How I Use a Local LLM to Extend Claude’s Context (Without Hitting Limits)

The Problem: I Cancelled ChatGPT, Now I’m Stuck on Free Tiers

I run five blogs. Each has its own content strategy, SEO approach, and ongoing projects. For the longest time, ChatGPT Plus handled the heavy lifting while I used Claude for specific tasks.

Then GPT-5 dropped and disappointed the hell out of me. I cancelled my subscription. I wasn’t paying $20/month for regression disguised as an upgrade.

So I shifted to Claude’s free tier. And that’s when the real problems started.

Claude’s free tier is generous—until you actually use it for work. Every new chat starts from zero. I have to explain the same context: “I have five blogs. MomentumPath is productivity. EngineeredAI is AI content. QAJ is QA engineering…” Then I explain what we worked on yesterday. Then I explain the current problem.

By the time we get to actual work, I’ve burned through hundreds of tokens just recreating context.

And if the conversation runs long? Claude has to scan the entire history before each response. Token costs scale. Response times slow. Eventually, I hit the free tier rate limit and have to wait hours to continue.

The worst part? Claude is good at what it does. After testing Claude 4.5 against Claude 4, I know the quality is there. The problem isn’t the model, it’s the architecture combined with free tier limits.

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Why “Just Pay for Claude Pro” Doesn’t Solve It

Sure, I could subscribe to Claude Pro. But after getting burned by ChatGPT’s GPT-5 “upgrade,” I’m not eager to lock into another $20/month subscription.

Plus, even Claude Pro has issues:

1. Memory still resets – Same context re-explanation problem
2. Still has rate limits – Just higher ones
3. Subscription fatigue – Already paying for too many tools
4. No guarantee of consistency – Models change, quality can regress (looking at you, GPT-5)

I needed something that:

• Maintains full conversation history indefinitely
• Doesn’t reset between sessions
• Works offline without rate limits
• Complements cloud AI instead of requiring another subscription

Enter: The Local LLM as a Context Engine

Here’s the insight that changed my workflow: I don’t need a local LLM to replace Claude. I need it to extend Claude.

Think of it like this:

Claude/ChatGPT = High-performance sports car (fast, powerful, expensive to run)
Local LLM = Reliable truck (slower, handles the heavy lifting, always available)

I don’t drive the truck to a race. I don’t use the sports car to haul equipment.

My Hybrid Workflow

Local LLM handles:

• Storing conversation history across sessions
• Context summaries and project notes
• Repetitive tasks (formatting, basic rewrites, tag generation)
• First drafts and outlines
• Bulk processing (categorizing 55 blog posts at 2 AM)
• Always-on queries without burning API credits

Claude/ChatGPT handles:

• Complex strategic thinking
• High-quality content creation
• Creative work (hooks, unique angles)
• Final polish on important content
• Speed-critical decisions

Result: Claude stays fresh. No context bloat. No memory resets killing my productivity. Local LLM maintains the thread.

My Setup (And Whether Yours Will Work)

Before you think “I need a $3,000 GPU,” let me share my specs:

My Hardware:

• Intel i7-8700 (6 cores, 12 threads)
• NVIDIA GTX 1660 (6GB VRAM)
• 16GB RAM
• Standard NVMe SSD

This is a mid-range work/gaming PC from 2018. Not a server. Not a workstation. Just… a computer.

What I Can Actually Run on This

With 6GB VRAM and 16GB RAM, I’m limited to quantized 7-8B parameter models:

• Llama 3 8B (quantized) – My go-to general-purpose model
• Mistral 7B – Fast, great for coding and technical tasks
• Phi-3 Mini – Microsoft’s lightweight model, surprisingly capable

Performance I Get:

• Response time: 30-60 seconds (vs. Claude’s 2-5 seconds)
• Quality: Good enough for drafts, context management, basic tasks
• Cost: Free after initial setup

Does this replace Claude? No. A 7B model isn’t competing with Claude Sonnet.

Does this make me more productive? Absolutely.

