My 2-Month local llm daily coding replacement: Real Benchmarks

This article was originally published on BuildZn.

Everyone talks about cutting cloud LLM costs, but nobody gives you the raw numbers and real workflow impact. I've been running on-device for my daily dev work for two months straight, pushing for a full local llm daily coding replacement. Figured it out the hard way.

Why I Bothered with local llm daily coding replacement

Look, the Claude API bills were getting wild. We're talking $100-$150/month just for me, mostly on opus and sonnet for code generation and refactoring in Flutter and Node.js. Multiply that by a team, and it's a significant burn. Plus, network latency, even for a few hundred ms, adds up to serious context switching friction over a full day. Data privacy is another one, especially with client code. So, the goal was clear: ditch the cloud, embrace self hosted llm coding.

Here's why I went down this rabbit hole:

• Cost Savings: Obvious one. Cut recurring API spend.
• Latency: Local inference is fast, once the model is loaded. No network roundtrips.
• Privacy: Keep proprietary code off third-party servers.
• Control: Fine-tune models, experiment with quantization, no API rate limits.

The Setup: Ollama and a Beefy Rig

You're not doing this on a MacBook Air. I'm running a custom build: Ryzen 9 7950X, 64GB DDR5, and an RTX 4090. That GPU is non-negotiable for any serious local llm code generation.

I went with Ollama 0.1.33. It's the easiest entry point for self hosted llm coding, hands down. It handles model downloads, serving, and even supports multiple models concurrently.

First, get Ollama running:

# Install Ollama (macOS example, check their site for other OS)
curl -fsSL https://ollama.com/install.sh | sh

# Pull models. These are my go-tos.
ollama pull llama3:8b # Great all-rounder
ollama pull codellama:7b-instruct # Specific code tasks
ollama pull phi3:mini # Sometimes surprisingly good for small functions

Once Ollama is up, you'll want an IDE integration. I primarily use VS Code.

• Continue.dev: This is a fantastic copilot alternative. It lets you configure local Ollama models directly.
• Code GPT: Also supports Ollama, though I found Continue.dev's UI a bit more intuitive for multi-turn conversations.

My ~/.continue/config.json looks something like this, pointing to my local Ollama instance for llama3:8b:

{
    "$schema": "https://json.schemastore.org/continue.json",
    "models": [
        {
            "name": "llama3",
            "provider": "ollama",
            "model": "llama3:8b",
            "apiBase": "http://localhost:11434"
        }
        // ... other models ...
    ],
    "completionOptions": {
        "temperature": 0.2,
        "topP": 0.9,
        "maxTokens": 1024
    },
    "tabAutocompleteModel": {
        "name": "llama3", // Or a lighter model like phi3:mini for faster suggestions
        "provider": "ollama",
        "model": "llama3:8b"
    }
}

This setup gets you going.

Real-World Impact: Flutter, Node.js, and Benchmarks

Here's where the rubber meets the road. Can local LLMs genuinely replace cloud models for daily coding? For flutter llm coding and nodejs llm coding, the answer is a nuanced "mostly."

Benchmarks & Performance

On my dev rig (Ryzen 9 7950X, 64GB DDR5, RTX 4090), codellama:7b-instruct-q4_K_M via Ollama 0.1.33 consistently hits 38.7 tok/s for a 200-token Flutter ListView.builder scaffold. This was averaged over 50 identical prompts, measuring llm_local_eval_response_time from ollama logs and token counts. Anything less than Q4_K_M quantization for smaller models felt like a significant step down in coherence without huge perf gains. llama3:8b-instruct-q4_K_M delivers around 32.1 tok/s for similar prompts. These numbers are fantastic; latency is essentially instant for small to medium responses.

Flutter (Dart) Coding

For Flutter widget generation and boilerplate, local LLMs are surprisingly capable. I'd say llama3:8b with Continue.dev provides ~80% of Claude/GPT's utility for Flutter widget generation and basic boilerplate.

What works well:

• Generating StatefulWidget or StatelessWidget boilerplate: Give it a prompt like "Create a UserProfileScreen that displays user data from a User object," and it nails the basic structure, build method, and state management hooks.
• Bloc/Riverpod boilerplate: "Generate Bloc events and states for a LoginBloc." It gets the Equatable boilerplate and basic event/state classes right.
• Simple utility functions: debounce, formatDate, validation helpers.
• DartDoc comments: Quick and accurate.

Here's an example: I often need a FutureBuilder wrapped in a Consumer (for Riverpod). Instead of typing it out:

// Prompt:
// Generate a Flutter widget using Riverpod's Consumer and FutureBuilder.
// It should fetch a list of products and display them in a ListView.
// Show a loading indicator, error message, and empty state.

// Response (truncated for brevity):
import 'package:flutter/material.dart';
import 'package:flutter_riverpod/flutter_riverpod.dart';

// Assuming you have a Product model and a provider
class Product {
  final String id;
  final String name;
  final double price;

  Product({required this.id, required this.name, required this.price});
}

final productListProvider = FutureProvider<List<Product>>((ref) async {
  // Simulate network delay
  await Future.delayed(const Duration(seconds: 2));
  // Simulate an error sometimes
  // throw Exception('Failed to load products');
  return List.generate(5, (index) => Product(id: '$index', name: 'Product ${index + 1}', price: 10.0 * (index + 1)));
});

class ProductListPage extends ConsumerWidget {
  const ProductListPage({super.key});

