Deploy Qwen / Gemma on Cloud with API endpoints

@tadinhkien

Greetings, Thank you for considering my application for this project. As an AI Engineer and Python Developer with over 8+ years of experience, I bring a wealth of knowledge and expertise in the field of Python, Deep Learning. I have carefully reviewed the project description and am eager to discuss your specific needs and requirements in more detail. My commitment is to provide dedicated support and consistent follow-up throughout the project's lifecycle. Please feel free to reach out to me to further discuss how I can contribute to the success of your project. Looking forward to the opportunity of working together. Best regards, KuroKien

@koolvishruth

My renowned expertise in AI, Data, Automation, Cloud, and Software Development makes me the ideal candidate for your project. Having successfully completed over 126 similar projects within my 7+ years of experience span, I bring you unrivaled know-how that will ensure the production-ready GPU instance on E2E Cloud you seek. My in-depth knowledge includes using CUDA, cuDNN, vLLM and relevant tools to create and deploy systems that achieve substantial throughput, which is crucial for the 200k-500k daily calls you anticipate.

@jinufeb14

With an extensive background in Linux, Machine Learning, Python, and Software Architecture, I believe I am the right candidate who can seamlessly complete your project - deploying Qwen/Gemma on Cloud with API endpoints. As ranked among the Top 1% on Freelancer, I pride myself on delivering efficient and reliable solutions that perfectly align with your needs. I will bring this finesse to bear by provisioning a high-powered E2E Cloud VM for your needs, carefully deploying CUDA, cuDNN, and vLLM while ensuring seamless integration with a lean and well-documented Python server. FastAPI has traditionally been my choice for creating lightweight and scalable REST APIs but I am open to explore alternatives. My commitment to optimized performance and scalability shines through my inclusion of a clean way - be it Terraform, Ansible or concise script - to replicate or scale the setup for future spiky traffics. To ensure production readiness, I will run intricate load tests using Locust or JMeter tooling to guarantee stability at 200k-500k daily calls without any memory leaks or crashing processes. Lastly, not only will I guarantee meeting the acceptance criteria including p95 < 800ms latency for standard image sizes but also provide valuable documentation to aid in scaling and endpoint specifications. My goal is to deliver a service that is not only functional but can immediately integrate into your production environment. Let’s discuss further.

@johnallenvillan1

Hi there, Your E2E Cloud deployment needs a production GPU stack for Qwen vLLM object detection, with image upload, bounding-box output, and stable 24-hour throughput at 200k-500k calls daily. I’ve spent the last 4 years solving exactly this type of problem. I’ve built and stabilized similar GPU inference services that moved from prototype to repeatable deployment, including FastAPI model endpoints with OpenAPI docs and load-tested inference pipelines that held under sustained traffic. The real risk here isn’t just getting the model to run; it’s keeping latency, memory use, and worker concurrency stable once batching and long-running requests start interacting. I’ll provision the VM, install CUDA/cuDNN, wire vLLM and the latest Qwen checkpoints, and build a lean Python API that returns labels and boxes cleanly. I’ll also add a reproducible deploy path with Terraform, Ansible, or bash, then run a 24-hour load test and document the results clearly. Best regards, John allen.

@android2014213

Hi, I can help you to deploy Qwen / Gemma on cloud. I am interested to start this project as soon as possible. Thanks Ashish A.

@Shrutibimpl

Hello, I will deploy and optimize your Qwen vision-language model for high-throughput object detection on an E2E Cloud GPU instance. I will provision the virtual machine and install the required GPU stack, including CUDA, cuDNN, and the vLLM inference engine to run your selected Qwen-VL model checkpoint. I will develop a lightweight, concurrent FastAPI server to receive images via a REST API, process coordinates, and return structured JSON bounding boxes and labels. To achieve your target of two to five requests per second and under eight hundred milliseconds of latency, I will configure vLLM's native dynamic batching and concurrent worker parameters. Additionally, I will provide a reproducible Ansible playbook or shell script for scaling, and use Locust to execute a twenty-four-hour load test to verify stability. I have successfully deployed several high-performance LLM and vision-language model inference pipelines on cloud GPU nodes using vLLM, Docker, and FastAPI. 1) Which specific Qwen model version, such as Qwen2-VL-7B, do you plan to use for your object detection workflow? 2) What GPU card profile, such as an NVIDIA A100 or L4, do you prefer to provision on E2E Cloud? 3) Does your application require storage of the input images on the GPU server, or will they be processed entirely in memory? Thanks, Bharat

@letswork92

I understand that you're seeking a robust deployment of the Qwen vLLM model for object detection, capable of handling significant traffic while ensuring quick response times. The need for a production-ready GPU instance on E2E Cloud, coupled with an efficient REST API, is crucial for your project’s success. With over 12 years of experience in full-stack development and cloud solutions, I can expertly provision a powerful VM, install CUDA and cuDNN, and integrate the latest Qwen checkpoints. My preferred choice would be FastAPI to ensure that we maintain lean performance while also documenting everything thoroughly using OpenAPI/Swagger. To address scalability, I'll provide a clear provisioning script—whether through Terraform or Ansible—to facilitate additional nodes during traffic spikes. Furthermore, I’ll conduct thorough load testing using tools like Locust to ensure we meet your p95 latency requirement under real-world conditions. Could you clarify which specific metrics you'd like to see emphasized in the load-test report?

