Under a simulated load of 100 concurrent users, Taklif.AI averaged 30 seconds end-to-end response time per request¶
Claim · All claims
Evidence · 1 study · designr2·q1
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person · recheck by 2029: tool and model accuracy changes quickly
Subclaims¶
Reading the evidence codes
q is the design tier, i the impact, n the sample, and the kind with its r
the rigour judged within that kind: what each code means.
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q1 i? In performance testing simulating 100 concurrent users generating assignments simultaneously, the platform yielded an average end-to-end response time of 30 seconds per request with no significant resource bottlenecks detected. → Zaki Kurdya 2026
Evidence¶
Zaki Kurdya 2026¶
Zaki Kurdya, Mohammed Zuqlam, Salem Amassi, Shady Telbany, Motaz Saad. (2026). Taklif.AI: LLM-Powered Platform for Interest-Based Personalized College Assignments. https://arxiv.org/abs/2605.05842 doi:10.48550/arxiv.2605.05842
q1 · i? · design · r2
System performance (load) testing of the serverless architecture, including LLM inference via Groq's optimized Llama 3.3 70B endpoint. The scenario "simulating 100 concurrent users" produced the 30-second average response time; the serverless architecture auto-scales and LiteLLM reroutes requests on provider latency. No effect size is printed.
"A scenario simulating 100 concurrent users generating assignments simultaneously yielded an average end-to-end response time of 30 seconds per request"
Discussion¶
Related Claims¶
- The AI feedback system returns responses in under a second for MCQs and about six seconds for open-ended questions, and its reusable prompt is roughly 2.77 times more time-efficient to prepare than equivalent educator text — related
- In preliminary user acceptance testing, 84% of participants rated Taklif.AI's personalization feature as beneficial — related
- Asynchronous concurrent execution bounds overall latency to approximately the slowest individual model's inference time — related
- Human scoring and feedback for HDR takes about 11 minutes 30 seconds per learner, while GPT-based scoring and feedback is nearly instant — related
- System reliability and latency acted as a prerequisite bottleneck: 56.4% of started solves completed, with a 32.0-second average latency and 68.5-second p90 — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
load-test-100-concurrent-users-30-second-response |
title |
Under a simulated load of 100 concurrent users, Taklif.AI averaged 30 seconds end-to-end response time per request |
description |
Under a simulated load of 100 concurrent users, Taklif.AI averaged 30 seconds end-to-end response time per request |
status |
review |
evidence_strength |
weak |
newest_evidence |
2026 |
recheck_by |
2029 |
generated |
by process:wiki-ingest on 2026-10-11 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — zaki-kurdya-2026 |
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