Gemma 2

Run Google's Gemma 2 models efficiently on Clore.ai GPUs

Newer version available! Google released Gemma 3 in March 2025 — the 27B model beats Llama 3.1 405B and adds native multimodal support. Consider upgrading.

Run Google's Gemma 2 models for efficient inference.

All examples can be run on GPU servers rented through CLORE.AI Marketplace.

Renting on CLORE.AI

1. Visit CLORE.AI Marketplace

2. Filter by GPU type, VRAM, and price

3. Choose On-Demand (fixed rate) or Spot (bid price)

4. Configure your order:

   • Select Docker image

   • Set ports (TCP for SSH, HTTP for web UIs)

   • Add environment variables if needed

   • Enter startup command

5. Select payment: CLORE, BTC, or USDT/USDC

6. Create order and wait for deployment

Access Your Server

• Find connection details in My Orders

• Web interfaces: Use the HTTP port URL

• SSH: ssh -p <port> root@<proxy-address>

What is Gemma 2?

Gemma 2 from Google offers:

• Models from 2B to 27B parameters

• Excellent performance per size

• Strong instruction following

• Efficient architecture

Model Variants

Model	Parameters	VRAM	Context
Gemma-2-2B	2B	3GB	8K
Gemma-2-9B	9B	12GB	8K
Gemma-2-27B	27B	32GB	8K

Quick Deploy

Docker Image:

pytorch/pytorch:2.5.1-cuda12.4-cudnn9-runtime

Ports:

22/tcp
8000/http

Command:

pip install vllm && \
vllm serve google/gemma-2-9b-it --port 8000

Accessing Your Service

After deployment, find your http_pub URL in My Orders:

1. Go to My Orders page

2. Click on your order

3. Find the http_pub URL (e.g., abc123.clorecloud.net)

Use https://YOUR_HTTP_PUB_URL instead of localhost in examples below.

Using Ollama

# Run Gemma 2
ollama run gemma2

# Specific sizes
ollama run gemma2:2b
ollama run gemma2:9b
ollama run gemma2:27b

Installation

pip install transformers accelerate torch

Basic Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "google/gemma-2-9b-it"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

messages = [
    {"role": "user", "content": "Explain how neural networks learn."}
]

inputs = tokenizer.apply_chat_template(
    messages,
    return_tensors="pt",
    add_generation_prompt=True
).to("cuda")

outputs = model.generate(
    inputs,
    max_new_tokens=512,
    temperature=0.7,
    do_sample=True
)

response = tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True)
print(response)

Gemma 2 2B (Lightweight)

For edge/mobile deployment:

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "google/gemma-2-2b-it"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto"
)

# Fast inference for simple tasks
messages = [{"role": "user", "content": "Summarize in one sentence: AI is transforming industries."}]

Gemma 2 27B (Best Quality)

from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
import torch

model_id = "google/gemma-2-27b-it"

# Use 4-bit to fit in 24GB VRAM
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.bfloat16
)

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    quantization_config=quantization_config,
    device_map="auto"
)

vLLM Server

vllm serve google/gemma-2-9b-it \
    --port 8000 \
    --dtype bfloat16 \
    --max-model-len 8192

OpenAI-Compatible API

from openai import OpenAI

client = OpenAI(base_url="http://localhost:8000/v1", api_key="x")

response = client.chat.completions.create(
    model="google/gemma-2-9b-it",
    messages=[
        {"role": "user", "content": "Write a haiku about programming"}
    ],
    temperature=0.8
)

print(response.choices[0].message.content)

Streaming

from openai import OpenAI

client = OpenAI(base_url="http://localhost:8000/v1", api_key="x")

stream = client.chat.completions.create(
    model="google/gemma-2-9b-it",
    messages=[{"role": "user", "content": "Tell me a short story"}],
    stream=True
)

for chunk in stream:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="", flush=True)

Gradio Interface

import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "google/gemma-2-9b-it"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id, torch_dtype=torch.bfloat16, device_map="auto"
)

def chat(message, history, temperature):
    messages = []
    for h in history:
        messages.append({"role": "user", "content": h[0]})
        messages.append({"role": "assistant", "content": h[1]})
    messages.append({"role": "user", "content": message})

    inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True).to("cuda")
    outputs = model.generate(inputs, max_new_tokens=512, temperature=temperature, do_sample=True)

    return tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True)

demo = gr.ChatInterface(
    fn=chat,
    additional_inputs=[gr.Slider(0.1, 1.5, value=0.7, label="Temperature")],
    title="Gemma 2 Chat"
)

demo.launch(server_name="0.0.0.0", server_port=7860)

Batch Processing

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "google/gemma-2-9b-it"
tokenizer = AutoTokenizer.from_pretrained(model_id, padding_side="left")
tokenizer.pad_token = tokenizer.eos_token

model = AutoModelForCausalLM.from_pretrained(
    model_id, torch_dtype=torch.bfloat16, device_map="auto"
)

prompts = [
    "Explain gravity in one sentence.",
    "What is photosynthesis?",
    "Define machine learning.",
    "What is the speed of light?"
]

messages_batch = [[{"role": "user", "content": p}] for p in prompts]

inputs = tokenizer.apply_chat_template(
    messages_batch,
    return_tensors="pt",
    padding=True,
    add_generation_prompt=True
).to("cuda")

outputs = model.generate(inputs, max_new_tokens=128, pad_token_id=tokenizer.pad_token_id)

for i, output in enumerate(outputs):
    response = tokenizer.decode(output, skip_special_tokens=True)
    print(f"Q: {prompts[i]}")
    print(f"A: {response.split('<start_of_turn>model')[-1].strip()}\n")

Performance

Model	GPU	Tokens/sec
Gemma-2-2B	RTX 3060	~100
Gemma-2-9B	RTX 3090	~60
Gemma-2-9B	RTX 4090	~85
Gemma-2-27B	A100	~45
Gemma-2-27B (4-bit)	RTX 4090	~30

Comparison

Model	MMLU	Quality	Speed
Gemma-2-9B	71.3%	Great	Fast
Llama-3.1-8B	69.4%	Good	Fast
Mistral-7B	62.5%	Good	Fast

Troubleshooting

CUDA out of memory

for 27B - Use 4-bit quantization with BitsAndBytesConfig - Reduce `max_new_tokens` - Clear GPU cache: `torch.cuda.empty_cache()`

Slow generation

• Use vLLM for production deployment

• Enable Flash Attention

• Try 9B model for faster inference

Output quality issues

• Use instruction-tuned version (-it suffix)

• Adjust temperature (0.7-0.9 recommended)

• Add system prompt for context

Tokenizer warnings

• Update transformers to latest version

• Use padding_side="left" for batch inference

Cost Estimate

Typical CLORE.AI marketplace rates (as of 2024):

GPU	Hourly Rate	Daily Rate	4-Hour Session
RTX 3060	~$0.03	~$0.70	~$0.12
RTX 3090	~$0.06	~$1.50	~$0.25
RTX 4090	~$0.10	~$2.30	~$0.40
A100 40GB	~$0.17	~$4.00	~$0.70
A100 80GB	~$0.25	~$6.00	~$1.00

Prices vary by provider and demand. Check CLORE.AI Marketplace for current rates.

Save money:

• Use Spot market for flexible workloads (often 30-50% cheaper)

• Pay with CLORE tokens

• Compare prices across different providers

Next Steps

• Llama 3.2 - Meta's model

• Qwen2.5 - Alibaba's model

• vLLM Inference - Production serving