@Algokage

Hello, I can efficiently deploy the Qwen vLLM model on E2E Cloud with a production-ready GPU instance, ensuring it handles 200k–500k daily calls. I’ll provision a powerful VM, install CUDA, cuDNN, and vLLM, pull Qwen checkpoints, and integrate a FastAPI server for REST endpoints. Batching and concurrent workers will optimize throughput to meet 2–5 requests/second. I’ll include Terraform scripts for scalability and conduct a 24-hour load test to ensure stability and p95 latencies under 800 ms. With 5+ years of experience, I’ll deliver all specified deliverables. Message me for samples or to discuss further. Thanks, Adegoke. M

@iimtpradeepdash

Provisioning Qwen with vLLM on an E2E Cloud GPU instance is mostly a workflow of CUDA/cuDNN alignment and endpoint hardening for throughput. The main risk here is memory and batching configuration—vLLM can run into allocation limits at high concurrency unless monitored and tuned. I’ve deployed similar inference servers for computer vision and LLMs, including an NDA project where we scaled concurrent image inference endpoints using FastAPI, Gunicorn, and process pools to hit sustained 5–10 RPS on commodity GPUs. I’d script the full stack (cloud provision, CUDA, vLLM, FastAPI app, plus Docker or shell scripts for replication). Load testing with Locust. You get API code with OpenAPI docs, p95 latency proof, and a README for scaling/replica spin-up. Is E2E Cloud’s CUDA setup bare, or do you already have base images I should start from? That affects the provisioning script. I can start this week if you want fast turnaround. Pradeep

@Manish502

Hi, I have strong experience deploying GPU-based AI workloads on Linux, containerized environments, and production cloud infrastructure. I can provision and optimize the complete inference stack, configure the GPU environment, deploy the API, automate provisioning, and ensure the service is production-ready, scalable, and easy to maintain. My focus is on building reliable infrastructure with automated deployments, monitoring, load testing, and documentation so the service can handle production traffic with confidence. I'll also provide reproducible deployment scripts and a clear scaling strategy for future growth. Available to start immediately and happy to discuss your performance and infrastructure requirements.

@ammarahmed20

As a data engineer and full-stack developer, I bring a comprehensive skill set to your project. My experience building AI agents, automation systems, and scalable applications make me uniquely qualified for deploying Qwen on the Cloud with API endpoints. Your project requirements align perfectly with my deep knowledge of machine learning and API development in Python, ensuring that I can provision a GPU instance on E2E Cloud that runs the Qwen vLLM model flawlessly. Efficiency and scalability are my core values. I am well-versed in configuring powerful instances, installing frameworks like CUDA and cuDNN, and managing the deployment process from start to finish. With your preference for FastAPI in mind, I can ensure a robust and efficient REST endpoint for your image file processing needs. And incase traffic spikes,I'll provide you with an automated provisioning script – be it through Terraform, Ansible or concise bash commands – so that you can easily scale up the setup without any hassle.

@ssquare489

As a seasoned developer with vast exposure to API Development, FastAPI and Machine Learning (ML), I am confident in my ability to seamlessly deliver your Qwen/Gemma deployment project. Not only have I extensively worked with E2E Cloud VMs, but I also have a deep understanding of CUDA, cuDNN and can set up vLLM parallels. Given my background at Google and Apple, efficiency and quality are two driving forces of my work ensuring an optimized, lean environment which aligns perfectly with the request for an efficient system. I am all for resource optimization be it through model sharding, concurrent workers or batching. In terms of scalability, your project requires a clean way to replicate or scale the setup- which I am capable of delivering using tools like Terraform/Ansible/bash. Moreover, I run extremely thorough tests with commendable p95 latencies usually falling below your 800ms target for standard 256x256 images as per your time limit and budget. Putting myself in the end-users' shoes, I ensure that there are 'Zero critical errors during sustained load test'.

@goranm16

Hi, you’re looking for a GPU inference service that can actually stay up under production traffic, not just a demo endpoint. I’d build this around Qwen/vLLM on an E2E Cloud GPU VM, with a lean FastAPI image endpoint returning boxes/labels and OpenAPI docs. I’d start by locking the model/runtime versions, then automate the VM setup with a repeatable script so extra nodes can be created cleanly. The main thing I’d watch is GPU memory and request queuing under sustained load, so I’d test batching/concurrency early and run Locust against the 256x256 path until the p95 and error rate are clear. Please send the sample image set you want used for correctness checks and the preferred Qwen checkpoint/version. Thanks!

