Ollama under WSL2 or native Windows: which should you choose? ?

#The challenge: two Ollama on the same machine

Since Ollama introduced a native Windows installer, the question « ollama wsl2 or native? » keeps coming up. Both approaches run exactly the same daemon, listen by default on http://localhost:11434, and serve the same GGUF models. The difference lies elsewhere: in how the GPU is exposed, where your files live, and the tool ecosystem you use around it.

In short, native Windows wins on installation simplicity and desktop integration, while WSL2 wins on consistency with a Linux/dev workflow and compatibility with tools that exist only on Unix. Neither is “better” in absolute terms—the right choice depends on what you do with your LLMs.

Native Ollama Windows

An .exe, an icon in the system tray, and the daemon starts with Windows. No Linux layer to manage.

Ollama under WSL2

A Linux distribution (usually Ubuntu) in which you install Ollama as you would on a server. Ideal if your stack is already Linux-based.

Common ground

Same API on port 11434, same models, same commands. You can even connect a Windows client to a WSL2 server and vice versa.

#Prerequisites

To compare the two fairly, the GPU must be properly supported in each environment. This is where most disappointments arise.

Windows 11 (or 10 recent ones)

WSL2 with GPU acceleration (WSLg) requires Windows 11 or a recent, up-to-date Windows 10 build.

Up-to-date GPU driver on Windows

Under WSL2, the Windows driver exposes the GPU to Linux through /dev/dxg. Install the latest NVIDIA driver (Game Ready or Studio) or AMD Adrenalin driver, NOT a Linux driver inside the distro.

WSL2 enabled

The “wsl --install” command from an administrator PowerShell installs WSL2 and a default Ubuntu distribution.

Sufficient VRAM

The Q4_K_M benchmarks remain the same in both worlds: 7B ≈ 5 GB, 14B ≈ 9 GB, 32B ≈ 19 GB, 70B ≈ 40 GB. WSL2 doesn't change these requirements.

#Cleanly install Ollama in WSL2

Installation on WSL2 is identical to installation on a Linux server: the official script detects the GPU exposed by WSLg and configures acceleration automatically.

1. 01
   Enable WSL2

   In an elevated PowerShell: “wsl --install”. Restart if prompted. Then verify with “wsl -l -v” that your distribution is indeed VERSION 2.
2. 02
   Update the distribution

   Open Ubuntu, then run “sudo apt update && sudo apt upgrade -y”. An up-to-date distro prevents surprises with GPU runtimes.
3. 03
   Install Ollama

   Run the official script: « curl -fsSL https://ollama.com/install.sh | sh ». It detects NVIDIA (through CUDA exposed by WSLg) or AMD (ROCm) and reports it in the logs.
4. 04
   Check the GPU

   Load a small model and then check « ollama ps »: the PROCESSOR column must indicate GPU, not CPU. If it says CPU, acceleration is not active.
5. 05
   Test a model

   “ollama run qwen3.5:9b,” then ask a question. This 2026 model (6.6 GB in Q4, 256k context, vision) is the default choice on an 8 GB card. Add --verbose to see the actual tokens per second.

# Dans le terminal Ubuntu de WSL2
sudo apt update && sudo apt upgrade -y

# Installation officielle d'Ollama
curl -fsSL https://ollama.com/install.sh | sh

# Vérifier la prise en charge du GPU
ollama pull qwen3.5:9b
ollama run qwen3.5:9b --verbose

# Le processeur utilisé (GPU attendu)
ollama ps

#GPU performance: native vs. WSL2, backed by numbers

This is THE question that motivates this guide. The good news: for LLM inference on NVIDIA GPUs, the gap between native Ollama and WSL2 is small. Once the model is loaded into VRAM, computation runs on the GPU in both cases, and WSL2 adds virtually no overhead to token generation itself.

In practice, on the same card (for example, a RTX 4070 12 GB with an 8B in Q4_K_M), generation throughput is very similar—the difference is typically within a few percent, often lost in measurement variance. Where WSL2 can incur a small cost is the model's initial load from disk: WSL2's filesystem is fast on its ext4 virtual disk, but accessing files stored on the Windows side (via /mnt/c) is significantly slower.

Token generation (GPU)

Nearly identical natively and under WSL2 on NVIDIA. The GPU does the work in both cases; the WSL2 layer is transparent to computation.

Loading the model

Fast if the models live in WSL2’s native Linux filesystem. Slow if Ollama reads its blobs from /mnt/c/... (crossing the Windows bridge).

Time to first token

Comparable, provided the model is already cached in VRAM. The initial cold load is the sensitive step.

CPU overhead

Negligible for inference. WSL2 is a lightweight real VM, not emulation; the virtualization overhead isn't noticeable on GPU workloads.

Conclusion on raw performance: if you have a NVIDIA card, the native-vs.-WSL2 choice does NOT come down to generation speed. Choose based on your workflow. Performance becomes a deciding factor again in only two cases: model storage (keep it on the Linux side under WSL2) and AMD support, which we cover now.

