 13.5% memory premium over Q3_K_M.
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⚠️ **All Q3 variants are production-ready** — Even Q3_K_S 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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---
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## Memory Budget Guide
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| Available VRAM | Recommended Variant | Expected Quality | Why |
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|----------------|---------------------|------------------|-----|
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| **< 2.0 GiB** | Q3_K_S + imatrix | PPL 11.02, +9.0% loss ⚠️ | Only option that fits; quality acceptable for non-critical tasks |
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| **2.0 – 2.5 GiB** | Q3_K_M + imatrix | PPL 10.62, +5.1% loss ✅ | Best Q3 balance; production-ready quality |
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| **2.5 – 3.5 GiB** | Q2_K + imatrix | PPL 11.46, +13.4% loss ⚠️ | Maximum speed at 169.9 TPS; quality acceptable for simple tasks |
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| **3.5 – 5.0 GiB** | Q4_K_M + imatrix | PPL 10.24, +1.3% loss ✅ | Best balance of quality/speed/size; standard compatibility |
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| **5.0 – 6.5 GiB** | Q5_K_HIFI + imatrix | PPL 10.14, +0.27% loss ✅ | Near-lossless quality; requires custom build |
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| **> 15.3 GiB** | F16 | Best quality (baseline) | Only if absolute precision required |
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---
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## Cross-Bit Performance Comparison
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| Priority | Q2_K Best | Q3_K Best | Q4_K Best | Q5_K Best | Winner |
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|----------|-----------|-----------|-----------|-----------|--------|
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| **Quality (with imat)** | Q2_K (+13.4%) | Q3_K_HIFI (+3.5%) | Q4_K_M (+1.3%) | **Q5_K_HIFI (+0.27%)** ✅ | **Q5_K_HIFI** |
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| **Speed** | **Q2_K (169.9 TPS)** ✅ | Q3_K_S (223.5 TPS) | Q4_K_S (131.0 TPS) | Q5_K_S (113.3 TPS) | **Q2_K** |
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| **Smallest Size** | Q2_K (3.05 GiB) | **Q3_K_S (1.75 GiB)** ✅ | Q4_K_S (4.47 GiB) | Q5_K_S (5.32 GiB) | **Q3_K_S** |
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| **Best Balance** | Q2_K + imat | Q3_K_M + imat | **Q4_K_M + imat** ✅ | Q5_K_HIFI + imat | **Q4_K_M** |
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✅ = Recommended for general use
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⚠️ = Context-dependent (see warnings above)
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---
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## Scale-Specific Insights: Why 8B Quantizes So Well
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1. **Model redundancy threshold**: 8B represents the point where parameter count provides sufficient redundancy that quantization errors average out rather than accumulating catastrophically (unlike 0.6B/1.7B)
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2. **Q2_K viability inflection**: 8B is the smallest scale where Q2_K becomes genuinely viable with imatrix (+13.4% loss). At 4B, Q2_K + imatrix is +18.7%; at 1.7B, +35.0%. This demonstrates a clear scale-dependent improvement curve.
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3. **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. Q5_K_S benefits most (74.1% recovery), making it competitive with Q5_K_M when imatrix is used.
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4. **Residual quantization sweet spot**: Q5_K_HIFI's `Q6_K_HIFI_RES8` tensors provide maximal benefit at 8B scale — the 5 residual tensors capture precisely the right amount of quantization error without overhead.
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5. **Q4_K_HIFI behavior shift**: Unlike at 14B where imatrix *harms* Q4_K_HIFI, at 8B imatrix *helps* it (-1.1% PPL improvement) — demonstrating non-linear scale effects.
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6. **Q3_K viability threshold**: 8B is the smallest scale where Q3_K_HIFI achieves truly production-ready quality (+3.5% with imatrix) — below this, Q3 quantization requires careful validation.
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---
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## Decision Flowchart
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```mermaid
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Need best quality?
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├─ Yes → Q5_K_HIFI + imatrix (+0.27% loss)
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└─ No → Need max speed?
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├─ Yes → Q2_K + imatrix (169.9 TPS, +13.4% loss)
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└─ No → Need smallest size?
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├─ Yes → Memory < 2.0 GiB?
