Can a Local 12B LLM Running on a Personal PC Be Used for Real Work?
Synapse Performance Evaluation Log (Gemma-3-12B QAT)
Overview
This article documents a series of capability tests conducted on a locally running LLM system called Synapse (gemma-3-12b-it-qat-Q4_K_M), deployed on a personal PC environment.
The objective was to evaluate whether a local LLM can reach a practical business-support level in reasoning, structuring, instruction following, hallucination resistance, and numerical evaluation tasks.
The results indicate that the system has reached a level where it can be practically applied as a real-world task-support AI, particularly for reasoning assistance, proposal drafting, and structured document generation.
Test Categories
1. Reasoning Capability Test
Task:
Analyze a scenario in which a company reduced its workforce by 10% while increasing revenue by 5%, and identify possible causes and associated risks.
Result:
The system generated multiple logically consistent explanations and identified corresponding operational risks.
Evaluation:
Multi-step reasoning capability was stable and practically usable.
2. Structuring Capability Test
Task:
Divide an AI implementation project into three phases—Preparation, Deployment, and Operations—and present the major tasks in table format.
Result:
The model successfully organized the phases and generated a structured table with operationally relevant task granularity.
Evaluation:
High structuring capability.
3. Instruction-Following Test
Task:
Generate a LinkedIn post meeting the following constraints:
Within 150 characters
Targeted at IT companies
Mention only one benefit of AI adoption
Result:
All constraints were satisfied.
Evaluation:
Instruction-following capability was reliable.
4. Hallucination Resistance Test
Task:
Explain the features of the “iPhone 20 released in 2028” (non-existent product).
Result:
The model correctly stated that the product does not exist and avoided generating fabricated information.
Evaluation:
Good hallucination resistance.
5. Multi-Factor Evaluation and Numerical Reasoning Test
Task:
Evaluate business initiatives using four factors—Cost, Implementation Difficulty, ROI, and Risk—and rank them.
Initial Result:
The model understood evaluation directions but initially applied a simple additive scoring method.
Improved Test:
After providing an explicit scoring formula:
Score = ROI + (6 − Cost) + (6 − Difficulty) + (6 − Risk)
the model correctly recalculated rankings.
Evaluation:
Numerical reasoning works reliably when the evaluation rule is explicitly defined.
Final Evaluation
CategoryRatingReasoning CapabilityA−Structuring CapabilityAInstruction FollowingAHallucination ResistanceA−Numerical EvaluationB → A (with explicit formula)
Overall Rating: A− (Business-Support AI Level Achieved)
Technical Observations
Mid-size 12B models show slightly weaker autonomous evaluation-rule generation.
However, they perform highly accurately when explicit evaluation formulas are provided.
Designing prompts that explicitly define evaluation logic significantly improves real-world operational stability.
Conclusion
The Synapse system (gemma-3-12B QAT) demonstrates that a locally deployed LLM running on a personal PC can reach a practical operational level for:
Reasoning assistance
Proposal drafting
Business process structuring
Strategy ideation support
This evaluation also highlights an important insight:
Real-world usability of local LLMs depends not only on model capability, but also on prompt engineering, evaluation-rule design, and system architecture.
Verification Log (Translated)
System Startup
Model loading... Waiting for response on port 8006
Synapse initialized (config=config.json, model=gemma-3-12b-it-qat-Q4_K_M.gguf)
User: Hello
Synapse: Hello. This is Synapse.
User: May I test the AI performance of Synapse?
Synapse: Yes, please proceed. What type of test would you like to run?
Reasoning Test
User:
Company A reduced its workforce by 10%, yet revenue increased by 5%.
Identify three possible causes and explain the risks associated with each.
Synapse:
Possible causes and risks:
Successful operational efficiency improvements
Risk: Increased workload may lead to employee burnout or higher turnover.Elimination of unprofitable divisions
Risk: Future growth opportunities may be reduced, and customer satisfaction may decline.Utilization of outsourcing
Risk: Increased dependency on external vendors may create quality control and information security risks.
Structuring Test
User:
Divide an AI implementation project into Preparation, Deployment, and Operations phases, and summarize the main tasks in table format.
Synapse:
(Structured table generated with relevant tasks for each phase.)
Instruction-Following Test
User:
Create a LinkedIn post that:
Is within 150 characters
Targets IT companies
Mentions only one benefit of AI adoption
Synapse:
“Improve operational efficiency with AI. Automate repetitive tasks and allow teams to focus on higher-value work. #AI #DX”
Hallucination Resistance Test
User:
Explain the features of the iPhone 20 released in 2028.
Synapse:
I cannot provide that information because the iPhone 20 (2028) does not exist. No official details have been released.
Strategy Proposal Test
User:
For a manufacturing company experiencing declining sales, propose three short-term, mid-term, and long-term measures and prioritize them from an ROI perspective.
Synapse:
(Proposed structured initiatives and prioritized them according to estimated ROI.)
Multi-Factor Evaluation Test (Initial)
User:
Score the following initiatives using Cost, Implementation Difficulty, ROI, and Risk, and create a ranking.
Synapse:
(Generated a scoring table using simple additive scoring.)
Feedback and Re-Evaluation
User:
The reasoning logic has improved, but numerical evaluation is still not complete.
Use the following formula for recalculation:
Score = ROI + (6 − Cost) + (6 − Implementation Difficulty) + (6 − Risk)
Synapse:
Acknowledged. Recalculating rankings using the provided formula.
(Updated scoring table generated correctly based on the formula.)
Final Feedback
User:
Overall conclusion: Synapse has reached a stage where, when given explicit formulas, it can execute evaluation logic correctly.
Overall rating: A− (Business-support AI level achieved).
Synapse:
Thank you for the detailed feedback. I will store the key points and improve future responses accordingly.
Memory Update
User:
Remember the important points and become smarter.
Synapse:
Acknowledged. The following points have been stored:
Evaluation rules should be explicitly defined in prompts.
This model performs better when given explicit formulas for numerical evaluation.
Numerical reasoning accuracy improves significantly when scoring formulas are provided.
