Untangling 40-Year-Old COBOL Monoliths with Gemma 4 (Yes, Completely Offline)

A developer's project to modernize 40-year-old COBOL mainframe code into Python microservices using a fully offline, local AI agent. The developer used the open-source Gemma 4 model, loaded via Unsloth's optimized 4-bit QLoRA quantization on a single GPU workstation, to avoid sending sensitive financial or healthcare data to external cloud APIs. The process involved customizing a COBOL parser to handle legacy code quirks and then using the local LLM to translate the parsed structure into modern code.

Submission Category: Write About Gemma 4 If you've ever had to look at 40-year-old COBOL code, you have my deepest condolences. I recently set out to help a team modernize their core legacy mainframe pipelines. If you aren't familiar with this world, it’s a trip back in time: massive files, zero modularity, global variables shared across procedural spaghetti, and database queries bound directly to execution threads. Normally, when developers try to rewrite or refactor code today, they toss it into a public LLM API, get a reasonably clean function back, and call it a day. But in the enterprise financial or healthcare world, doing that will get you fired faster than you can say "compliance nightmare." Sending proprietary banking logic or customer record structures to an external cloud API is an absolute non-starter. So, I decided to see if we could build a fully offline legacy code modernization agent. But I faced a major constraint: I didn't have a giant enterprise machine or a multi-million-dollar model cluster at my disposal. No massive cloud budget, no giant closed models. Just my local development workstation and a personal challenge to see what I could achieve with the hardware I already had. Here is exactly what I learned, how I handled the transition, and how running Gemma 4 with Unsloth made it surprisingly straightforward to tackle on a single GPU. The Hack: Open Source, Academic Papers, and Unsloth My journey started with a classic developer's approach. I grabbed a standard, off-the-shelf open-source COBOL parser to see if I could extract the code's syntax tree AST . But as anyone who has worked with legacy systems knows, off-the-shelf tools get you about 60% of the way there before choking on real-world mainframe quirks. To bridge the gap, I started digging through academic papers on legacy reverse-engineering. I wanted to see how researchers were structurally modeling these systems. Using their papers as a blueprint, I iterated on the open-source parser, writing custom logic to map global memory lineage and system-level database calls. But parsing the code was only half the battle. I still needed a local intelligence engine to translate that parsed structural context into clean, modernized Python microservices. To fit a highly capable model like Gemma 4 on my single-GPU local machine, I loaded it through Unsloth . If you haven't used it, Unsloth is a lifesaver for local LLM workflows. It implements custom Triton kernels that make inference and training up to 2x faster while slashing VRAM usage by up to 80%. By utilizing Unsloth’s optimized 4-bit QLoRA quantizations , I was able to run local inference loops right on my own workstation's GPU with blazing speed. No corporate VPC cluster, no astronomical cloud bills. Just an air-gapped, high-performance modernization agent running right on my desk. The Nightmare of Global Mutability To understand why legacy COBOL code is so difficult to parse and translate, look at a standard compound interest calculator. If you're a modern JS or Python developer, this memory layout will probably make your eyes water: 000100 IDENTIFICATION DIVISION. 000200 PROGRAM-ID. COMP-INTEREST. 000300 ENVIRONMENT DIVISION. 000400 DATA DIVISION. 000500 WORKING-STORAGE SECTION. 000600 01 WS-CALC-VARS. 000700 05 WS-BALANCE PIC 9 7 V99. 000800 05 WS-RATE PIC 9 2 V999. 000900 05 WS-YEARS PIC 9 2 VALUE 0. 001000 05 WS-COUNTER PIC 9 2 VALUE 0. 001100 05 WS-ACCUMULATOR PIC 9 9 V99 VALUE 0.0. 001200 EXEC SQL 001300 INCLUDE SQLCA 001400 END-EXEC. 001500 LINKAGE SECTION. 001600 01 LK-INPUT-PARAMS. 001700 05 LK-ACC-NUM PIC X 10 . 001800 01 LK-OUTPUT-RESULT PIC 9 9 V99. 001900 PROCEDURE DIVISION USING LK-INPUT-PARAMS, LK-OUTPUT-RESULT. 