Modelleri Banana.dev'de Dağıtmak: 2026 Geçiş Kılavuzu

The Serverless GPU Evolution

The serverless GPU landscape has shifted dramatically between 2024 and 2026. Banana.dev was once a pioneer here. However, its sunsetting marked a major pivot toward robust providers like RunPod and Modal.

This guide serves as your comprehensive 2026 resource. We will address the legacy of Banana.dev and provide a clear migration path. You will learn exactly how to deploy custom models today. We move beyond simple inference to build the “self-driving” ecosystems that we specialize in.

Deploying Models: From Banana.dev to Modern Serverless

If you are searching for “deploying models on Banana.dev,” you likely want one of two things. You might need serverless GPU inference without managing Kubernetes. Or, you are solving the cold start problem that plagues on-demand AI.

You are in the right place, but the tools have changed.

As of March 31, 2024, Banana.dev officially sunset its serverless GPU platform. Documentation from 2023 is no longer functional. However, the philosophy of serverless, scale-to-zero AI has survived. It is now the standard for modern AI architecture.

In 2026, we don’t just deploy models. We architect intelligent agents. At Thinkpeak.ai, we transform raw endpoints into autonomous “Digital Employees.”

This guide is your handbook for the post-Banana era. We cover superior alternatives and provide a technical tutorial on deploying a custom Büyük Dil Modeli (LLM).

Part 1: The Post-Mortem of Banana.dev

What Was Banana.dev?

For developers in 2022-2023, Banana.dev was the “easy button” for machine learning. It introduced Potassium, a Python framework. It made serving a PyTorch model as easy as writing a Flask app. You simply defined `init()` and `handler()` functions. Banana handled the Docker containerization and auto-scaling.

It solved three massive problems:
1. **Idle Costs:** You didn’t pay for a GPU when no one was using it.
2. **DevOps Complexity:** Data scientists didn’t need to learn Kubernetes.
3. **Cold Starts:** It promised faster boot times than standard AWS Lambda with GPU support.

Why Did It Sunset?

Banana.dev shut down due to hardware economics. Maintaining a massive pool of idle GPUs for fast starts requires immense capital. Demand for H100s and A100s surged. The unit economics of cheap serverless inference became unsustainable for smaller providers compared to giants like RunPod.

The Landscape in 2026

Today, the market offers two dominant philosophies for deploying models:

1. **The Container-Native Approach (RunPod):** You build a Docker container. You push it to a registry, and the platform runs it serverlessly. This offers the best price-performance ratio.
2. **The Code-First Approach (Modal):** You write pure Python code. Infrastructure is defined via decorators. There is no Dockerfile to manage. This offers the fastest developer velocity.

We utilize both approaches at Thinkpeak.ai depending on client needs.

Part 2: The New Standards (RunPod vs. Modal)

You need to select your new “home” for deployment before writing code.

1. RunPod Serverless

RunPod Serverless is the closest spiritual successor to Banana.dev. It has significantly more power under the hood. You define a template and deploy it as a serverless endpoint.

* **Cold Starts:** “FlashBoot” technology keeps many cold starts under 200ms.
* **Hardware:** Access range from budget RTX 3090s to H100 NVL clusters.
* **Pricing:** Purely consumption-based per second of GPU time.

2. Modal

Modal abstracts the container entirely. If you loved Potassium for its Pythonic feel, you will love Modal. You define your environment directly in the code.

* **Architecture:** It feels like writing local Python, but execution happens in the cloud.
* **Best For:** Complex pipelines where one model output triggers another.

Feature Comparison Table (2026)

Part 3: Technical Tutorial – Deploying a Custom LLM

We will use RunPod for this example. It follows the “Container to Endpoint” paradigm most familiar to former Banana users.

The Goal

We will deploy **Llama-3-8B-Instruct** as a serverless endpoint.

Step 1: The Handler

In the Banana days, you used Potassium. In RunPod, you use the RunPod SDK. The logic is nearly identical. You load the model once in the global scope. Then, you run inference per request in the handler scope.

Create a file named `handler.py`:

import runpod
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

# 1. Global Loading (The "Init" Phase)
# This runs only once when the container starts
MODEL_NAME = "meta-llama/Meta-Llama-3-8B-Instruct"
device = "cuda" if torch.cuda.is_available() else "cpu"

print(f"Loading model: {MODEL_NAME}...")
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForCausalLM.from_pretrained(
    MODEL_NAME,
    torch_dtype=torch.float16,
    device_map="auto"
)
print("Model loaded successfully.")

# 2. The Handler Function
# This runs for every API request
def handler(job):
    job_input = job["input"]
    
    # Extract prompt from input
    prompt = job_input.get("prompt", "Hello, who are you?")
    max_tokens = job_input.get("max_tokens", 100)
    
    # Prepare input
    messages = [
        {"role": "system", "content": "You are a helpful AI assistant."},
        {"role": "user", "content": prompt},
    ]
    
    input_ids = tokenizer.apply_chat_template(
        messages, 
        add_generation_prompt=True, 
        return_tensors="pt"
    ).to(device)

    # Generate
    terminators = [
        tokenizer.eos_token_id,
        tokenizer.convert_tokens_to_ids("<|eot_id|>")
    ]
    
    outputs = model.generate(
        input_ids,
        max_new_tokens=max_tokens,
        eos_token_id=terminators,
        do_sample=True,
        temperature=0.6,
        top_p=0.9,
    )
    
    # Decode response
    response = outputs[0][input_ids.shape[-1]:]
    decoded_output = tokenizer.decode(response, skip_special_tokens=True)
    
    return {"response": decoded_output}

# 3. Start the Serverless Worker
runpod.serverless.start({"handler": handler})

Step 2: The Dockerfile

RunPod requires a robust container definition. We need to bake the model dependencies into the image.