Setting Up My Local LLM (The Practical Way)

I tested three approaches: Ollama, LM Studio, and Docker-based solutions. If you want the detailed comparison, I already covered Ollama vs GPT4All vs other local LLMs here. For this post, I’ll focus on what actually worked for me—someone who just wants to get shit done.

Why I Chose Ollama

Why Ollama worked for me:

• One-command install
• Simple CLI and API
• Automatic model management
• Works on Windows, Mac, Linux

My Installation Process:

# Downloaded from ollama.ai
curl -fsSL https://ollama.ai/install.sh | sh

# Pulled my first model
ollama pull llama3:8b

# Ran it
ollama run llama3:8b

That’s it. I had a local LLM running in under 5 minutes.

How I Use It Daily:

# I start the server (runs in background)
ollama serve

# Access via API for automation
curl http://localhost:11434/api/generate -d '{
  "model": "llama3:8b",
  "prompt": "Summarize the key points from our conversation about SEO categories"
}'

Other Options I Tested

LM Studio (Good for GUI Users): I tested this first because I wanted a visual interface:

1. Downloaded from lmstudio.ai
2. Browsed models in the UI
3. Downloaded with one click
4. Started chatting immediately

Great for experimenting, but I switched to Ollama for automation.

Text Generation WebUI (For Power Users): I tried this for advanced features:

git clone https://github.com/oobabooga/text-generation-webui
cd text-generation-webui
./start_linux.sh

Most features and customization, but overkill for my needs. Stuck with Ollama.

How I Integrated Local LLM Into My Workflow

Here’s how I actually use this in practice:

1. Context Management

My Problem: I’m working on multiple blog projects across different days and weeks.

What I Used to Do:

• Day 1: Explain project to Claude
• Day 2: Re-explain everything, continue
• Day 3: Re-explain again, hit token limits

What I Do Now:

# Day 1: I dump context to local LLM
ollama run llama3:8b
> "Store this context: I'm working on a content calendar for Q4. 
Focus areas: SEO optimization, long-form guides, product comparisons..."

# Day 2: I retrieve context
> "What am I working on this week?"
< "You're developing Q4 content calendar with focus on SEO..."

# Then I use that context with Claude for complex work

My local LLM = My external memory bank.

2. Bulk Content Processing

My Problem: I need to generate 20 article outlines for the month.

What I Used to Do with Claude:

• Generate 5 outlines → Claude works well
• Generate 5 more → Claude re-reads everything
• Hit rate limit at outline 15
• Start new session, lose context

What I Do Now:

# I process all outlines locally first
import requests

topics = [
    "How to optimize remote work setup",
    "Best productivity tools for 2025",
    "Time management strategies",
    # ... 17 more topics
]

for topic in topics:
    response = requests.post('http://localhost:11434/api/generate', 
        json={
            'model': 'llama3:8b',
            'prompt': f'Create a detailed outline for: {topic}'
        })
    print(response.json())

No rate limits. No token costs. Just runs.

Then I take the best outlines to Claude for refinement and final polish.

3. Draft Generation & Editing Scripts

My Problem: I need first drafts for multiple posts each week.

What I Used to Do:

• Ask Claude for draft
• Refine with Claude
• Repeat 5 times
• Burn through daily limits

What I Do Now:

# My editorial workflow script
def content_workflow(topic, target_length=1500):
    # Step 1: Local LLM generates rough draft
    draft = local_llm_generate(topic, target_length)
    
    # Step 2: Local LLM does first edit pass
    edited = local_llm_edit(draft, "improve clarity and flow")
    
    # Step 3: Local LLM formats and structures
    structured = local_llm_format(edited, "markdown with headers")
    
    # Step 4: Save intermediate result
    save_draft(structured)
    
    # Step 5: Send best version to Claude for final polish
    final = claude_polish(structured)
    
    return final

# Run for multiple topics
topics = ["topic1", "topic2", "topic3"]
for topic in topics:
    content_workflow(topic)

My local LLM does 80% of the work. Claude handles the final 20% that matters most.

4. Always-On Content Assistant

My Problem: Random questions throughout the day (“What’s a good hook?” “How do I structure this?” “Quick meta description?”)

