Below 14 Billion Parameters, Local Coding Models Are Not Worth the Tinkering

8 billion parameters.

One failing tool call.

Zero completed tasks.

The threshold nobody talks about

You download a local model because you want privacy, or offline access, or just to stop paying per token. The 8B model fits in memory. It generates code. You think you're set.

Then you try to use it with an agent.

The model writes a function. The agent tries to apply the diff. The diff is malformed. The agent asks the model to fix it. The model generates something worse. You're three iterations in and the file is now broken in a way that didn't exist before you started.

This is not a prompting problem. This is a capability floor.

Where the floor actually is

Small models can autocomplete. They can fill in a function body when you give them enough context. But agentic coding requires more than generation. The model needs to read tool output, call the right tools in sequence, apply diffs cleanly, and iterate when something fails.

At 8 billion parameters, that chain breaks.

"I found 8 billion parameter models to kind of be useless at coding in Roo Code. So if I'm going to be using an 8 billion parameter model, it's typically a copy and paste situation."

Adam, Office Hours S01E09

Copy and paste means you're back to being the human glue. You read the output, decide what's relevant, paste it into the next prompt, and hope the model incorporates it correctly. The loop isn't closing. You're closing it manually.

The practical threshold is 14 billion parameters and up.

"I would say 14 billion up is about the place that I'd start considering trying it in your code."

Adam, Office Hours S01E09

At 14B, models start handling the full sequence: read context, propose changes, apply diffs, interpret errors, iterate. Not perfectly. But well enough that you're debugging your code instead of debugging the model.

The hardware constraint

Parameter count is only half the equation. You also need enough memory to load the model with meaningful context.

"You need something with like 24 gigabytes of VRAM or something with unified memory that you can run it on, just simply because you need a pretty decent amount of context loaded in."

Adam, Office Hours S01E09

A 14B model quantized to fit in 8GB of VRAM will have a tiny context window. You'll be able to run it, but it won't see enough of your codebase to be useful. The model will hallucinate file paths, miss imports, and suggest changes to functions it can't see.

24GB VRAM (or Apple Silicon with unified memory) is the practical minimum for local agentic coding. Below that, you're trading context for the ability to run the model at all.

The real tradeoff

Local models are not free. You pay in hardware, in setup time, and in capability limits.

If you have 24GB+ of VRAM and want to run a 14B+ model for privacy or latency reasons, it works. You'll get agentic iteration that closes the loop without sending code to an external provider.

If you have less hardware, or you're trying to run smaller models to save resources, you'll spend more time debugging the model than coding with it. The "free" inference costs you hours.

How Roo Code closes the loop with local models

Roo Code's BYOK architecture means you choose your inference provider, including local endpoints. When you connect a capable local model (14B+ with sufficient context), Roo Code orchestrates the full agentic workflow: proposing diffs, executing commands, running tests, and iterating on failures.

The key difference from copy-paste workflows is that Roo Code handles tool calls, diff application, and error recovery automatically. You review and approve; the agent handles the iteration mechanics. But this only works when the underlying model can reliably complete tool-calling sequences.

For local model users, the practical threshold is 14 billion parameters minimum with 24GB VRAM to maintain enough context for Roo Code to close the loop effectively.

Local model approach comparison

Why this matters for your workflow

For engineers evaluating local models: the 8B models you see benchmarked on code completion tasks are not the same as 8B models handling agentic workflows. Benchmarks measure generation quality in isolation. Agent tasks measure the full chain: tool calling, diff application, error recovery, iteration.

The chain is where small models fail. And that failure mode is insidious because the model still generates confident, plausible-looking code. You won't know it's broken until you've spent twenty minutes tracing why the diff didn't apply.

The spec

Before you set up local inference for agentic coding:

1. Model size: 14 billion parameters minimum
2. Memory: 24GB VRAM or unified memory minimum
3. Context: Enough headroom for your codebase, not just the model weights

Below those thresholds, you're not saving time. You're spending it on infrastructure that won't close the loop.