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feat: Add Qwen3.5 benchmark comparison (2B vs 4B)
- Added benchmark scripts for model evaluation - Qwen3.5-2B-GGUF: 5.32 tok/s (winner) - Qwen3.5-4B-GGUF: 4.67 tok/s - Imported results into dashboard database - Added system information and performance matrices - 2B model shows 12% better throughput and 2.3x parameter efficiency Files: - scripts/benchmark_qwen.py: Benchmark execution tool - scripts/import_benchmarks_direct.py: Database import tool - benchmark_results.json: Raw benchmark data - BENCHMARK_COMPARISON_REPORT.md: Detailed analysis report - SYSTEM_INFO_AND_MATRIX.md: Hardware specs and performance matrices Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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‎BENCHMARK_COMPARISON_REPORT.md‎

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# Qwen3.5-2B-GGUF vs Qwen3.5-4B-GGUF Benchmark Report
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**Date:** April 7, 2026
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**Test Environment:** Lemonade Eval Dashboard
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**Backend:** lemonade-server (llamacpp)
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**Device:** GPU
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---
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## Executive Summary
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Both Qwen3.5 models were benchmarked using the Lemonade Eval Dashboard with identical test conditions. The **Qwen3.5-2B-GGUF** model demonstrated superior token generation throughput compared to the Qwen3.5-4B-GGUF model.
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### Winner: Qwen3.5-2B-GGUF
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- **Best TPS:** 5.32 tokens/second (at 64-token prompt)
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- **Advantage:** ~12% faster than 4B variant
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---
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## Detailed Results
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### Qwen3.5-2B-GGUF
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| Prompt Length | Tokens/Second (Mean) | Std Dev | Latency (Mean) |
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|---------------|---------------------|---------|----------------|
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| 64 tokens | 5.32 tok/s | ±0.43 | 5.42s |
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| 128 tokens | 5.20 tok/s | ±0.19 | 5.90s |
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| 256 tokens | 4.43 tok/s | ±0.45 | 6.43s |
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**Best Performance:** 5.32 tok/s at 64-token prompt length
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### Qwen3.5-4B-GGUF
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| Prompt Length | Tokens/Second (Mean) | Std Dev | Latency (Mean) |
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|---------------|---------------------|---------|----------------|
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| 64 tokens | 4.67 tok/s | ±1.55 | 4.99s |
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| 128 tokens | 3.53 tok/s | ±0.54 | 4.31s |
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| 256 tokens | 3.82 tok/s | ±0.60 | 6.30s |
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**Best Performance:** 4.67 tok/s at 64-token prompt length
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---
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## Performance Comparison
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### Token Generation Speed (TPS)
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```
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Qwen3.5-2B-GGUF ████████████████████████████████████████ 5.32 tok/s
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Qwen3.5-4B-GGUF ████████████████████████████████ 4.67 tok/s
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```
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### Key Findings
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1. **Speed Advantage:** Qwen3.5-2B-GGUF is approximately 12% faster than Qwen3.5-4B-GGUF in token generation
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- 2B: 5.32 tok/s (best)
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- 4B: 4.67 tok/s (best)
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2. **Consistency:** Qwen3.5-2B-GGUF shows more consistent performance across different prompt lengths
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- 2B: Lower standard deviation (0.19-0.45)
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- 4B: Higher variance at shorter prompts (1.55 std dev at 64 tokens)
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3. **Prompt Length Impact:** Both models show decreased performance with longer prompts
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- 2B: 17% drop from 64 to 256 tokens
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- 4B: 18% drop from 64 to 256 tokens
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4. **Latency:** Qwen3.5-4B-GGUF has slightly lower latency at 128-token prompts
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- 2B: 5.90s average
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- 4B: 4.31s average
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---
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## Test Configuration
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- **Iterations:** 5 benchmark runs + 1 warmup
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- **Output Tokens:** 32 tokens per run
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- **Prompt Lengths:** 64, 128, 256 tokens
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- **Backend:** llamacpp (GPU)
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- **Quantization:** Q4_K_XL (UD)
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---
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## Database Records
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Results have been imported into the Lemonade Eval Dashboard database:
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- **Models Created:** Qwen3.5-2B-GGUF, Qwen3.5-4B-GGUF
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- **Runs Created:** 2 benchmark runs
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- **Metrics Recorded:** 12 total metrics (6 per model)
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### Database IDs
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- Qwen3.5-2B-GGUF Model ID: `3d32510e-27d8-4ce1-93fc-5b59ddc2e343`
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- Qwen3.5-4B-GGUF Model ID: `cc182c7f-1c76-489c-891a-b44230352ec9`
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---
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## Recommendations
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### For High-Throughput Applications
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**Choose Qwen3.5-2B-GGUF**
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- Better sustained token generation rates
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- More consistent performance across prompt lengths
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- Lower memory footprint
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### For Accuracy-Critical Applications
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**Consider Qwen3.5-4B-GGUF**
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- Larger model may have better reasoning capabilities
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- Slightly better latency at medium prompt lengths
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- Trade-off between speed and potential accuracy
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---
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## Files Generated
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1. `benchmark_results.json` - Raw benchmark data
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2. `scripts/benchmark_qwen.py` - Benchmark script
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3. `scripts/import_benchmarks_direct.py` - Database import script
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4. `BENCHMARK_COMPARISON_REPORT.md` - This report
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---
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*Report generated by Lemonade Eval Dashboard Benchmarking System*

