# Coding LLMs on 8GB VRAM: Start with a Bounded Task

Test coding LLMs on 8GB VRAM with a small artifact, bounded context and executable checks instead of assuming a complete repository assistant.

Author: Retrivium team
Updated: 2026-11-01
Canonical URL: https://retrivium.ai/journal/coding-llm-8gb-vram/
Language: en

For coding LLMs on 8GB VRAM, begin with a small specific model, short input and one session. This creates a testable starting point, not a guarantee that every similarly named artifact fits. Memory must cover execution as well as weights, and useful code must pass the task's checks.

A narrow function repair can be a meaningful experiment even when the machine is unsuitable for a large repository-wide assistant.

## Select a bounded candidate

Qwen2.5-Coder-1.5B-Instruct is one official artifact whose card can inform a test. It is not a universal winner for every 8GB GPU. Preserve its complete identifier, revision, weight format and loading method.

If using a converted file, record the converter and relationship to the original model. A small parameter count does not establish suitability for your language, framework or required output.

## Estimate weights without confusing them with service memory

An illustrative 1.5 billion parameters at two bytes each gives about three billion bytes of weights. This is arithmetic, not a measurement of a particular build or complete service.

Hugging Face's quantisation overview describes approaches that can reduce weight storage. Support and quality need checking separately. Leave room for request state, buffers and the owner's graphical applications or other processes.

| Check | Evidence |
| --- | --- |
| Artifact loads | Exact configuration and memory |
| Short request completes | Input and output limits |
| Code is useful | Named tests pass |
| Context expands | New memory and timing record |
| Owner resumes work | Verified resource release |

## Test code rather than presentation

Prepare three small tasks: write a pure function, fix a known defect and return a patch in an agreed format. Define expected behaviour and tests. Do not run arbitrary generated code with access to production secrets.

Record syntax, test outcomes and format compliance. Confident explanation is not a substitute for a working change. Keep failures, especially cases that alter the function interface or unrelated code.

## Expand context only for the task

Add one function, then a longer relevant fragment. Do not start at the largest advertised window. Keep input length and allowed output separate. Unexpected verbosity may involve the instruction or template rather than insufficient VRAM.

Compare startup and an already loaded model, then test release back to the owner's applications. There is no need to purchase hardware based on a claim that a memory number guarantees every coding workflow.

Continue with [Coding LLMs on 12GB VRAM: Budget Context and Headroom](/journal/coding-llm-12gb-vram/) and [Local LLMs on 16GB VRAM: Narrow the Model Choice](/journal/local-llm-16gb-vram/).

## Official references

- [Official model documentation — Qwen2](https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-Instruct)
- [Official model documentation — overview](https://huggingface.co/docs/transformers/quantization/overview)