---
title: "Local coding on Linux with 8–32 GB, no GPU"
description: "The local coding setup we tested on a 7 GB Linux x64 container: Qwen3 1.7B Q4_K_M on llama.cpp, its measured run, and the commands."
canonical: https://oloop.run/local/linux-cpu-8-32
updated: 2026-10-05
evidence: /setups/evidence/linux-x64-cpu@1.2.2.json
---

# Local coding on Linux x64 with 8–32 GB, no GPU

The tested setup is llamacpp/qwen3-1.7b on llamacpp: Q4_K_M weights, a 32,768-token context, and a 1.1 GB download. On a Linux x64 container with 7 GB of RAM, the probe took 157.3 s cold and 119.2 s warm, with a peak of 5.82 GB of RAM. 7 GB is below the 8 GB floor the catalog sets for this class.

## Run it

Install the CLI. Start this exact setup. Open the harness. The first pull creates a free handle.

An explicit setup runs on the OS its catalog entry declares, after that entry's RAM, VRAM, and driver checks. The no-arg bootstrap is macOS on Apple silicon only.

```sh
curl -fsSL https://oloop.run/install.sh | sh
oloop host start --setup linux-x64-cpu@1.2.2
oloop host open
```

- [Install and requirements](/docs#install)
- [Setup compatibility](/docs#compatibility)
- [The exact plan and lock](/docs#exact-plan)
- [This setup in the catalog](/setups#linux-x64-cpu-1.2.2)

## Why this setup without a GPU

llamacpp/qwen3-1.7b is a 1.7B model at Q4_K_M, run by llamacpp on the CPU. The download is 1.1 GB and the measured peak was 5.82 GB of system RAM, so it runs on a small box with no discrete card.

The trade is capability. This is a smaller model, and it fits where an 8B model would not. With a GPU and more memory, the L4 page is the measured step up.

The exact pins and hardware.

| Fact | Value |
| --- | --- |
| OS / arch | linux / x64 |
| Memory kind | cpu |
| Host | llamacpp@b11292 |
| Harness | opencode@1.18.21 |
| Model | llamacpp/qwen3-1.7b |
| Model revision | unsloth/Qwen3-1.7B-GGUF@d7f544eead698dbd1f15126ef60b45a1e1933222 |
| Quantization | q4_k_m |
| Context window | 32,768 tokens |
| Download | 1.1 GB |

Source: /setups/evidence/linux-x64-cpu@1.2.2.json (qualified 2026-10-05, engine 0.1.0).

- [The L4 setup we measured](/local/nvidia-l4)

## Measured results

The timer measures one probe. The prompt asks for the word OK in answer.txt, and the run is capped at 3 turns. Cold is the first run, which starts the host and loads the model; warm repeats the same probe on the same machine. It is not coding throughput and not a tokens-per-second figure.

Warm was 38.1 s faster than cold here. One probe pair gives a direction, not a rate.

The qualification run on the tested machine.

| Run | Cold | Warm | Peak RAM | Peak VRAM |
| --- | --- | --- | --- | --- |
| Probe | 157.3 s | 119.2 s | 5.82 GB | none (no discrete GPU) |

Measured on Linux x64, 7 GB RAM on 2026-10-05. Source: /setups/evidence/linux-x64-cpu@1.2.2.json.

## Tested on, listed for

Tested on: Linux x64, 7 GB RAM.

Listed for: Linux x64, 8–32 GB RAM.

The catalog lists a range, and the record covers one machine in it. The times and memory numbers belong to that machine.

## What the run proves

One pinned setup passed one probe on the stated hardware and date, using the checksummed bundle from the catalog.

- The run is not a security review. It checks the named file; it does not audit the model, the harness, or their permissions.
- A pass is not a quality score. It means the configured check passed, not that every answer is correct.
- The model license belongs to the model publisher. Read it before you ship the output.
- The bundle is pinned to one version. A newer catalog version is a different record.
- The tested box had 7 GB, below the catalog's 8 GB floor. Its times are not a 32 GB machine's times.

- [What the checks mean](/learn#checks)
- [All machine setups](/setups)

## Questions

### Does this need a GPU?

llamacpp/qwen3-1.7b runs on the CPU at Q4_K_M. The record lists no discrete card.

### Will it run in 8 GB of RAM?

The measured peak was 5.82 GB on a 7 GB box, so an 8 GB machine has more headroom than the test box did. The catalog lists 8–32 GB, and we measured only the 7 GB box.

### Why such a small model?

A 1.7B model is what fits and starts on a small CPU box. It is a coding model, and it is not the same capability as an 8B model on a GPU.

### What is the step up from here?

The NVIDIA L4 class is the measured GPU step. More RAM on the same CPU is a different configuration for a larger model, and it is unmeasured.

### Can I use a larger model on a CPU box?

Only as a new qualification. A 7B model needs more memory and a longer download, and we have not measured one on a CPU box.

## Related

- [Local coding on Apple silicon, 64–128 GB](/local/apple-silicon-64-128)
- [Local coding on one NVIDIA L4, 24 GB](/local/nvidia-l4)
- [Local coding model by memory size](/guides/local-coding-model-by-memory)
- [Why own the agent runtime](/guides/why-own-the-runtime)

## Evidence

Tested on Linux x64, 7 GB RAM. Listed for Linux x64, 8–32 GB RAM.

- [The qualification record](/setups/evidence/linux-x64-cpu@1.2.2.json)
- [linux-x64-cpu@1.2.2](/setups#linux-x64-cpu-1.2.2)

Updated 2026-10-05 · Outerloop