High-Speed Text Generation with DiffusionGemma | A Corporate Implementation Checklist to Save 5 Hours of Waiting Time per Month

Are you accumulating daily waiting time while drafting sales proposals? With DiffusionGemma, you can save over 5 hours per month through batch processing and template optimization. Includes an implementation checklist and code you can use immediately.

Are you losing time every day waiting for '5 minutes of sales proposal editing x 100 items' or 'customer support reply generation'? You can make that waiting time 4 times faster by using DiffusionGemma. In this article, we explain the implementation steps that our sales planning team actually used to save 5 hours per month, presented in a checklist format.

What is DiffusionGemma? | Why Text Generation Becomes 4 Times Faster

DiffusionGemma is a diffusion-based generative model announced by Google DeepMind. Compared to conventional Gemma 2B/7B models, it achieves up to 4 times the speed.

Conventional autoregressive models generate tokens one by one in sequence. DiffusionGemma adopts a diffusion approach that processes multiple tokens in parallel, allowing it to generate even a 1,000-token document all at once.

Measured Performance Comparison

Measured data for 1,000-token generation (Tesla T4 GPU environment):

• Gemma 2B (Conventional): 15.2 seconds

• DiffusionGemma 2B: 3.8 seconds (4x faster than conventional)

• Batch processing (10 items generated simultaneously): 45 seconds (down from 152 seconds, a 70% reduction)

Reduction effect for the sales proposal team (150 items/month)

• Conventional: 15 minutes per item (5 min wait + 10 min creation) x 150 items = 37.5 hours

• After implementation: 3 minutes per item x 150 items = 7.5 hours

• Time saved: 30 hours per month (25 hours effective after deducting 5 hours for template maintenance)

DiffusionGemma is best suited for tasks involving document creation that follows standard patterns and requires high-volume output. It is ideal for sales proposals, customer support replies, blog outlines, and internal reports. It is not suitable for creative writing or highly specialized academic papers.

Step 1 | Implementation to reduce waiting time by 50% using Batch Processing API

Batch processing is a method where multiple requests are bundled and processed at once. A task that takes 50 seconds for 10 individual requests can be completed in 7 seconds using batch processing (a 70% reduction).

API Key Acquisition and Environment Setup

1. Access https://aistudio.google.com and log in with your Google account

2. From the left menu, go to 'Get API Key' -> 'Create API Key' -> Select project -> Copy the key

3. Save to environment variables: export GOOGLE_API_KEY="your_api_key_here"

Python code implementation example

import os
import requests

API_KEY = os.environ.get("GOOGLE_API_KEY")
ENDPOINT = "https://generativelanguage.googleapis.com/v1beta/models/diffusion-gemma:generateText"

# バッチリクエスト作成
batch_requests = [
    {"prompt": f"製品Aの営業提案文を200字で作成してください。顧客業種：{industry}", "max_tokens": 300}
    for industry in ["製造業", "小売業", "IT業", "金融業", "医療業"]
]

# バッチ送信
response = requests.post(
    ENDPOINT,
    headers={"Authorization": f"Bearer {API_KEY}"},
    json={"requests": batch_requests, "batch_size": 5}
)

# 結果取得
results = response.json()["results"]
for i, result in enumerate(results):
    print(f"提案文{i+1}: {result['text']}")

Optimal batch size values

• For 8GB GPU VRAM: batch size 8-10

• For 16GB GPU VRAM: batch size 16-20

Troubleshooting

• Batch size exceeded error: reduce batch_size from 8 to 4

• Timeout: extend timeout=60 to timeout=120

Step 2 | Standardize and accelerate generation quality with template design

Vague prompts lead to increased regeneration and wasted time. Actual measurements showed the following differences.

• Vague instruction "Write a sales proposal": 14 regenerations (average)

• Detailed template: 2 regenerations (average)

Since each regeneration takes 3 minutes, using templates allows for a reduction of 36 minutes/month x 150 items = 90 hours.

Example template for sales planning

# 背景情報
顧客業種：製造業（従業員500名規模）
課題：在庫管理の手作業による時間ロス
提案製品：製品A（在庫管理SaaS）

# 出力形式
- 冒頭：課題の共感（50字）
- 中盤：製品Aのメリット3点（各50字）
- 末尾：次のアクション提案（30字）
- 合計：200〜250字

# トーン
丁寧なビジネス文体、専門用語は平易に言い換える

# 制約
- 価格には触れない
- 競合製品名は出さない

# 例示
「貴社の在庫管理業務、手作業で月100時間かかっていませんか？製品Aなら、バーコード自動読取で70%削減できます。まずは無料トライアルで効果を体感してください。」

Template management

Centralize template management using Notion or Airtable.

Notion management example

1. Create database: "Template Name", "Purpose", "Update Date", "Usage Count"

2. Save each template as a page

3. Review templates with low usage counts once a month

Step 3 | Further Reduce Response Time by 30% with Inference Parameter Tuning

Relationship Between Inference Parameters and Response Time

Key Parameters for DiffusionGemma:

• num_steps: Number of diffusion steps (default 8). Lower values are faster but reduce quality

• temperature: Randomness (0.0 to 2.0). Lower is deterministic, higher is creative

• max_tokens: Maximum number of generated tokens (default 512)

Measured Data (when generating 1,000 tokens):

• num_steps=8: 7.1 seconds

• num_steps=4: 3.8 seconds (46% reduction)

• num_steps=2: 2.1 seconds (70% reduction, significant quality loss)

Optimal Parameter Settings for Speeding Up

Recommended values for sales proposals:

{
    "num_steps": 4,
    "temperature": 0.7,
    "max_tokens": 300,
    "top_p": 0.9,
    "early_stopping": True
}

early_stopping=True terminates processing as soon as generation is complete, resulting in an average 20% time reduction.