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│ ├─ Yes → Q3_K_S + imatrix (1,792 MiB, +9.0% loss)
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│ └─ No → Q2_K + imatrix (3,134 MiB, +13.4% loss, fastest)
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└─ No → Q4_K_M + imatrix (best balance, +1.3% loss, standard build)
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```
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---
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## Practical Deployment Recommendations
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### For Most Users
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**→ Q4_K_M + imatrix**
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Delivers excellent quality (+1.3% vs F16), strong speed (125.5 TPS), compact size (4.68 GiB), and universal llama.cpp compatibility. The safe, practical choice for 95% of deployments.
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### For Quality-Critical Work
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**→ Q5_K_HIFI + imatrix**
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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.
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### For Edge/Mobile Deployment
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**→ Q3_K_HIFI + imatrix**
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Best Q3 quality (+3.5% vs F16) with smallest viable footprint (2.15 GiB). Production-ready even without imatrix (+8.6% loss) — valuable for environments where imatrix generation isn't feasible.
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### For High-Throughput Serving
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**→ Q5_K_S + imatrix**
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Fastest Q5 variant (113.3 TPS) with surprisingly good quality (+0.42% vs F16) that actually beats Q5_K_M with imatrix. Ideal when every TPS matters and marginal quality differences are acceptable.
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### For Maximum Compression
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**→ Q2_K + imatrix**
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Only consider when memory/speed are absolutely critical and quality degradation is acceptable. At 8B scale, Q2_K + imatrix achieves +13.4% loss — viable for simple chatbots or non-critical inference.
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---
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## Bottom Line Recommendations
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| Scenario | Recommended Variant | Rationale |
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|----------|---------------------|-----------|
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| **Default / General Purpose** | Q4_K_M + imatrix | Best balance of quality (+1.3%), speed (125.5 TPS), size (4.68 GiB), and compatibility |
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| **Maximum Quality** | Q5_K_HIFI + imatrix | Near-lossless (+0.27% vs F16) with 64% memory reduction and 2.4× speedup |
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| **Maximum Speed** | Q2_K + imatrix | Fastest (169.9 TPS, +267% vs F16) with acceptable quality (+13.4% loss) |
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| **Minimum Size** | Q3_K_S + imatrix | Smallest footprint (1.75 GiB) with acceptable quality (+9.0% loss) |
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| **No imatrix available** | Q5_K_HIFI (no imat) | Still excellent (+1.11% vs F16); all variants usable but quality reduced |
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| **Extreme constraints** | Q3_K_S + imatrix | Only if memory < 2.0 GiB; +9.0% loss acceptable for non-critical tasks |
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⚠️ **Golden rules for 8B**:
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1. **Always use imatrix** — provides 62–76% precision recovery across all bit widths
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2. **Never use Q2_K without imatrix** — completely unusable (+57.9% loss)
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3. **Prefer Q2_K over Q2_K_HIFI** — HIFI is strictly worse on all metrics at 8B
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4. **Q5_K_S + imatrix beats Q5_K_M + imatrix** — unexpected quality ranking reversal
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5. **All four bit widths are viable** — choose based on constraints, not quality cliffs
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✅ **8B is the quantization sweet spot**: Large enough for robustness across all bit widths (even Q2_K), small enough for dramatic efficiency gains. This scale demonstrates that intelligent quantization can deliver near-F16 quality at 1/3 the memory with 2.4–3.5× speed — a compelling value proposition for nearly all deployments.
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## Non-technical model anaysis and rankings
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**NOTE:** This analysis does not include the HIFI models.
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There are numerous good candidates - lots of different models showed up in the top 3 across all the quesionts. However, **Qwen3-8B-f16:Q3_K_M** was a finalist in all but one question so is the recommended model (or Qwen3-8B-f16:Q3_HIFI). **Qwen3-8B-f16:Q5_K_S** did nearly as well and is worth considering,
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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.
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You can read the results here: [Qwen3-8B-analysis.md](Qwen3-8B-analysis.md)
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If you find this useful, please give the project a ❤️ like.