What I Used to Do:

• Open Claude → New chat → Ask → Close
• Repeat 20 times
• Death by a thousand context resets

What I Do Now:

# My local LLM runs in terminal, always available
ollama run llama3:8b

# Quick content queries, no context loss
> "Write 3 headline variations for productivity post"
> "Create meta description for remote work article"
> "Suggest 5 related topics to cover next"

No rate limits. No new chats. Just persistent conversation.

Real-World Performance: My Numbers

I tracked my workflow for two weeks—one week with Claude only, one week with my hybrid setup.

Week 1 (Claude Free Tier Only):

• Blog posts I drafted: 8
• Times I hit rate limits: 12
• Hours waiting for rate limit reset: ~18
• Context re-explanations needed: ~30
• My frustration level: High

Week 2 (My Hybrid Setup):

• Blog posts I drafted: 12 (50% increase)
• Times I hit rate limits: 2
• Hours wasted waiting: 0 (used local LLM instead)
• Context re-explanations needed: 3
• Local LLM queries I made: 150+
• My Claude sessions: More focused, higher quality output
• My frustration level: Minimal

Key insight: My local LLM didn’t make individual tasks faster. It made my entire workflow more efficient by eliminating wait times and handling the repetitive heavy lifting.

The Honest Trade-Offs

Let me be real about limitations I’ve experienced:

What My Local LLM Does Worse:

❌ Quality – My 7B models aren’t Claude-level
❌ Speed – I wait 30-60 seconds vs. Claude’s 2-5 seconds
❌ Setup – I spent time on initial technical work
❌ Maintenance – I manage updates, models, storage myself

What My Local LLM Does Better:

✅ Memory – Persistent across infinite sessions
✅ Cost – Free after my hardware investment
✅ Privacy – My data never leaves my machine
✅ Availability – No rate limits I can hit, no downtime
✅ Customization – I can fine-tune for my specific needs

When I Still Use Cloud AI Only:

• I need best-in-class quality every time
• Speed is critical (client calls, live demos)
• I’m working on simple one-off queries
• Setup complexity isn’t worth the benefit for that task

Cloud AI isn’t dying. It’s just not my only tool anymore.

Models I Actually Recommend

After testing 15+ models on my hardware, here’s what I use:

For General Use:

Llama 3 8B (Quantized) – My daily driver

• Best all-around performance
• Good reasoning and coding
• Handles context well
• ollama pull llama3:8b

For Coding:

Mistral 7B Instruct – When I need code generation

• Fast inference
• Strong code generation
• Good at following instructions
• ollama pull mistral:7b-instruct

For Speed:

Phi-3 Mini – When I need quick answers

• Smallest footprint
• Fast responses
• Surprisingly capable for size
• ollama pull phi3:mini

What I Avoid on My Hardware:

❌ Llama 70B+ (needs 40GB+ VRAM I don’t have)
❌ Mixtral 8x7B (too large for my 6GB VRAM)
❌ Unquantized models (memory hog)

I stick to quantized 7-8B models on my consumer hardware.

Advanced: My Content Creation System

For those who want to build something similar, here’s what I coded:

My Content Context Manager (Python):

import requests
import json

class ContentContextManager:
    def __init__(self, model="llama3:8b"):
        self.model = model
        self.url = "http://localhost:11434/api/generate"
        self.context = []
    
    def add_context(self, text):
        """Store content context in my local LLM"""
        self.context.append(text)
        prompt = f"Remember this for our content project: {text}"
        self._query(prompt)
    
    def get_context(self, query):
        """Retrieve relevant context for content creation"""
        prompt = f"Based on our content discussions: {query}"
        return self._query(prompt)
    
    def _query(self, prompt):
        response = requests.post(self.url, json={
            'model': self.model,
            'prompt': prompt,
            'stream': False
        })
        return response.json()['response']

# How I use it
ctx = ContentContextManager()
ctx.add_context("Working on Q4 content strategy")
ctx.add_context("Focus: long-form SEO guides and product comparisons")
ctx.add_context("Target: 15 posts per month, 2000+ words each")

