LLM Llama3 8B + Sentiment Analyzer vs. Community 4-bit Benchmark Comparison (RTX 2060 8GB benchmark Performance)

LLM Pipeline Benchmark Comparison (RTX 2060 8GB)

Date: 2025-09-19

Target GPU: RTX 2060 8GB Series (RTX 2060 Super)

Benchmark Tool: PKC MARK (Custom-developed)

Format: 4-bit Quantization (Q4)

Overview

This article compares the self-measured results of the Llama3 8B + kluebert-v2 pipeline with recent benchmark cases shared within the community, which utilize 4-bit quantization (e.g., Q4_K_M, Q4_0) on an RTX 2060 8GB (or 2060 Super 8GB) based system.

The analysis specifically focuses on Practical Usability (User Experience, UX) within a memory-constrained environment (8GB VRAM).

Detailed Comparison Table

Llama 3 8B (4-bit) Recent Community Examples

Model / Pipeline	bllossom-8B (Conversational LLM) + kluebert-v2 (Pipeline)	7B/8B Class (e.g., Llama-2-7B, Llama-3.1-8B. Single model)
Quantization	4-bit (Q4 Series)	4-bit (Q4_K_M, Q4_0)
GPU	RTX 2060 8GB	RTX 2060 Super 6GB / RTX 2060 8GB
TTFT (Time to First Token Latency)	~39–52 ms (Instant Response)	~1.0–1.1 s (Noticeable Latency)
TPS (Tokens per Second)	~46 tok/s (8B standard)	8B: ~50–52 tok/s · 7B: ~61 tok/s
VRAM Consumption	~6.7 GB (8B)	8B: ~7.5–8.0 GB · 7B: ~6–7 GB
Output Quality	Consistent Korean Output + Sentiment Tagging	Good language quality, but no sentiment tagging
Practical Usability	Very low latency leads to superior conversational UX	High latency and VRAM limitations

※ PKC figures are summarized results from a custom benchmark, and community values are based on representative examples.

Conclusion

The LLM 8B + kluebert-v2 (4bit, PKC MARK) pipeline offers significantly lower latency and stable Korean output quality compared to community 4-bit benchmarks, giving it a clear advantage in real-world conversational UX.

While the TPS (processing speed) is similar, the Responsiveness (TTFT) is overwhelmingly low, providing immediate feedback to the user. Furthermore, the inclusion of Sentiment Tagging functionality makes it much more practically useful than a simple LLM.

Specifically, with VRAM consumption remaining stable at under 7GB, this pipeline is shown to be highly suitable for operation in an 8GB VRAM environment.

 

This analysis was conducted by leveraging AI to process and compare the result logs from the custom-developed PKC benchmark tool against community case studies.