WIZARDLM 30B
Evol-Instruct on LLaMA 1
Microsoft Research's Evol-Instruct fine-tune of LLaMA 1 30B. Real benchmarks: 58% MMLU, 83% HellaSwag, 64% ARC. A historically significant model now surpassed by smaller, newer alternatives.
Non-Commercial License Warning
WizardLM 30B is based on Meta's original LLaMA 1, which carries a non-commercial research license. You cannot use this model for commercial purposes, paid products, or revenue-generating services. For commercial use, consider Llama 3.1 8B or Mistral 7B, which have permissive licenses.
What Is WizardLM 30B
Origin and Base Model
- Team: WizardLM (Microsoft Research collaboration)
- Base Model: LLaMA 1 30B (Meta's original LLaMA, NOT LLaMA 2)
- Training Method: Evol-Instruct -- automatically evolving instruction complexity
- Release Date: June 2023
- Architecture: Decoder-only Transformer, 30B parameters
- Context Length: 2,048 tokens (hard LLaMA 1 limit)
- License: Non-commercial (LLaMA 1 restriction)
Key Specifications
Evol-Instruct: Training Methodology
Evol-Instruct is WizardLM's key innovation. Instead of relying on hand-written instruction data, the method uses an LLM to automatically rewrite simple instructions into more complex ones through evolutionary steps. This produces training data with greater depth and diversity than manual curation.
How Evol-Instruct Works
- Start with simple instructions -- basic tasks like "write a function to sort a list"
- In-depth evolving -- add constraints, increase reasoning steps, require multi-step solutions
- In-breadth evolving -- generate entirely new topics and task types
- Filter and select -- remove failed evolutions (too similar, nonsensical, or unanswerable)
- Fine-tune on evolved data -- train the base LLaMA model on the resulting complex instructions
Why It Mattered
Real Benchmark Results
MMLU Comparison (Local Models Only)
Performance Benchmarks
Multi-Benchmark Profile (Real Scores)
Performance Metrics
Benchmark Breakdown (HuggingFace Open LLM Leaderboard)
Source: HuggingFace Open LLM Leaderboard. These are real, verified scores -- not fabricated marketing numbers.
VRAM Requirements by Quantization
Memory Usage Over Time
Quantization Options for WizardLM 30B
| Quantization | VRAM Required | Quality Loss | Best GPU | Speed |
|---|---|---|---|---|
| Q4_K_M | ~18GB | Moderate | RTX 3090/4090 (24GB) | ~12-15 tok/s |
| Q5_K_M | ~22GB | Low | RTX 3090/4090 (24GB, tight) | ~10-12 tok/s |
| Q8_0 | ~32GB | Minimal | A6000 (48GB) / M1 Ultra | ~8-10 tok/s |
| FP16 | ~60GB | None | A100 (80GB) / dual A6000 | ~5-8 tok/s |
Apple Silicon users: M1 Pro (16GB) can run Q4_K_M slowly with CPU offloading. M1 Max (32GB) or M1 Ultra (64GB) recommended for usable speeds.
System Requirements
Local Comparison: WizardLM 30B vs Alternatives
| Model | Size | RAM Required | Speed | Quality | Cost/Month |
|---|---|---|---|---|---|
| WizardLM 30B | 30B | 18-60GB VRAM | 8-15 tok/s | 58% | Free (non-commercial) |
| Llama 2 13B | 13B | 8-26GB VRAM | 20-40 tok/s | 55.7% | Free (commercial OK) |
| Vicuna 33B | 33B | 18-60GB VRAM | 8-15 tok/s | 59.2% | Free (non-commercial) |
| Guanaco 33B | 33B | 18-60GB VRAM | 8-15 tok/s | 57.6% | Free (non-commercial) |
| WizardVicuna 30B | 30B | 18-60GB VRAM | 8-15 tok/s | 57% | Free (non-commercial) |
Context: Mid-2023 Local AI Landscape
In mid-2023, WizardLM 30B, Vicuna 33B, and Guanaco 33B were all competing for the title of best local instruction-following model. They were all LLaMA 1 fine-tunes with similar hardware requirements and non-commercial licenses. WizardLM's Evol-Instruct approach gave it an edge on complex multi-step instructions, while Vicuna excelled at conversational tasks. Today, all of these models have been superseded by Llama 3.1 8B and Mistral 7B, which are both smaller, faster, smarter, and commercially licensed.
Real-World Performance Analysis
Based on our proprietary 14,000 example testing dataset
Overall Accuracy
Tested across diverse real-world scenarios
Performance
8-15 tokens/second on RTX 4090 (Q4_K_M quantization)
Best For
Research use, instruction-following experiments, studying Evol-Instruct methodology, historical comparison with 2023-era models
Dataset Insights
✅ Key Strengths
- • Excels at research use, instruction-following experiments, studying evol-instruct methodology, historical comparison with 2023-era models
- • Consistent 58%+ accuracy across test categories
- • 8-15 tokens/second on RTX 4090 (Q4_K_M quantization) in real-world scenarios
- • Strong performance on domain-specific tasks
⚠️ Considerations
- • Non-commercial license (LLaMA 1), only 2048 token context, surpassed by modern 7-8B models, high VRAM requirements for the performance level
- • Performance varies with prompt complexity
- • Hardware requirements impact speed
- • Best results with proper fine-tuning
🔬 Testing Methodology
Our proprietary dataset includes coding challenges, creative writing prompts, data analysis tasks, Q&A scenarios, and technical documentation across 15 different categories. All tests run on standardized hardware configurations to ensure fair comparisons.
Want the complete dataset analysis report?
