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

# Ollama Model Testing Log

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

---

## 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 |
| | | | | |

---

## 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:**
- [x] Code generation
- [x] General Q&A
- [ ] Creative writing
- [x] 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:** ⭐⭐⭐⭐

---

### 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:**
- [x] Code generation
- [x] General Q&A
- [ ] Creative writing
- [x] Data analysis
- [x] 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:** ⭐⭐⭐⭐⭐

---

## 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

---

## 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

---

*Last Updated: 2026-02-04*