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LLaVA v1.5-7B LLM Benchmark: An In-Depth AI Performance Analysis Report
Author: Gemini
1. Overview
This analysis covers the detailed performance benchmark results for the LLaVA v1.5-7B Large Language Model (LLM).
This test was conducted in standalone execution mode without pipeline integration (connect_pipeline: ✗) to measure the independent performance of the AI model.
All AI inference requests were processed concurrently (run_mode: "concurrent"), simulating a load scenario similar to a real-world multi-user environment.
2. Benchmark Test Environment
The AI model benchmark was performed using the following hardware and software stack.
- GPU: NVIDIA GeForce RTX 2060 SUPER (VRAM 8GB)
- CPU: Intel64 Family 6 Model 158 Stepping 13 (6 Threads)
- System: Windows 10 / 31.9 GB RAM
- AI Stack: CUDA 12.1 / PyTorch 2.5.1+cu121
- LLM Acceleration: Llama.cpp (AVX, AVX2, FMA, F16C Enabled)
- cuBLAS: Enabled
3. Benchmark Configuration
Three types of prompts were used to measure the LLM's diverse AI response quality and performance.
Each test was repeated 5 times (repeat_count: 5), with a minimum token length set to 15 (repeat_min_len: 15).
- Positive Sentiment:
- “I had such an amazing day today, I feel like I'm floating!”
- Negative Sentiment / Stress Situation:
- “The project deadline is looming, and I feel so anxious because I can't seem to focus on anything.”
- Technical Issue / Social Concern:
- “I'm excited about the advancement of AI technology, but at the same time, I'm worried it might reduce the number of jobs.”
4. Key AI Performance Metrics (Average)
The key performance metrics for the LLaVA LLM were measured as follows:
- Model Load Time: 1.85 sec
- Time to First Token (TTFT): 47.2 ms
- Token Processing Speed: 59.6 tokens/s
- Avg. Inference Time: 2.55 sec
TTFT is a critical metric for evaluating the perceived responsiveness of an AI. A result under 50ms suggests that excellent, real-time AI interaction is possible, where the user perceives almost no delay.
5. LLM Response Quality Analysis
The LLaVA v1.5-7B LLM showed strengths in text comprehension, but some limitations in context management.
- Emotional and Situational Understanding:
- In both positive and negative sentiment prompts, the AI accurately understood the emotional context and generated natural, empathetic responses. It demonstrated a high level of contextual understanding, such as suggesting specific advice (e.g., yoga, meditation) in stressful situations.
- Logical Response:
- In a prompt regarding the social impact of AI (job loss concerns), the AI provided a logical answer that balanced the positive aspects of technological advancement with potential concerns.
- Limitations:
- An issue was observed where unnecessary prompt structures (e.g., [Request]/[Response]) were included in some outputs. This indicates that multi-turn conversation or complex context-keeping functions may be somewhat unstable, potentially requiring post-processing in a real-world AI service application.
6. System Resource Efficiency
- VRAM Usage:
- In an 8GB VRAM environment (NVIDIA RTX 2060 SUPER), this LLM used an average of 5.78 GB of VRAM. This demonstrates that the LLaVA LLM can be run stably even on consumer-grade GPUs in the 8GB class.
- GPU and CPU Load:
- GPU power consumption averaged 174.6W, and temperature remained stable at an average of 50°C (Max 56°C).
- The average CPU usage was 47.6%, suggesting that multi-thread optimization is working effectively during the LLM inference process.
7. Conclusion
The results of this benchmark test demonstrate that the LLaVA v1.5-7B model has strong performance in text-centric sentiment analysis, logical response generation, and AI response speed (TTFT).
In particular, a TTFT of less than 50ms shows this LLM is highly suitable for conversational AI assistants or AI applications requiring real-time responses.
However, maintaining consistent prompt context in complex conversations remains a challenge for this LLM.
This benchmark is the result of a standalone AI model test without pipeline integration, and further analysis may be needed regarding performance changes when integrated into a pipeline.