Create a `Dockerfile`:

# Use a base image with PyTorch and CUDA pre-installed
FROM pytorch/pytorch:2.2.1-cuda12.1-cudnn8-runtime

# Set working directory
WORKDIR /app

# Install system dependencies
RUN apt-get update && apt-get install -y git && rm -rf /var/lib/apt/lists/*

# Install Python dependencies
RUN pip install --no-cache-dir runpod transformers accelerate

# Copy the handler code
COPY handler.py .

# Command to run the handler
CMD [ "python", "-u", "handler.py" ]

Step 3: Optimization

A naive deployment downloads the model weights every time the container starts. This leads to slow cold starts. To fix this, we bake the model into the image.

Add this to your Dockerfile before the `CMD`:

# Create a builder script to download model during build time
RUN python -c "from transformers import AutoModelForCausalLM, AutoTokenizer; 
    AutoTokenizer.from_pretrained('meta-llama/Meta-Llama-3-8B-Instruct'); 
    AutoModelForCausalLM.from_pretrained('meta-llama/Meta-Llama-3-8B-Instruct')"

Step 4: Build and Deploy

1. **Build:** `docker build -t my-username/llama3-runpod .`
2. **Push:** `docker push my-username/llama3-runpod`
3. **Deploy on RunPod Console:**
* Go to **Serverless > New Endpoint**.
* Container Image: `my-username/llama3-runpod`.
* GPU: RTX 3090 or RTX 4090.
* FlashBoot: **Enabled**.

Once deployed, you will get an API endpoint ID.

Part 4: Beyond Deployment – Building the Agent

Deploying the model is only Step 1. A raw API endpoint is not a business solution.

At Thinkpeak.ai, we see businesses fail because they cannot integrate models into reliable workflows.

The “Naked Endpoint” Problem

If you just query your new RunPod endpoint, you have to handle several issues. You must manage retries if the GPU is busy. You need to handle context since LLMs don’t remember previous queries. Finally, you must format the JSON output into a PDF, Slack message, or database row.

The Thinkpeak Solution: Custom AI Agents

We wrap these raw serverless endpoints into Otonom Ajanlar. Here is how we elevate the tutorial above into a production asset:

1. **The Reasoning Layer:** We build a “Supervisor Agent” that decides *when* to call the RunPod endpoint.
2. **Tool Use:** We give the agent access to your internal API. The model generates parameters, and the agent executes the call.
3. **Memory Store:** We attach a vector database so the model retains long-term memory of client interactions.

Do you need a raw model, or do you need a digital employee? Contact our engineering team to build the infrastructure that surrounds your model.

Part 5: Advanced Strategies for 2026

If you manage your own deployments, implement these best practices to stay cost-effective.

1. Flash Attention 3 & Quantization

In 2026, you should use **AWQ** or **GGUF** formats. This allows you to run a 70B parameter model on a consumer GPU instead of an enterprise A100. This results in roughly a 75% reduction in hourly burn rate.

2. Speculative Decoding

For latency-sensitive apps, use speculative decoding. A small draft model predicts the next tokens, and the large model verifies them. This doubles your tokens-per-second without changing quality.

3. Multi-LoRA Serving

Don’t deploy one endpoint per customer. The modern way is to deploy one base model. Then, dynamically load LoRA adapters per request. We use this to inject specific brand voices at runtime, reducing infrastructure costs significantly.

Part 6: Frequently Asked Questions

Is Banana.dev ever coming back?

No. The platform was sunset in March 2024. Do not confuse it with crypto projects using similar names.

What is the cheapest alternative?

**RunPod Serverless** is generally the cost leader. Their community cloud allows you to rent consumer GPUs which are cheaper than the enterprise options on major clouds.

Can I still use the Potassium framework?

Technically yes, but it is unmaintained. We strongly recommend migrating to **FastAPI** or the native **RunPod SDK**.

How does Thinkpeak.ai differ from RunPod?

RunPod is the engine rental. Thinkpeak.ai builds the self-driving car. If you want a cold outreach tool or proposal generator that drives revenue, we build the complete solution.

Sonuç

The era of deploying on Banana.dev has ended. However, the era of accessible, high-performance AI is just beginning. Tools like RunPod and Modal allow us to build systems that were impossible two years ago.

It is no longer about access to GPUs; it is about orchestrating them effectively. Whether you need a simple utility or a bespoke internal tool, we provide the engineering rigor to make it scalable.

Ready to stop debugging Dockerfiles and start automating your business? Check out our solutions at Thinkpeak.ai.

Kaynaklar

* https://www.banana.dev/blog/sunset
* https://www.runpod.io/articles/guides/serverless-for-generative-ai
* https://www.runpod.io/articles/comparison/serverless-gpu-deployment-vs-pods
* https://www.runpod.io/product/serverless/
* https://modal.com