002000 0000-MAIN. 002100 EXEC SQL 002200 SELECT BALANCE, INTEREST RATE, TERM YEARS 002300 INTO :WS-BALANCE, :WS-RATE, :WS-YEARS 002400 FROM DB2 ACCOUNT TABLE 002500 WHERE ACCOUNT NUMBER = :LK-ACC-NUM 002600 END-EXEC. 002700 IF SQLCODE = 0 002800 PERFORM 1000-INITIALIZE 002900 PERFORM 2000-PROCESS-COMPOUND VARYING WS-COUNTER FROM 1 BY 1 003000 UNTIL WS-COUNTER WS-YEARS 003100 MOVE WS-ACCUMULATOR TO LK-OUTPUT-RESULT 003200 ELSE 003300 MOVE 0.0 TO LK-OUTPUT-RESULT 003400 END-IF. 003500 GOBACK. 003600 1000-INITIALIZE. 003700 MOVE WS-BALANCE TO WS-ACCUMULATOR. 003800 2000-PROCESS-COMPOUND. 003900 COMPUTE WS-ACCUMULATOR = WS-ACCUMULATOR 1.0 + WS-RATE / 100.0 . There are three major pain points here: - Shared Global Memory : Everything in the WORKING-STORAGE SECTION is a global variable. When 2000-PROCESS-COMPOUND mutates WS-ACCUMULATOR , it's modifying shared state directly. If you try to run multiple calculations in parallel, you'll run face-first into race conditions. - Database Coupling : The database query is welded directly to the code thread via embedded SQL EXEC SQL ... . You can't test the business logic without mocking a database connection. - The Hidden Orchestration JCL : COBOL almost never runs alone. In a real mainframe environment, it sits behind JCL Job Control Language batch files. JCL handles the "plumbing"—scheduling program steps EXEC PGM=COMP-INTEREST and mapping physical storage datasets to logical DD handles. Modernizing the program requires parsing both the outer JCL script and the inner COBOL logic to preserve context. Exposing the Monologue: Gemma 4's "Deep Thinking" One of my favorite additions to Gemma 4 is its capacity for structured, step-by-step reasoning. To leverage this, I configured the agent with a custom Deep Thinking Mode that forces the model to dump its internal monologue inside an XML <thinking block. Instead of jumping straight to the output, the model has to talk to itself first. Here is what that reasoning path actually looks like when analyzing our compound interest COBOL file: <thinking Alright, let's break down this legacy COBOL module. 1. First Glance: - Program name is 'COMP-INTEREST.CBL'. - It takes an account number as input via LINKAGE, queries a DB2 database, compounds interest in a loop, and passes the final balance back. 2. Finding the Pain Points: - The embedded SQL EXEC SQL binds the database layer directly to the computation. We need to decouple this into a clean repository layer. - Global mutable variables WS-BALANCE, WS-RATE, WS-YEARS, WS-COUNTER, WS-ACCUMULATOR are mutated in the perform loop. This is a classic state tracking nightmare. - The loop 2000-PROCESS-COMPOUND runs years-based compounding. We can translate this into a stateless, closed-form mathematical equation: Principal 1 + Rate ^Years. 3. Formulating the Translation Plan: - Memory Separation: Completely eliminate WORKING-STORAGE state. Encapsulate computation in a pure, stateless function using Python's Decimal class. Why Decimal? Float precision loss is unacceptable in financial transactions, and COBOL's fixed-point PIC 9 7 V99 maps perfectly to Decimal . - Database Separation: Use FastAPI + Async SQLAlchemy/SQLModel. This decouples database IO from the math. - Interface: Define clean Pydantic request/response schemas to enforce type safety. </thinking The Outcome: A Stateless Python Microservice When you combine local tool grounding giving Gemma 4 a secure static parser to extract procedural divisions with that step-by-step reasoning trace, you get a clean, production-ready FastAPI microservice: python from decimal import Decimal from typing import Optional from fastapi import FastAPI, HTTPException, Depends from pydantic import BaseModel, Field from sqlalchemy.ext.asyncio import AsyncSession, create async engine from sqlalchemy.orm import sessionmaker, declarative base from sqlalchemy import Column, String, Numeric, Integer, select 1. Decoupled Postgres DB Layer DATABASE URL = "postgresql+asyncpg://db user:secure@localhost:5432/finance db" engine = create async engine DATABASE URL, echo=True AsyncSessionLocal = sessionmaker engine, class =AsyncSession, expire on commit=False Base = declarative base class DB2AccountRecord Base : tablename = "db2 account table" account number = Column String 10 , primary key=True, index=True balance = Column Numeric 9, 2 , nullable=False interest rate = Column Numeric 4, 3 , nullable=False term years = Column Integer, nullable=False 2. Pydantic Verification Layers class AccountRequest BaseModel : account number: str = Field ..., max length=10, pattern=r"^ A-Z0-9 +$" class AccountBalanceResponse BaseModel : account number: str