claude-home/ollama-model-testing.md

title	description	type	domain	tags
Ollama Model Testing Log	Testing log tracking Ollama model evaluations with performance observations, VRAM requirements, and suitability ratings for different use cases on a 16GB GPU workstation.	reference	development	ollama llm model-testing vram gpu deepseek glm

Ollama Model Testing Log

Track models tested, performance observations, and suitability for different use cases.

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Quick Summary

Model	Date Tested	Primary Use Case	Rating	Notes
GLM-4.7:cloud	2026-02-04	General purpose	⭐⭐⭐⭐	Cloud-hosted, fast, good reasoning
deepseek-v3.1:671b-cloud	2026-02-04	Complex reasoning	⭐⭐⭐⭐⭐	Cloud, very capable, slower response

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Model Testing Details

GLM-4.7:cloud

Date Tested: 2026-02-04

Model Info:

• Size/Parameters: Unknown (cloud)
• Quantization: N/A (cloud)
• Base Model: GLM-4.7 by Zhipu AI

Performance:

• Response Speed: Fast
• RAM/VRAM Usage: Cloud (local minimal)
• Context Window: 128k

Testing Use Cases:

• Code generation
• General Q&A
• Creative writing
• Data analysis
• Task planning
• Other:

Observations:

• Strengths: Fast response, good at general reasoning
• Weaknesses: Cloud dependency
• Resource requirements: Minimal local resources
• Output quality: Solid for most tasks
• When to use this model: Daily tasks, coding help, general assistance

Verdict: ⭐⭐⭐⭐

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deepseek-v3.1:671b-cloud

Date Tested: 2026-02-04

Model Info:

• Size/Parameters: 671B (cloud)
• Quantization: N/A (cloud)
• Base Model: DeepSeek-V3 by DeepSeek

Performance:

• Response Speed: Moderate (671B model)
• RAM/VRAM Usage: Cloud (local minimal)
• Context Window: 128k+

Testing Use Cases:

• Code generation
• General Q&A
• Creative writing
• Data analysis
• Task planning
• Other:

Observations:

• Strengths: Very capable, excellent reasoning, great with complex tasks
• Weaknesses: Slower response, cloud dependency
• Resource requirements: Minimal local resources
• Output quality: Top-tier, handles complex multi-step reasoning well
• When to use this model: Complex coding tasks, deep analysis, planning

Verdict: ⭐⭐⭐⭐⭐

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Models to Test

Local Models (16GB GPU Compatible)

Small & Fast (2-6GB VRAM at Q4):

• phi3:mini - 3.8B params, great for quick tasks ~2.2GB
• llama3.1:8b - 8B params, excellent all-rounder ~4.7GB
• qwen2.5:7b - 7B params, strong reasoning ~4.3GB
• gemma2:9b - 9B params, Google's small model ~5.5GB

Medium Capability (6-10GB VRAM at Q4):

• mistral:7b - 7B params, classic workhorse ~4.1GB
• llama3.1:14b - 14B params, higher quality ~8.2GB
• qwen2.5:14b - 14B params, strong multilingual ~8.1GB

Specialized:

• deepseek-coder-v2:lite - 16B params, optimized for coding ~8.7GB
• codellama:7b - 7B params, coding specialist ~4.1GB

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General Notes

Any overall observations, preferences, or patterns discovered during testing.

Initial Impressions:

• Cloud models (GLM-4.7, DeepSeek-V3) provide excellent quality without local resources
• Planning to test local models for privacy, offline use, and comparing quality/speed trade-offs
• Focus will be on models that fit comfortably in 16GB VRAM for smooth performance

VRAM Estimates at Q4 Quantization:

• 3B-4B models: ~2-3GB
• 7B-8B models: ~4-5GB
• 14B models: ~8-9GB
• Leaves room for context window and system overhead

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Last Updated: 2026-02-04