#The AMD case: ROCm under WSL2

On AMD GPUs, the story is different and more nuanced. Ollama uses ROCm to accelerate compatible Radeon cards. ROCm, however, was long problematic on Windows, and WSL2 changed the situation—in a way that is not always favorable depending on your card.

AMD natively on Windows

Ollama includes a ROCm library for Windows. On officially supported cards (RX 7000 recent cards, certain RX 6000), acceleration works without WSL2. This is often the simplest path for an AMD desktop.

AMD under WSL2

ROCm is available for WSL2, but the list of supported GPUs is narrower and the setup more involved. It may be necessary if you want a Linux toolchain, but it isn't inherently faster.

Unsupported cards

Many older Radeon cards and APUs aren't on the official ROCm list. On these cards, Ollama may fall back to the CPU in both environments.

HSA workaround

The HSA_OVERRIDE_GFX_VERSION variable can sometimes force recognition of a GPU close to a supported model. For advanced users only, with no guarantee.

# La carte AMD est-elle vue par ROCm dans WSL2 ?
rocminfo | grep -i 'Name\|gfx'

# Forcer une version gfx proche (avancé, sans garantie)
# Exemple pour cibler gfx1030 sur une carte non listée :
HSA_OVERRIDE_GFX_VERSION=10.3.0 ollama serve

#File access and VS Code integration

Beyond performance, file access is often what decides the outcome. Both file systems (Windows NTFS and WSL2’s Linux ext4) are accessible from the other, but crossing the bridge incurs a performance cost, and their path conventions differ.

From WSL2 to Windows

Your Windows drives are mounted under /mnt/c, /mnt/d, and so on. Convenient for reading a document folder, but slow for intensive access (loading large models, RAG indexing).

From Windows to WSL2

The Linux file system is accessible through the network path \\wsl$\Ubuntu\ in File Explorer. Useful for dropping off a file, but avoid having Windows tools work there intensively.

Golden rule

Keep each workload in its native filesystem. Linux dev project → in WSL2. Office documents → on the Windows side. This avoids the slow bridge.

On the VS Code side, WSL integration is excellent and strongly favors WSL2 for developer use. The official “WSL” extension opens a project directly in the distro: the VS Code server runs on Linux, the integrated terminal is a Linux shell, and your code calling the Ollama API runs in the same environment as the daemon.

import requests

# Même endpoint quel que soit l'environnement
resp = requests.post(
    "http://localhost:11434/api/generate",
    json={
        "model": "qwen3.5:9b",
        "prompt": "Explique WSL2 en une phrase.",
        "stream": False,
    },
    timeout=120,
)
print(resp.json()["response"])

#Which configuration is recommended for your use case

Here is the actionable summary. Choose based on your dominant profile rather than on tiny differences in tokens per second.

Desktop / consumer use (NVIDIA)

Native Ollama for Windows. One-click installation, starts with the session, and no Linux layer to maintain. Perfect with LM Studio or Open WebUI as the interface.

Linux developer / Unix stack

Ollama under WSL2. You get your tools (bash scripts, Docker, Python venv) in the same environment as the daemon, with exemplary VS Code integration.

Consumer AMD GPU

Test native Windows first: it is often the easiest to get running through the bundled ROCm stack. Switch to WSL2 only if your workflow requires it and your card is supported.

Server / network share

WSL2 more closely resembles a conventional Linux server deployment and makes reproducibility easier (the same commands as in production). Remember to set OLLAMA_HOST to expose it.

#Troubleshooting

“ollama ps” displays CPU instead of GPU

Under WSL2, verify that “nvidia-smi” responds. If it does not, update the driver on the Windows side and do not install any Linux driver in the distro.

Very slow model loading

Your models are probably stored under /mnt/c. Move them into the Linux filesystem (~/.ollama) to restore performance.

“address already in use” on 11434

A native Ollama and a Ollama WSL2 instance run at the same time. Stop one, or change the other's port with OLLAMA_HOST=127.0.0.1:11435.

AMD GPU ignored

Card not listed in ROCm. Check compatibility, possibly try HSA_OVERRIDE_GFX_VERSION, or fall back to native Windows.

The Windows client does not connect to the WSL2 daemon

Use http://localhost:11434 (WSL2 redirection handles the bridge) and make sure only one daemon is listening on this port.

# Faire écouter l'Ollama WSL2 sur un autre port
OLLAMA_HOST=127.0.0.1:11435 ollama serve

# Puis interroger ce daemon précisément
curl http://localhost:11435/api/tags

#Go further

Once you have chosen your environment, these guides will help you get the most from it:

Install Ollama on Windows 11

The native installer's step-by-step guide, a companion to this comparison if you're choosing the Windows route.

Troubleshoot Ollama: GPU not detected, slowdowns, out-of-memory errors

For more on GPU failures, applicable both natively and under WSL2.

Choose your quantization (Q4, Q5, Q8, FP16)

To determine the right VRAM footprint for your card, regardless of the environment.