  @override
  Widget build(BuildContext context, WidgetRef ref) {
    final productListAsyncValue = ref.watch(productListProvider);

    return Scaffold(
      appBar: AppBar(
        title: const Text('Products'),
      ),
      body: productListAsyncValue.when(
        data: (products) {
          if (products.isEmpty) {
            return const Center(child: Text('No products found.'));
          }
          return ListView.builder(
            itemCount: products.length,
            itemBuilder: (context, index) {
              final product = products[index];
              return Card(
                margin: const EdgeInsets.symmetric(horizontal: 16, vertical: 8),
                child: ListTile(
                  title: Text(product.name),
                  subtitle: Text('\$${product.price.toStringAsFixed(2)}'),
                  trailing: IconButton(
                    icon: const Icon(Icons.add_shopping_cart),
                    onPressed: () {
                      // Handle add to cart
                    },
                  ),
                ),
              );
            },
          );
        },
        loading: () => const Center(child: CircularProgressIndicator()),
        error: (error, stack) => Center(child: Text('Error: ${error.toString()}')),
      ),
    );
  }
}

It's not always perfect, but it's a solid 80% there, saving significant boilerplate typing.

Node.js (TypeScript) Coding

This is where things get trickier. While local models are decent for simple nodejs llm coding tasks, they noticeably struggle with complex Node.js refactoring across multiple files or intricate TypeScript type inference.

What works well:

• Express route boilerplate: Setting up a basic /users or /products route with CRUD operations.
• Simple utility functions: Date formatting, string manipulation, basic validation.
• Database schema snippets (MongoDB/Mongoose, basic Supabase): Defining models or interfaces.
• Jest/Vitest test boilerplate: Generating describe and it blocks for a given function.

Where it falls short (compared to Claude/GPT-4):

• Complex TypeScript type inference: If you have deep nested types or generics, local models often get lost, leading to any or incorrect type suggestions.
• Multi-file refactoring: "Refactor serviceA.ts to use a new utilB.ts function, and update all callers in controllerC.ts." Cloud models handle this with higher success. Local models struggle to maintain global context.
• Nuanced architectural decisions: Asking for advice on structuring a large Express application or designing a microservice boundary often yields generic or less optimal patterns.
• Advanced regex or complex algorithm generation.

Overall, for simple local llm daily coding replacement tasks in Node.js, llama3:8b is perhaps 60-70% effective. For heavy lifting, I still find myself reaching for Claude Opus.

This setup is saving me roughly $70/month in API costs. The initial investment was about 15 hours in setup and calibration time, plus a subtle hit from context switching latency when local output isn't perfect, but the long-term savings and privacy benefits outweigh that.

What I Got Wrong First

Moving to self hosted llm coding wasn't a straight shot. I hit a few walls.

1. Underestimating Model Size vs. Quality: I started with phi-2 and mistral. They're fast but often hallucinate or provide too generic code. I thought "smaller is better for local." Turns out, llama3:8b-instruct or codellama:7b-instruct are the minimum viable models for reliable code generation. Anything less and you're just wasting time debugging model output.
2. Quantization Sweet Spot: I experimented with q8_0, q5_K_M, etc. Initially, I just went for the lowest quantization for speed. However, q4_K_M or q5_K_M consistently offered the best balance of quality and inference speed on my RTX 4090. Going lower significantly degraded response quality, leading to more manual fixes. Higher quantization increased VRAM usage without a proportional quality jump for coding tasks.
3. Ignoring Prompt Engineering for Local: I used the same prompts I'd give Claude Opus. Big mistake. Local models, especially smaller ones, need more explicit instructions, fewer implicit assumptions, and often a few-shot example to guide them. You need to be far more verbose.
4. Expectations for Refactoring: I genuinely believed I could do multi-file refactoring with local LLMs. That was naive. The context window limitations, even for 8B models, make this a pipe dream without a much more sophisticated RAG setup or agentic workflow, which defeats the "simple local replacement" goal.

The Context Window Problem

Here's the thing — the biggest limitation for serious coding tasks isn't token generation speed; it's the context window. Even with 8K or 16K context, a complex Node.js service with multiple dependencies and data models often spans more than that. You can feed it snippets, but asking it to reason about the entire codebase state or perform multi-file changes is still largely in the domain of cloud models with larger contexts (e.g., Claude 200K, GPT-4o 128K).

For a true local llm daily coding replacement, especially in a large codebase, we need significantly larger context windows on consumer hardware or more intelligent RAG systems integrated directly into the IDE. This is where Continue.dev helps by intelligently pulling relevant files, but it's not foolproof.

FAQs

Can I run these models without a powerful GPU?

You can, but don't expect the same performance. On a CPU, even a decent one, llama3:8b will be significantly slower (e.g., <5 tok/s). Integrated GPUs might give you a slight bump, but an RTX 30 series or better is truly needed for a usable experience.

What's the best local LLM for Flutter code generation?

For Flutter, I've had the most success with llama3:8b-instruct-q4_K_M or codellama:7b-instruct-q4_K_M running via Ollama. llama3 is a bit more general-purpose, while codellama excels at pure code completion and generation.

Is it worth the setup time to replace cloud LLMs?

If you're spending more than $50/month on cloud LLM APIs for coding tasks, and you have the hardware, absolutely. The initial setup is a pain (10-15 hours for me), but the long-term cost savings, privacy, and instant local latency are major wins. Just manage your expectations for complex refactoring.

Honestly, a full local llm daily coding replacement isn't 100% there for everything yet, but for 80% of my boilerplate and single-file coding needs in Flutter, it's a total game-changer. For Node.js, it's closer to 60-70%. It saves me cash, keeps my code private, and the instant responses feel like magic. It's not a silver bullet, but if you're a developer burning through API credits, this is the immediate future. Just grab that RTX 4090 first.