[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-84656-en":3,"doc-seo-84656-105":28,"detail-sidebar-cat-0-en-105":82},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":13,"seo_description":14,"update_tm":26,"read_time":27},84656,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",6,"Technology","Edge Deployable LLM Fine Tuning on a Single GPU for Telecom Network Troubleshooting","Telecom troubleshooting at edge sites demands low-latency model responses and localized adaptation to meet data sovereignty constraints, yet GPU deployment at cell sites is limited by power, cooling, space, and weight, plus low hardware utilization under RAN traffic. Architectural gaps between ASIC-style deterministic processing and GPU-based AI further hinder deployment. This paper profiles LLM fine-tuning on a single edge-class GPU using Unsloth, evaluating sequence length, LoRA rank, KV-cache usage, compilation stability, and activation memory overhead. It also studies reasoning vs non-reasoning architectures on a telecom Q&A dataset with retrieved context, providing configuration guidelines for efficient, stable, resource-aware edge training.","Edge-Deployable LLM Fine-Tuning on a Single GPU for Telecom Network Troubleshooting  \nChenhua Shi* , Bhavika Jalli* , John Zou* , Gregor Macdonald, Wanlu Lei, Mridul Jain, Joji Philip  \nEricsson  \n*Equal contribution  \narXiv :2607 .02523v1 [ cs .DC] 6 May 2026  \nAbstract—Telecom troubleshooting at edge sites requires lowlatency model responses that cannot tolerate cloud round-trip delays, while data sovereignty requirements necessitate localized model adaptation; however, deploying GPUs at cell sites is fundamentally constrained by power, cooling, space, and weight limitations, and further challenged by RAN traffic patterns that lead to low hardware utilization and poor return on investment, as well as architectural mismatches between ASIC-optimized deterministic processing and GPU-based AI workloads. Consequently, single-GPU fine-tuning emerges as a critical enabler for practical AI deployment at the edge rather than merely aresource constraint. In this paper, we present a GPU profiling study of LLM fine-tuning under the Unsloth framework, systematically characterizing the impact of maximum sequence length, GPU memory utilization, Low-Rank Adaptation (LoRA) rank, and number of generations on a single edge-class accelerator. We analyze trade-offs between parameter configurations and KV cache usage, examine the effect of inductor compilation on runtime stability, and quantify activation memory overhead during training. Furthermore, we demonstrate that model characteristics—specifically reasoning versus non-reasoning architectures—significantly influence both supervised fine-tuning (SFT) and reinforcement fine-tuning (RFT) due to differences in chat template requirements, including reasoning tags and control flags. Experiments are conducted on a telecom troubleshooting dataset comprising question–answer pairs augmented with top-3 retrieved context documents. The results provide practical configuration guidelines for enabling efficient, stable, and resourceaware LLM fine-tuning in telecom edge environments.  \nIndex Terms—Edge AI, O-RAN, MEC, near-RT RIC, LLMs, RFT, Unsloth, LoRA, GPU profiling, Telecom Dataset  \nI. INTRODUCTION  \nTelecom operators increasingly deploy AI-assisted troubleshooting at edge sites, including regional Network Operations Centers (NOCs), O-RAN near-Real-Time RAN Intelligent Controller (near-RT RIC) nodes [1], and ETSI Multiaccess Edge Computing (MEC) platforms [2], where compute is deliberately constrained to a single GPU accelerator. At these sites, large language models (LLMs) must be adapted to operator-specific fault patterns, alarm vocabularies, and network topologies. However, offloading fine-tuning to centralized cloud introduces round-trip latencies incompatible with real-time network troubleshooting, and transmitting sensitive operator data (subscriber metadata, performance counters, alarm logs) to remote infrastructure violates data sovereignty requirements imposed by regulations such as GDPR. SingleGPU fine-tuning at the edge is therefore not merely a resource constraint but a deployment requirement. The NVIDIA RTXA6000, representative of accelerators available in edge-class  \nservers and regional NOCs [3], provides sufficient capacity for parameter-efficient adaptation of 7–8B parameter models using Low-Rank Adaptation (LoRA) [4] and quantization, yet the operational limits of such hardware for LLM fine-tuning remain poorly characterized.  \nThis work addresses that gap by characterizing LLM finetuning using the Unsloth framework [5] on a single edgeclass GPU. Our study pursues two objectives: (1) GPU memory profiling to establish safe operating envelopes for edge-deployed accelerators, and (2) identifying behavioral differences between reasoning and non-reasoning models that affect edge deployment choices. We fine-tune the Qwen family of models on a telecom troubleshooting dataset comprising question-answer pairs with top-3 retrieved document chunks, profiling memory consumption, batch size li","cbCaihiGfGeuXFiu","https://ap.wps.com/l/cbCaihiGfGeuXFiu","pdf",228482,1,"English","en",105,"# Introduction\n# Related Work","[{\"question\":\"What dataset and training setup are used for the telecom troubleshooting experiments?\",\"answer\":\"Experiments use a telecom troubleshooting dataset of question–answer pairs augmented with top-3 retrieved context documents. The workflow applies SFT followed by RFT and compares outcomes under edge-representative single-GPU settings.\"}]",1784197512,15,{"code":4,"msg":29,"data":30},"ok",{"site_id":23,"language":22,"slug":31,"title":13,"keywords":32,"description":14,"schema_data":33,"social_meta":77,"head_meta":79,"extra_data":81,"updated_unix":26},"edge-deployable-llm-fine-tuning-on-a-single-gpu-for-telecom-network-troubleshooting","",{"@graph":34,"@context":76},[35,52,67],{"@type":36,"itemListElement":37},"BreadcrumbList",[38,42,46,49],{"item":39,"name":40,"@type":41,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":43,"name":44,"@type":41,"position":45},"https://docshare.wps.com/document/","Document",2,{"item":47,"name":12,"@type":41,"position":48},"https://docshare.wps.com/document/technology/",3,{"item":50,"name":13,"@type":41,"position":51},"https://docshare.wps.com/document/edge-deployable-llm-fine-tuning-on-a-single-gpu-for-telecom-network-troubleshooting/84656/",4,{"url":50,"name":13,"@type":53,"author":54,"headline":13,"publisher":56,"fileFormat":59,"inLanguage":22,"description":14,"dateModified":60,"datePublished":61,"encodingFormat":59,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":55},"Person",{"url":39,"name":57,"@type":58},"DocShare","Organization","application/pdf","2026-07-17","2026-07-16",true,{"@type":64,"interactionType":65,"userInteractionCount":20},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70],{"name":71,"@type":72,"acceptedAnswer":73},"What dataset and training setup are used for the telecom troubleshooting experiments?","Question",{"text":74,"@type":75},"Experiments use a telecom troubleshooting dataset of question–answer pairs augmented with top-3 retrieved context documents. The workflow applies SFT followed by RFT and compares outcomes under edge-representative single-GPU settings.","Answer","https://schema.org",{"og:url":50,"og:type":78,"og:title":13,"og:site_name":57,"og:description":14},"article",{"robots":80,"canonical":50},"index,follow",{"doc_id":7,"site_id":23},{"code":4,"msg":5,"data":83},[84,88,92,96,101,104,109,114,119,122,126],{"id":20,"doc_module":4,"doc_module_name":44,"category_name":85,"show_sort_weight":86,"slug":87},"Story & Novel",90,"story-novel",{"id":45,"doc_module":4,"doc_module_name":44,"category_name":89,"show_sort_weight":90,"slug":91},"Literature",80,"literature",{"id":51,"doc_module":4,"doc_module_name":44,"category_name":93,"show_sort_weight":94,"slug":95},"Exam",70,"exam",{"id":97,"doc_module":4,"doc_module_name":44,"category_name":98,"show_sort_weight":99,"slug":100},5,"Comic",60,"comic",{"id":11,"doc_module":4,"doc_module_name":44,"category_name":12,"show_sort_weight":102,"slug":103},50,"technology",{"id":105,"doc_module":4,"doc_module_name":44,"category_name":106,"show_sort_weight":107,"slug":108},7,"Healthcare",40,"healthcare",{"id":110,"doc_module":4,"doc_module_name":44,"category_name":111,"show_sort_weight":112,"slug":113},8,"Research & Report",30,"research-report",{"id":115,"doc_module":4,"doc_module_name":44,"category_name":116,"show_sort_weight":117,"slug":118},9,"Religion & Spirituality",20,"religion-spirituality",{"id":117,"doc_module":4,"doc_module_name":44,"category_name":120,"show_sort_weight":117,"slug":121},"World Cup","world-cup",{"id":123,"doc_module":4,"doc_module_name":44,"category_name":124,"show_sort_weight":123,"slug":125},10,"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":44,"category_name":128,"show_sort_weight":97,"slug":129},19,"General","general"]