# Later when I need it...
result = ctx.get_context("What's my content goal for this quarter?")
print(result)

My Editorial Workflow Integration:

import anthropic

def hybrid_content_workflow(topic, context_manager, use_claude=False):
    """How I route content creation intelligently"""
    
    # Get context from my local LLM
    context = context_manager.get_context(f"Content brief for: {topic}")
    
    if use_claude:
        # Use Claude for final content with local context
        client = anthropic.Anthropic(api_key="my-key")
        response = client.messages.create(
            model="claude-sonnet-4-5-20250929",
            messages=[{
                "role": "user",
                "content": f"Context: {context}\n\nWrite final draft for: {topic}"
            }]
        )
        return response.content[0].text
    else:
        # Use my local LLM for drafts and outlines
        return context

# Example of my actual workflow
ctx = ContentContextManager()
ctx.add_context("Writing productivity content for remote workers")
ctx.add_context("Audience: developers and technical professionals")

# First pass → my local LLM (draft/outline)
outline = hybrid_content_workflow("Time management for developers", ctx)

# Final pass → Claude (polish and quality)
final_content = hybrid_content_workflow("Time management for developers", 
                                       ctx, use_claude=True)

My Automated Editorial Script:

def batch_content_generation(topics, output_dir="drafts"):
    """My weekly content generation workflow"""
    ctx = ContentContextManager()
    
    for topic in topics:
        print(f"Processing: {topic}")
        
        # Step 1: Generate outline (local LLM)
        outline = ctx.get_context(f"Create outline for: {topic}")
        
        # Step 2: Generate first draft (local LLM)
        draft_prompt = f"Write 1500-word draft based on: {outline}"
        draft = ctx._query(draft_prompt)
        
        # Step 3: Edit for clarity (local LLM)
        edit_prompt = f"Improve clarity and flow: {draft}"
        edited = ctx._query(edit_prompt)
        
        # Step 4: Save for manual review
        filename = f"{output_dir}/{topic.replace(' ', '_')}.md"
        with open(filename, 'w') as f:
            f.write(edited)
        
        print(f"Saved draft: {filename}")
    
    print("All drafts ready for Claude final polish")

# My Monday morning routine
weekly_topics = [
    "Best productivity tools for remote teams",
    "How to structure your workday for deep work",
    "Remote work ergonomics guide",
    "Async communication best practices"
]

batch_content_generation(weekly_topics)

This is my actual workflow: My local LLM does the heavy lifting (drafts, outlines, edits), Claude does the final polish that makes content shine.

Cost Analysis: Was This Worth It?

Let me do the math on my 3-month experience:

What I Used to Spend (ChatGPT Plus Era):

• ChatGPT Plus: $20/mo × 3 = $60
• Claude: Free tier (hitting limits constantly)
• Frustration with GPT-5: Priceless
• My Total: $60 + constant rate limit pain

After I Cancelled ChatGPT:

• Claude: Free tier only
• Rate limits: Hit daily
• Productivity: Tanked
• My Total: $0 but work suffered

What I Spend Now (Hybrid):

• Hardware: I already owned it ($0 incremental)
• Electricity: ~$5/mo running local LLM = $15 (3 months)
• Claude: Still free tier, but rarely hit limits now
• ChatGPT: Cancelled, don’t miss it
• My Total: $15 + gained productivity

I’m spending less than ChatGPT Plus and getting better results. No subscriptions. No rate limit anxiety. No GPT-5 disappointment.

My ROI comes from:

• Time I save not re-explaining context
• Zero hours wasted waiting for rate limits
• Better workflow efficiency
• Claude free tier becomes viable for serious work
• No subscription regret

Common Pitfalls I Hit (And How I Fixed Them)

1. “My local LLM gives worse answers than Claude”

Yes. I expected that. I use it for different tasks now.

2. “It’s too slow”

I run quantized models. I enabled GPU acceleration. I adjusted my expectations—30 seconds isn’t bad for persistent context.

3. “I ran out of RAM”

I close other apps now. I use smaller models. If I get serious about this, I’ll upgrade to 32GB.

4. “Setup is complicated”

I started with Ollama. One command. Once that worked, I explored advanced options.

5. “I don’t know which model to use”

I started with Llama 3 8B. It’s the best general-purpose model for my consumer hardware.