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## Non-HIFI recommentation table based on output
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| Level | Speed | Size | Recommendation |
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|-----------|-----------|-------------|---------------------------------------------------------------------------------------|
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| Q2_K | ⚡ Fastest | 3.28 GB | Not recommended. Came first in the bat & ball question, no other appearances. |
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| 🥉Q3_K_S | ⚡ Fast | 3.77 GB | 🥉 Came first and second in questions covering both ends of the temperature spectrum. |
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| 🥇 Q3_K_M | ⚡ Fast | 4.12 GB | 🥇 **Best overall model.** Was a top 3 finisher for all questions except the haiku. |
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| 🥉Q4_K_S | 🚀 Fast | 4.8 GB | 🥉 Came first and second in questions covering both ends of the temperature spectrum. |
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| Q4_K_M | 🚀 Fast | 5.85 GB | Came first and second in questions covering high temperature questions. |
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| 🥈 Q5_K_S | 🐢 Medium | 5.72 GB | 🥈 A good second place. Good for all query types. |
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| Q5_K_M | 🐢 Medium | 5.85 GB | Not recommended, no appeareances in the top 3 for any question. |
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| Q6_K | 🐌 Slow | 6.73 GB | Showed up in a few results, but not recommended. |
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| Q8_0 | 🐌 Slow | 8.71 GB | Not recommended, Only one top 3 finish. |
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## Build notes
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You can read the guide for building llama.cpp here: [HIFI_BUILD_GUIDE.md](https://github.com/geoffmunn/llama.cpp/blob/master/HIFI_BUILD_GUIDE.md).
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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](https://huggingface.co/geoffmunn/Qwen3-8B-f16/blob/main/Qwen3-8B-f16-imatrix-4697-generic.gguf)
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The imatrix was created as a generic mix of Wikipedia, mathmatics, and coding examples.
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### Source code
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You can use the HIFI GitHub repository to build it from source if you're interested: [https://github.com/geoffmunn/llama.cpp](https://github.com/geoffmunn/llama.cpp).
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Build notes: [HIFI_BUILD_GUIDE.md](https://github.com/geoffmunn/llama.cpp/blob/master/HIFI_BUILD_GUIDE.md)
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Improvements and feedback are welcome.
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## Usage
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Load this model using:
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- [OpenWebUI](https://openwebui.com) – self-hosted AI interface with RAG & tools
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- [LM Studio](https://lmstudio.ai) – desktop app with GPU support and chat templates
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- [GPT4All](https://gpt4all.io) – private, local AI chatbot (offline-first)
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- Or directly via `llama.cpp`
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Each quantized model includes its own `README.md` and shares a common `MODELFILE` for optimal configuration.
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Importing directly into Ollama should work, but you might encounter this error: `Error: invalid character '<' looking for beginning of value`.
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In this case try these steps:
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1. `wget https://huggingface.co/geoffmunn/Qwen3-8B-f16/resolve/main/Qwen3-8B-f16%3AQ3_K_M.gguf` (replace the quantised version with the one you want)
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2. `nano Modelfile` and enter these details (again, replacing Q3_K_M with the version you want):
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```text
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FROM ./Qwen3-8B-f16:Q3_K_M.gguf
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# Chat template using ChatML (used by Qwen)
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SYSTEM You are a helpful assistant
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TEMPLATE "{{ if .System }}<|im_start|>system
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{{ .System }}<|im_end|>{{ end }}<|im_start|>user
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{{ .Prompt }}<|im_end|>
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<|im_start|>assistant
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"
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PARAMETER stop <|im_start|>
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PARAMETER stop <|im_end|>
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# Default sampling
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PARAMETER temperature 0.6
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PARAMETER top_p 0.95
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PARAMETER top_k 20
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PARAMETER min_p 0.0
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PARAMETER repeat_penalty 1.1
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PARAMETER num_ctx 4096
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```
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The `num_ctx` value has been dropped to increase speed significantly.
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3. Then run this command: `ollama create Qwen3-8B-f16:Q3_K_M -f Modelfile`
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You will now see "Qwen3-8B-f16:Q3_K_M" in your Ollama model list.
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These import steps are also useful if you want to customise the default parameters or system prompt.
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## Author
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👤 Geoff Munn (@geoffmunn)
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🔗 [Hugging Face Profile](https://huggingface.co/geoffmunn)
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## Disclaimer
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This is a community conversion for local inference. Not affiliated with Alibaba Cloud or the Qwen team.
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