@Geetam00

As a top 3% Freelancer with over 5 years of experience, I'm confident in my ability to successfully deploy the Qwen / Gemma model on E2E Cloud. With a strong background in Linux and extensive proficiency in Python, Software Architecture, Machine Learning, and notably Terraform, I am well-suited to your project needs. I understand that the crux of your project lies in creating a GPU instance on E2E Cloud, compiling it with CUDA, cuDNN, vLLM, and integrating the Qwen checkpoints for object detection. This is where my expertise comes into play. My aim for your infrastructure is to ensure you receive at least 2-5 sustained requests per second by leveraging batching, concurrent workers, or model sharding smartly. Moreover, I have substantial experience in building scalable and production-grade projects combined with proficiency in performance optimization tailored specifically for high-volume environments like yours. I will guarantee p95 latencies under 800ms and 100% stability even during peak traffic hours. My dedication and swift turnaround time (<2-3 hours response rate) means that I'll be there for you every step of the way - from initial ideation to final deployment and scaling. Let's collaborate to bring your vision to life and deliver a cutting-edge product that exceeds your expectations!

@mussaibb7

I'd love to dive into this project and help you build a robust AI workflow for object detection using Qwen/Gemma on E2E Cloud. A production-ready GPU instance requires a reliable AI workflow with clear inputs, outputs, and fallback behavior, not just a prompt wrapped in code. This means designing a modular orchestration system that can handle retrieval quality, memory, and logging effectively. Given my experience with AI Meeting Assistant Backend, I'm confident in delivering a closely related workflow. This project maps well to the delivery risk in this job, as it involved building a speech-to-text and LLM summarization backend with multi-API orchestration and real-time outputs. To execute this project, I'll follow a short plan tied to the actual job. This includes deploying Qwen/Gemma on Cloud with API endpoints, designing a modular workflow code, and handling logging/fallback behavior. I'll also ensure that the workflow is demo-ready and well-documented. Before we begin, could you clarify the deployment target, API boundaries, and whether this is a greenfield or an existing service? What part is already built, and where is the workflow currently breaking down?

@richard031596

Qwen doesn't ship a native object-detection head — it's a language/vision-language model, not a detector like YOLO. Before any deployment work starts, that needs to be nailed down: are you using a Qwen-VL variant for grounding/bounding-box output, or do you need a separate detection model (Gemma fine-tune, or YOLO alongside Qwen for classification) running behind the same endpoint? I've built AWS Lambda inference pipelines with LangChain agents in production, so I'm comfortable with the GPU provisioning → FastAPI → load-test pipeline you've outlined, including Terraform for node scaling and Locust for the 24-hour soak test. For 500k calls/day at p95 <800ms, batching strategy depends entirely on which model architecture we're actually serving — that's the one thing I'd lock down before writing a line of Terraform.

@rakhshanz3

As a seasoned technologist with deep experience in both academia and industry, I have successfully led projects that mirror the requirements of your deployment plan. My extensive background in artificial intelligence, machine learning, and full stack development will allow me to seamlessly handle all aspects of this project. Having worked on projects implementing technologies such as the Qwen vLLM model for object detection before, I am well-acquainted with the intricate relationship between computational intensiveness, GPU provisioning, and API availability. This familiarity will surely streamline the provisioning process and ensure we leverage server resources optimally. Moreover, my proficiency in tools like FastAPI, Linux, and Python will benefit you significantly by ensuring a robust inference pipeline with stable throughput. I am more than comfortable using alternative technologies when feasible to ensure a lightweight approach while prioritizing performance and stability. Finally, aware of your desire to replicate the setup and manage traffic surges effectively, I am proficient in designing automated deployment scripts in Ansible or Terraform or even shell scripts. This approach will equip you with the ability to scale up or down as needed during traffic fluxes-rgyzian of up to half a million requests daily won't cause system crashes or memory leaks.

@mundadiya

Hello, I hope you're having a great day. I've delivered multiple GPU-deployed inference services using vLLM and FastAPI that match this stack. For a recent project I provisioned Ubuntu GPU VMs with CUDA and cuDNN via Terraform, served a vision model with FastAPI and Uvicorn workers, and ran a 24h Locust test to verify stability and memory behavior. I'd provision an E2E Cloud VM, install CUDA/cuDNN, pull Qwen into vLLM, implement batching and optional model sharding, and expose a REST API with OpenAPI docs. I'll script provisioning with Terraform and include a Locust load test and a README. Could we hop on a short call to confirm the image formats, batching targets, and the exact Qwen checkpoint you prefer? Happy to walk through details. Thank you, Alpeshbhai M.

@sagarpanwar0123

Hi, Your project aligns well with my experience in deploying production-ready AI inference services. I've built scalable GPU-based APIs using FastAPI, Docker, CUDA, vLLM, and REST APIs, with a focus on high performance, reliability, and automated deployments. I have 7+ years of experience in Python, backend development, cloud infrastructure, and AI model deployment, and I can deliver a production-ready solution with automated provisioning, monitoring, load testing, and complete documentation. A few questions: • Which GPU configuration are you planning to use on E2E Cloud (L40S, A100, H100, etc.)? • Which Qwen object detection model/version would you like to deploy? • Do you require authentication (API Key/JWT) and rate limiting for the REST API? • Should the deployment include Docker and CI/CD, or is a provisioning script (Terraform/Ansible/Bash) sufficient? I'm available to start immediately and would be happy to discuss the architecture and deployment strategy. Best regards, Sagar