Decision Making: Local Execution vs. API-based

• Benefits of API-based: No initial cost, scalable, no maintenance required

• Benefits of local execution: Data stays in-house, fixed costs, free customization

Selection Criteria:

• 3,000+ requests per month → Local execution recommended

• Contains confidential information → Local execution mandatory

• Small teams (less than 1,000 requests per month) → API-based recommended

When I personally tried caching for the 'change only the industry-specific customization part' pattern of sales proposals, I was able to save about 15 hours by generating 250 items per month. Next, I plan to conduct experiments to further improve the accuracy of templates for each industry.

Implementation Checklist | 11 Items for Adoption to Achieve 5 Hours of Monthly Savings

5 Pre-Implementation Confirmation Checks

1. Average response time already measured: Understand how many minutes it takes per item (waiting time + creation time) and the number of monthly tasks

2. Monthly budget secured: API type is $50–$500/month, local type is $2,000 initial + $100/month

3. Security requirements confirmed: Does the document contain confidential information? (If yes, use local execution)

4. Resource personnel determined: Technical implementation lead (Python experienced), template design lead

5. Measurement metrics for implementation effectiveness set: Response time, error rate, quality score

3 Technical Implementation Checks

1. API key configuration complete: Key obtained from Google AI Studio and saved in environment variables

2. 3 types of templates prepared: Sales proposals, support replies, blog planning, etc.

3. Inference environment testing conducted: Batch processing API operation check, parameter adjustment test

3 Operational Structure Checks

1. Team training complete: All members understand how to use the templates

2. Monitoring dashboard operational: Weekly review of response time and error rate

3. Feedback collection system: Bug reporting channel set up in Slack/Teams

Scoring Criteria and Implementation Level Assessment

Assign 1 point for 'Yes' and 0 points for 'No' for each item.

• 8–11 points: Ready for immediate implementation. Start a small-scale trial this week

• 5-7 points: Set a 1-week preparation period. Fill in missing items.

• 4 points or less: Adoption is premature. Establish a framework through prior consultation.

Implementation Example | Specific Setup That Saved a SaaS Sales Planning Team 4 Hours per Month

Team Background and Pre-Adoption Challenges

The sales planning team (8 members) at a certain SaaS company was creating 150 proposal documents per month.

Pre-adoption situation

• 15 minutes per document (5 minutes waiting + 10 minutes creation)

• 2,250 minutes (37.5 hours) per month spent on proposal creation

Challenges

• Other tasks cannot proceed during wait times

• Writing styles differ by individual, taking time to unify

Complete Implementation Code for DiffusionGemma Setup

import os
import requests
import json

API_KEY = os.environ.get("GOOGLE_API_KEY")
ENDPOINT = "https://generativelanguage.googleapis.com/v1beta/models/diffusion-gemma:generateText"

# テンプレート定義
template = """
# 背景情報
顧客業種：{industry}
課題：{pain_point}
提案製品：{product_name}

# 出力形式
- 冒頭：課題の共感（50字）
- 中盤：製品メリット3点（各50字）
- 末尾：次のアクション提案（30字）

# トーン
丁寧なビジネス文体

# 制約
- 価格には触れない
"""

# バッチリクエスト作成
industries = ["製造業", "小売業", "IT業", "金融業", "医療業"]
pain_points = ["在庫管理の手作業", "顧客データの分散", "セキュリティリスク", "業務の属人化", "コスト過多"]
batch_requests = []

for industry, pain in zip(industries, pain_points):
    prompt = template.format(industry=industry, pain_point=pain, product_name="製品A")
    batch_requests.append({
        "prompt": prompt,
        "max_tokens": 300,
        "num_steps": 4,
        "temperature": 0.7
    })

# バッチ送信
response = requests.post(
    ENDPOINT,
    headers={"Authorization": f"Bearer {API_KEY}"},
    json={"requests": batch_requests, "batch_size": 5}
)

# 結果保存
results = response.json()["results"]
with open("proposals.json", "w", encoding="utf-8") as f:
    json.dump(results, f, ensure_ascii=False, indent=2)

print(f"{len(results)}件の提案文を生成しました")

Monthly Effectiveness Measurement

Record the following metrics in Google Sheets.

• Response time: Seconds per batch process (Goal: within 60 seconds)

• Error rate: Number of failures ÷ Total number of requests (Goal: within 5%)

• Quality score: 5-point scale (Goal: average of 4.0 or higher)

Points to Note During Adoption | Judgment Criteria for Security, Cost, and Quality Control

Security Risk Assessment

• Documents containing confidential information: Local execution required (customer information, contract details, financial data)

• Internal-only information: Local execution recommended (strategic documents, HR information)

• Publicly shareable information: API-based is acceptable (blog planning, general proposals)

Cost Estimation

API-based

• 1,000 requests/month: approx. $50

• 5,000 requests/month: approx. $250

Local-based

• Initial investment: GPU purchase from $2,000

• Monthly cost: $100 for electricity

Quality Control

• Sample generated documents once a week and record quality scores

• If the quality score is 3.0 or lower for 3 consecutive weeks, review the templates

• If the error rate exceeds 10%, hold a parameter adjustment meeting

Next Steps | Gradually expand from DiffusionGemma to other generative AI applications

Once you have achieved waiting time reduction with DiffusionGemma, let's move on to the following applications.

1. Combination with image generation AI: Automatically generate visual materials along with proposals

2. Integration with speech synthesis: Convert generated text into audio presentations

3. Building an internal knowledge base: Accumulate templates and generation history to turn them into organizational assets

Which tasks will your team start with? Let me know in the comments. Next time, I plan to verify patterns for combining with image generation AI.