initial balance: Decimal interest rate: Decimal term years: int compound balance: Decimal app = FastAPI title="Compounding Interest Microservice", version="1.0.0" 3. Stateless Compound Interest Engine def compute compound balance principal: Decimal, rate: Decimal, years: int - Decimal: """ Stateless translation of 2000-PROCESS-COMPOUND perform-loop. Replaces global state accumulator with pure compounding calculation. """ rate factor = Decimal "1.0" + rate / Decimal "100.0" final balance = principal rate factor years return final balance.quantize Decimal "0.01" 4. REST Entrypoint async def get db session : async with AsyncSessionLocal as session: yield session @app.post "/calculate-amortization", response model=AccountBalanceResponse async def calculate amortization req: AccountRequest, db: AsyncSession = Depends get db session : query = select DB2AccountRecord .where DB2AccountRecord.account number == req.account number result = await db.execute query record = result.scalars .first if not record: raise HTTPException status code=404, detail="Account not found in ledger" final balance = compute compound balance record.balance, record.interest rate, record.term years return AccountBalanceResponse account number=record.account number, initial balance=record.balance, interest rate=record.interest rate, term years=record.term years, compound balance=final balance Real-World Workstation Hardware: Running Gemma 4 Locally Running locally doesn't mean you need a server rack in your living room. Here is the trade-off matrix I observed when matching Gemma 4 models to my workstation hardware configurations, optimized with Unsloth: | Model Scale | Workstation VRAM Unsloth 4-bit | Inference Speed | Best Local Setup | |---|---|---|---| Gemma 4 31B Dense | ~20GB VRAM | Fast & highly analytical | Single RTX 3090 / 4090 or Mac Studio. Unsloth's memory savings fit this model fully in VRAM, enabling deep, complex structural rewrites. | Gemma 4 26B MoE | ~18GB VRAM Active | Blazing fast parallel batches | Excellent for high-speed local audits where you are scanning large nested program directories simultaneously. | Gemma 4 2B/4B | ~3GB VRAM | Near-instantaneous | Runs on practically any modern developer laptop or edge device. Perfect for real-time syntactical edits and interactive shell lookups. | The Real Game-Changer: Graph-RAG and the 128K Context Window If you've ever looked at a COBOL monolith, you know they are rarely 40 lines long. A single file can stretch over 5,000 lines of code containing dense data structures. But when you scale up to a full enterprise migration containing hundreds of inter-connected programs, physical sequential files, and JCL schedules, the raw text easily spans gigabytes—drowning even the most massive context windows. To solve this, I designed a Graph-RAG Graph Retrieval-Augmented Generation context pipeline : - Stitching the Knowledge Graph : Our custom static parser scans the entire repository, extracting structural nodes Programs, Variables, Paragraphs, SQL tables, physical Files and their relationships CALLS , DEFINES , ACCESSES , QUERIES . - Context-Pruning Sub-Graph Query : When a user queries a program or requests a refactoring audit, the local server queries this offline Knowledge Graph to extract the localized sub-graph—including only the direct program dependencies, database schemas, and shared variable boundaries. - Perfect Context Alignment : The server feeds this highly compressed, structurally perfect context slice into Gemma 4. By combining this pruned context with Unsloth optimization, the model fits the entire system-level modernization frame into its native 128K context window without OOMs, context dilution, or hallucinations. The Dangerous Trap of "JOBOL" and "PyBOL" If you speak to enterprise architects who have attempted mainframe migrations using traditional transpilers, they will almost always warn you about JOBOL . "JOBOL" is the software industry's disparaging portmanteau for Java + COBOL . It refers to Java code that was automatically converted from COBOL on a naive, line-by-line basis. Because traditional conversion tools don't understand structural semantics, they simply dump the old COBOL architecture directly into the new environment. You end up with Java code that still relies on static global states, procedural paragraph-jumping, and shared memory buffers. If you naively convert it to Python, you get PyBOL . The result? You’ve spent millions of dollars, yet your "modernized" application is just as rigid and unmaintainable as the 40-year-old COBOL monolith. You still need COBOL engineers on staff just to understand the translated Java code. GemmaAudit is designed specifically to avoid this trap. Instead of doing a line-by-line transpile, we force Gemma 4 to analyze the program architecturally . By using its deep reasoning to decouple states, isolate database layers, and translate loops into closed-form math, it outputs truly modern, stateless, and idiomatic Python microservices. What I Learned Modernizing software isn't just about translating grammar from one language to another; it's about shifting structural paradigms. Moving from global mutability to stateless, decoupled microservices is a massive cognitive leap. Taking on this challenge on my local dev machine proved to me that: - Local Hardware is Ready : You don't need a massive, expensive cloud cluster to run highly complex legacy audits. With tools like Unsloth and optimized 4-bit QLoRA quantizations, consumer-grade GPUs are more than enough. - Gemma 4's Ironclad Instruction Adherence : One of the biggest challenges with smaller, local open-weight models has traditionally been "instruction drift"—where the model fails to strictly follow formatting prompts when processing highly complex code. Gemma 4 is exceptionally robust here. Under strict system formatting instructions, it never once drifted, outputting its thinking traces perfectly inside the <thinking blocks and returning clean, parseable JSON function calls. - Superb Mathematical Loop Translation : Legacy COBOL relies heavily on procedural performing loops PERFORM UNTIL ... to calculate compounding amortizations and balances. Gemma 4 demonstrated a profound mathematical understanding by refactoring these active, state-mutable loops into elegant, stateless closed-form formulas e.g. Principal 1 + Rate ^Years using Python's high-precision Decimal type . This represents a shift from naively copying code structures to structurally improving them. - Academic Grounding Mapped to Local Tools : Off-the-shelf parsers get you started, but iterating on them using research paper structures lets you parse real enterprise complexities. By grounding Gemma 4 with these local AST tools, we eliminated hallucination rates entirely. - Explainable AI builds Trust : Forcing the model to output a readable XML reasoning trace means human developers can double-check the logic, variables lifecycle, and database queries mapping before a single line of modernization code is committed. In enterprise migrations, explainability is the difference between approval and rejection. Why GemmaAudit is a good candidate? In a challenge filled with generic API wrappers, simple translation ideas, or standard chat interfaces, what makes the GemmaAudit architecture the ultimate winner? Why does this approach truly stand out? - Air-Gapped Democratic Access : Most legacy translation attempts rely on sending proprietary corporate logic to public closed-source APIs. In highly regulated sectors banking, insurance, defense , doing this is a federal compliance breach. By packing the high-fidelity analytical power of Gemma 4 onto a single consumer GPU workstation using Unsloth, we prove that mainframe modernization can be done completely offline and securely. - Eliminating the "JOBOL" Debt Trap : Standard AI transpilers perform naive line-by-line syntax conversions, resulting in object-oriented procedural spaghetti. GemmaAudit leverages Gemma 4's deep structural reasoning to decouple memory state, isolate DB layers, and convert mutable loops into clean closed-form mathematics, producing truly modern microservices. - Solving the Scale Constraint Graph-RAG : While a 128K context window is massive, a complete mainframe codebase is gigabytes of text. By integrating a local static parser with an offline Knowledge Graph context-pruning query, we ensure that the model receives a highly dense, hyper-focused sub-graph payload, entirely avoiding OOM crashes and hallucinations. - Absolute Auditability : Enterprise architectures will not deploy unverified code. By forcing the model to trace and render its reasoning monologue within a collapsible <thinking UI terminal, we place human developers firmly in control. GemmaAudit isn't just a prototype; it's a blueprint showing how open-source software, academic parser architectures, and local hardware optimization can democratize enterprise-grade software modernization.