# Coding LLMs on 12GB VRAM: Budget Context and Headroom

Plan coding LLMs on 12GB VRAM using exact artifacts, compatible quantisation, code tests and separate input, output and concurrency limits.

Author: Retrivium team
Updated: 2026-11-01
Canonical URL: https://retrivium.ai/journal/coding-llm-12gb-vram/
Language: en

Choosing a coding LLM for 12GB VRAM requires more than a 7B label. Seven billion parameters at 16 bits imply roughly fourteen billion bytes of weights before request state and buffers. Inspect the exact artifact and supported format, then test useful code quality.

A smaller model that completes your task correctly is a valid alternative to the largest candidate. The goal is a reliable change, not a parameter-count milestone.

## Read the original model card

Qwen2.5-Coder-7B-Instruct provides one concrete official artifact to investigate. Its name does not establish that every loading method fits a 12GB device. Record revision, parameter information and recommended environment.

Rounded parameter names support rough arithmetic only. Tensor composition and runtime structures still require measurement. Converted builds need their own provenance and compatibility record.

## Match the numerical format

vLLM's quantisation documentation describes support by method and hardware. A launch flag does not transform arbitrary weights into any desired compatible format.

An illustrative four-bit estimate for seven billion parameters is about 3.5 billion bytes. Metadata, scales, higher-precision tensors and runtime memory are outside that simplified calculation. Successful loading should not lead to an unlimited context setting.

| Test condition | Keep fixed | Observe |
| --- | --- | --- |
| Weight-format comparison | Same coding tasks | Quality and memory |
| Longer input | Artifact and output limit | First-token delay and peak |
| Longer output | Input and format | Completion and resource use |
| Second session | Working single-session baseline | Concurrent behaviour |

## Use three concrete code changes

Prepare a defect repair in one function, a small interface extension and a patch-format response. Retain tests and restrictions on unrelated edits. Check that referenced APIs exist and the requested interface remains intact.

When comparing two quantisations, use the same cases. An easier task set on the smaller format does not demonstrate preserved quality. Store failure examples rather than relying on an overall impression of competence.

## Test the real working context

Start with one short file, then add its dependency and change requirements. Record token counts, memory and time at each step. A model's published window is not the available capacity of your host at the chosen concurrency.

Add another session only after stable single-session work. A concurrent failure needs a different investigation from a failure to load weights. The result can support a specific pilot without promising an unrestricted coding assistant.

Continue with [Coding LLMs on 8GB VRAM: Start with a Bounded Task](/journal/coding-llm-8gb-vram/) and [A 14B Model on 16GB VRAM: Understand the Bit Estimate](/journal/llm-14b-16gb-memory/).

## Official references

- [Official model documentation — Qwen2](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct)
- [vLLM — quantization](https://docs.vllm.ai/en/stable/features/quantization/)