The Future: Where I See This Heading

Local LLMs are getting better fast, and I’m watching closely:

2023: Local models were toys compared to GPT-4
2024: 7B models rival GPT-3.5 quality
2025: Llama 3.1 and Mistral approaching GPT-4 territory

What I’m expecting:

• Better quantization (same quality, less memory)
• Smaller models with comparable performance
• Native OS integration (Apple Intelligence, Windows Copilot)
• Hybrid cloud/local architectures built into products

The trend is clear to me: Local AI isn’t replacing cloud AI. It’s becoming the foundation layer with cloud as the enhancement.

Should You Try This?

I’d recommend it if you: ✅ Have ongoing projects requiring persistent context
✅ Hit Claude/ChatGPT rate limits regularly
✅ Have 16GB+ RAM and a decent GPU (or don’t mind CPU speed)
✅ Are comfortable with basic command-line tools
✅ Value privacy and control over convenience

I wouldn’t recommend it if you: ❌ Only use AI occasionally
❌ Speed is more important than memory
❌ Are happy paying for cloud AI subscriptions
❌ Setup complexity outweighs benefits
❌ Your hardware is too limited (8GB RAM or less)

For me? Running five blogs with continuous SEO work, content planning, and technical writing—this setup is indispensable.

Getting Started (Your First 30 Minutes)

Here’s how I did it:

Step 1: Install Ollama (5 minutes)

curl -fsSL https://ollama.ai/install.sh | sh

Step 2: Pull a Model (5 minutes)

ollama pull llama3:8b

Step 3: Test It (5 minutes)

ollama run llama3:8b
> "Hello, you're my new context manager. Remember: I'm working on..."

Step 4: Build Integration (15 minutes)

• I created a simple Python script to query my local LLM
• I tested with real context from my work
• I integrated with my existing workflow

That’s it. I had persistent AI memory in 30 minutes.

Final Thoughts

Claude is incredible—even on the free tier. I’m not abandoning it. I’m augmenting it.

After getting disappointed by GPT-5 and cancelling my ChatGPT subscription, I realized the bottleneck in AI-assisted work isn’t paying for the best model. It’s the architecture. Memory resets. Rate limits. Subscription lock-in. These aren’t model problems. They’re infrastructure problems.

Running a local LLM doesn’t solve intelligence for me. It solves continuity and availability.

And for anyone doing serious, ongoing work with AI—especially on free tiers—continuity and availability are everything.

My advice? Don’t rush to subscribe to the next AI service. Build your own infrastructure first. Use cloud AI for what it’s best at. Use local AI for what cloud AI can’t handle.

Go break your dependency on cloud AI rate limits and subscription fatigue. Your future self will thank you.

Related Reading:

• Claude 4.5 vs Claude 4: The Content Gauntlet – Why I trust Claude’s quality
• GPT-5 vs GPT-4: Why I Cancelled My Subscription – What pushed me to local AI
• Ollama vs GPT4All vs Local LLMs: Complete Comparison – Deep dive on choosing the right local LLM setup

Resources:

• Ollama – Easiest local LLM setup (what I use)
• LM Studio – GUI for model management
• Text Generation WebUI – Advanced interface
• Llama Models – Meta’s open models
• Mistral AI – Fast, efficient models

Hardware Upgrade Path (If You Get Serious Like I Might):

• 32GB RAM (~$80) – Run larger models on CPU
• RTX 4060 12GB (~$300) – Sweet spot for local LLM
• RTX 4070 Ti 16GB (~$700) – Run 13B+ models comfortably

I’m running a local LLM on an i7-8700, GTX 1660, and 16GB RAM. Managing five blogs. Zero regrets.

Jaren Cudilla / Chaos Engineer
Builds hybrid AI setups that blend Claude’s reasoning with a local LLM’s memory.
This article was written by Claude 4.5 while testing persistent context against Claude 4.

Runs EngineeredAI.net — documenting which AI models actually ship content vs which ones lecture.
Breaks down voice matching, obedience, and context retention in real production workflows.
If a model argues with instructions, it fails the gauntlet.

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