‎SYSTEM_INFO_AND_MATRIX.md‎

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# System Information and Benchmark Matrix
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## Hardware Configuration
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| Component | Specification |
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|-----------|---------------|
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| Platform | Windows 11 (10.0.26200) |
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| Processor | AMD Ryzen AI (Family 26, Model 36) |
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| CPU Cores | 24 cores |
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| RAM | 79.62 GB |
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| Python Version | 3.12.11 |
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## Software Stack
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| Component | Version/Details |
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|-----------|-----------------|
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| lemonade-server | 10.0.0 |
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| Backend | llamacpp (vulkan) |
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| Quantization | Q4_K_XL (UD) |
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| GPU Backend | Vulkan |
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## Model Benchmark Matrix
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### Token Generation Speed (tokens/second)
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| Model | 64-token prompt | 128-token prompt | 256-token prompt | Best |
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|-------|-----------------|------------------|------------------|------|
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| Qwen3.5-2B-GGUF | 5.32 ± 0.43 | 5.20 ± 0.19 | 4.43 ± 0.45 | **5.32** |
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| Qwen3.5-4B-GGUF | 4.67 ± 1.55 | 3.53 ± 0.54 | 3.82 ± 0.60 | 4.67 |
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| **Advantage** | **+12.3%** | **+31.8%** | **+14.0%** | **2B wins** |
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### Latency (Time to First Token in seconds)
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| Model | 64-token prompt | 128-token prompt | 256-token prompt | Best |
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|-------|-----------------|------------------|------------------|------|
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| Qwen3.5-2B-GGUF | 5.42 ± 0.56 | 5.90 ± 0.24 | 6.43 ± 0.69 | 5.42s |
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| Qwen3.5-4B-GGUF | 4.99 ± 1.32 | 4.31 ± 0.34 | 6.30 ± 0.74 | **4.31s** |
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| **Advantage** | 4B faster | **4B faster** | Similar | **4B wins** |
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### Performance per Billion Parameters
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| Model | Parameters | Best TPS | TPS per Billion |
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|-------|------------|----------|-----------------|
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| Qwen3.5-2B-GGUF | ~2B | 5.32 | **2.66 tok/s/B** |
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| Qwen3.5-4B-GGUF | ~4B | 4.67 | 1.17 tok/s/B |
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| **Efficiency** | | | **2B is 2.3x more efficient** |
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## Performance Charts
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### Token Generation Speed Comparison
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```
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Tokens/second (higher is better)
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Qwen3.5-2B-GGUF ████████████████████████████████████████ 5.32
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Qwen3.5-4B-GGUF ████████████████████████████████ 4.67
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0 2 4 6
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```
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### Performance by Prompt Length
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```
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TPS by Prompt Length
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64 tokens:
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2B ████████████████████████████████████████ 5.32
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4B ████████████████████████████████ 4.67
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128 tokens:
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2B ██████████████████████████████████████ 5.20
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4B ██████████████████████████ 3.53
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256 tokens:
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2B ████████████████████████████████ 4.43
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4B ██████████████████████████████ 3.82
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```
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## Model Characteristics
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| Attribute | Qwen3.5-2B-GGUF | Qwen3.5-4B-GGUF |
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|-----------|-----------------|-----------------|
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| Model Size | 1.34 GB | 2.91 GB |
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| Quantization | Q4_K_XL | Q4_K_XL |
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| Family | Qwen | Qwen |
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| Type | LLM (Vision-capable) | LLM (Vision-capable) |
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| Best Use Case | High-throughput | Better reasoning |
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## Database Records
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| Table | Records Created | IDs |
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|-------|-----------------|-----|
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| models | 2 | 3d32510e..., cc182c7f... |
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| runs | 2 | Benchmark runs |
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| metrics | 12 | 6 per model |
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## Benchmark Methodology
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### Test Parameters
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| Parameter | Value |
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|-----------|-------|
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| Iterations | 5 |
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| Warmup Runs | 1 |
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| Output Tokens | 32 |
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| Prompt Lengths | 64, 128, 256 tokens |
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| Backend | llamacpp (GPU/Vulkan) |
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### Metrics Collected
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- **TPS (Tokens Per Second):** Primary throughput metric
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- **TTFT (Time To First Token):** Latency metric
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- **Standard Deviation:** Consistency measure
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- **Min/Max Values:** Performance bounds
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## Files Created
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| File | Purpose |
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|------|---------|
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| `scripts/benchmark_qwen.py` | Benchmark execution script |
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| `scripts/import_benchmarks.py` | Dashboard API import |
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| `scripts/import_benchmarks_direct.py` | Direct DB import |
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| `benchmark_results.json` | Raw benchmark data |
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| `BENCHMARK_COMPARISON_REPORT.md` | Detailed report |
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| `SYSTEM_INFO_AND_MATRIX.md` | This document |
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## Conclusions
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1. **Speed Winner:** Qwen3.5-2B-GGUF delivers 12% better throughput
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2. **Latency Winner:** Qwen3.5-4B-GGUF has marginally better latency at medium prompts
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3. **Efficiency Winner:** Qwen3.5-2B-GGUF is 2.3x more parameter-efficient
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4. **Recommendation:** Use 2B for high-throughput, 4B for complex reasoning tasks
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---
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*Generated: April 7, 2026*
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*Lemonade Eval Dashboard Benchmarking System*

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