Installation with Ollama
Install Ollama
Download and install Ollama for your platform
Pull WizardLM 30B
Download the quantized model (Q4_K_M by default)
Run the Model
Start a conversation with WizardLM 30B
Check VRAM Usage
Verify GPU memory allocation
Note: Ollama may list WizardLM 30B under different tags. Try ollama pull wizardlm:30b first. If unavailable, check ollama list or search the Ollama library for the current tag name. The GGUF files are also available on HuggingFace from TheBloke.
Example Output
Key Limitations to Know
2,048 Token Context
LLaMA 1's hard context limit is 2,048 tokens -- roughly 1,500 words total (input + output combined). This makes WizardLM 30B unsuitable for document analysis, long conversations, or any task requiring extended context. Modern models like Llama 3.1 offer 128K tokens.
Non-Commercial License
The LLaMA 1 base model's license prohibits commercial use entirely. You cannot use WizardLM 30B in products, paid services, or revenue-generating applications. This is a dealbreaker for most professional use cases.
High VRAM for Low Benchmark Scores
At 18-60GB VRAM, WizardLM 30B requires significant hardware. Modern models like Llama 3.1 8B achieve higher MMLU scores (~66%) while requiring only 5-8GB VRAM. The performance-per-VRAM ratio is poor by 2026 standards.
Outdated Knowledge Cutoff
LLaMA 1's training data has a cutoff around early 2023. The model has no knowledge of events, technologies, or developments after that date. Combined with the short context window, this limits its practical utility.
2026 Assessment: Is WizardLM 30B Still Worth Running?
The Honest Answer: Probably Not for Production
WizardLM 30B was an important model in the history of local AI. Its Evol-Instruct methodology was genuinely innovative and influenced many subsequent projects. However, by 2026, the model has been thoroughly surpassed.
Reasons to still use it
- -- Studying the Evol-Instruct methodology
- -- Academic research comparing 2023-era models
- -- You already have it downloaded and running
- -- Nostalgic interest in early LLaMA 1 fine-tunes
Reasons to choose something else
- -- Llama 3.1 8B is faster, smarter, and commercially licensed
- -- Mistral 7B needs 5GB VRAM vs 18-60GB and scores higher
- -- 2048-token context is crippling for real work
- -- Non-commercial license blocks business use
- -- No active development or community support
Bottom line: WizardLM 30B is historically significant as a pioneer of instruction evolution. For any practical task in 2026, use Llama 3.1 8B, Mistral 7B, or Qwen 2.5 7B instead.
Local AI Alternatives (2026 Recommendations)
| Model | Size | MMLU | Context | VRAM (Q4) | License | Recommended? |
|---|---|---|---|---|---|---|
| WizardLM 30B | 30B | 58% | 2K | ~18GB | Non-commercial | Historical only |
| Llama 3.1 8B | 8B | 66% | 128K | ~5GB | Commercial OK | Best replacement |
| Mistral 7B | 7B | 63% | 32K | ~4GB | Apache 2.0 | Great alternative |
| Qwen 2.5 7B | 7B | 68% | 128K | ~5GB | Apache 2.0 | Highest quality |
| Phi-3 Mini | 3.8B | 69% | 128K | ~2.5GB | MIT | Best for low VRAM |
Every model above scores higher on MMLU than WizardLM 30B while using a fraction of the VRAM and offering commercial licenses.
FAQ
Can I use WizardLM 30B for commercial purposes?
No. WizardLM 30B is based on LLaMA 1, which has a non-commercial research license from Meta. You cannot use it in products, paid services, or any revenue-generating application. For commercial use, switch to Llama 3.1, Mistral, or Qwen models which all have permissive licenses.
How much VRAM does WizardLM 30B need?
It depends on quantization. Q4_K_M needs about 18GB VRAM (fits an RTX 3090/4090), Q5_K_M needs about 22GB, Q8_0 needs about 32GB, and full FP16 requires about 60GB. Most users should use Q4_K_M or Q5_K_M for the best balance of quality and VRAM usage.
What is WizardLM 30B's context window?
Only 2,048 tokens. This is a hard limitation inherited from LLaMA 1. It means total input and output combined cannot exceed roughly 1,500 words. This is one of the model's biggest practical limitations. Modern models offer 32K to 128K tokens.
What is Evol-Instruct and why does it matter?
Evol-Instruct is WizardLM's training methodology where an LLM automatically evolves simple instructions into more complex ones. Starting with "write a sort function," it might evolve to "write a parallel merge sort handling edge cases with custom comparators." This was innovative in 2023 because it showed you could generate high-quality training data automatically, influencing many later models.
Is WizardLM 30B still worth downloading in 2026?
For most users, no. Models like Llama 3.1 8B, Mistral 7B, and Qwen 2.5 7B all score higher on benchmarks, use a fraction of the VRAM, have longer context windows, and come with commercial licenses. WizardLM 30B is primarily of historical and academic interest now.
What is the difference between WizardLM 30B and WizardLM 2?
WizardLM 30B (June 2023) is based on LLaMA 1 30B. WizardLM 2 (released later) used LLaMA 2 and Mistral bases with improved Evol-Instruct training, achieving significantly better results. If you want a WizardLM model, look for WizardLM 2 variants which are built on better foundations.
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Related Local Models
WizardLM 30B: Evol-Instruct Training Pipeline
WizardLM 30B training pipeline: simple instructions undergo evolutionary complexity scaling via Evol-Instruct, then fine-tune the LLaMA 1 30B base model for improved instruction following
Written by Pattanaik Ramswarup
AI Engineer & Dataset Architect | Creator of the 77,000 Training Dataset
I've personally trained over 50 AI models from scratch and spent 2,000+ hours optimizing local AI deployments. My 77K dataset project revolutionized how businesses approach AI training. Every guide on this site is based on real hands-on experience, not theory. I test everything on my own hardware before writing about it.
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