Workflows/Code with Qwen3.8-Flash-Next in VS Code + Cline

Coding / WORKFLOW

Code with Qwen3.8-Flash-Next in VS Code + Cline

Use the base Qwen3.8-Flash-Next checkpoint with VS Code and Cline, review a real code change, and test it. This is separate from the Strata derivative.

Verified sourceSource checked 10/14/2026

LOCALRENTED GPU

Freshness 82 out of 100. Repository age: 52 days old, Aug 24, 2026. Measures age, not quality.

Popularity 88 out of 100. 2,290,500 downloads in 30 days; 6,235 lifetime likes. Checked Oct 15, 2026. Measures adoption, not quality.

What do these scores mean?

Two ways to compare models. Neither score measures answer quality, benchmark performance, or whether a model fits your machine.

Freshness · release age

A new release starts at 100. The score halves every 180 days: 100 × 2^(−age in days / 180). When the release date is unavailable, a repository date is a fallback—not proof of when the model was released. Missing dates stay unscored.

Check the date source

Popularity · adoption

Downloads over the past 30 days carry 80% of the score; lifetime likes carry 20%. Both use a logarithmic scale, capped at 10 million downloads and 100,000 likes. If likes are unavailable, downloads carry all the weight. Missing download statistics stay unscored.

round(80 × min(1, log10(1 + downloads) / 7) + 20 × min(1, log10(1 + likes) / 5))

Check the statistics source

Scores as of Oct 15, 2026. Statistics checked Oct 15, 2026. Statistics are marked for refresh after 72 hours.Full scoring method

Before you begin

Hardware

Full package footprint matters more than six active parameters.

Machine	Qwen3.8-Flash-Next · 125B MLX	Qwen3.8-Flash-Next · 125B Q4
NVIDIA GPU · 32 GB VRAM	No MLX NVIDIA route; use the Linux package on a larger server.	Below the 120 GB weight file; use a multi-GPU host.
NVIDIA GPUs · 160 GB aggregate VRAM	No MLX NVIDIA route; choose Q4 Linux package.	Capacity candidate; confirm Ollama distributes the model and leaves context headroom.
AMD GPUs · 160 GB aggregate VRAM	No MLX AMD route; choose a supported Linux GPU runtime.	Ollama backend compatibility and distribution need verification for these exact cards.
Intel GPUs · 160 GB aggregate VRAM	No MLX Intel route; choose the supported Mac package.	No verified multi-Arc distribution route for this package.
Apple Silicon Mac · 128 GB unified memory	Weight file is 105 GB; runtime headroom is tight and this is not a recommended start.	Q4 is the Linux route here; choose the MLX package on Mac.
Apple Silicon Mac · 192 GB unified memory	Capacity candidate with headroom for 8K context; measure memory pressure.	Choose the MLX package instead of Linux Q4 on Mac.
CPU only · 192 GB RAM	No MLX CPU route here; use a supported high-memory Mac.	May load through offload but agent steps are not a practical recommendation.
RunPod · NVIDIA GPUs · 160 GB aggregate VRAM	No MLX rental route; choose Q4 Linux.	Capacity candidate; validate model distribution and runtime before a long rental.

Package sizes are published; fit and speed ratings are estimates. Multi-GPU distribution must be confirmed on the chosen rental.

PRIVATE CODING · BASE QWEN MODELSOURCE-LINKED SETUP

Use Qwen3.8-Flash-Next in VS Code.

Choose the official Mac or Linux package, connect its private Ollama endpoint to Cline, then review and test a code change. This guide does not use Strata.

Choose a package Run its commands Check the result

BEFORE YOU BEGIN

Choose your package

Select the weights you want. The instructions, downloads and settings change with your choice.

Save your machine in My Hardware to get an automatic starting choice. You can always choose any package yourself.

CURRENT SETUP

Qwen3.8-Flash-Next · 125B MLX · official

Start at 8,192 tokens on 192 GB unified memory; the 105 GB file is only weights. Longer context consumes more memory.

Freshness unavailable. Date pending.

Popularity unavailable. Stats pending.

What do these scores mean?

Two ways to compare models. Neither score measures answer quality, benchmark performance, or whether a model fits your machine.

Freshness · release age

Check the date source

Popularity · adoption

round(80 × min(1, log10(1 + downloads) / 7) + 20 × min(1, log10(1 + likes) / 5))

Scores as of Oct 15, 2026. Statistics check pending. Statistics are marked for refresh after 72 hours. These statistics are due for a refresh.Full scoring method

View weight source

THE WORKFLOW

Set up Qwen3.8-Flash-Next · 125B MLX on your machine

Choose where you want to run it. The instructions below update to match your selection.

CHOOSE YOUR SETUPMac

Other operating systems

Windows: This package is MLX for Apple Silicon, not a Windows binary. Windows can use the Q4 package as a client to Linux instead. Compatibility source

Linux: This exact package is MLX for Apple Silicon; the separate official Q4 package is the Linux multi-GPU route. Compatibility source

Check the machine before downloading

Check 192 GB unified memory free for the full model, runtime and a short context. This is a conservative capacity starting point, not a measured speed claim.

Exact Ollama package

Install and start Ollama

Download Ollama for macOS and open it. In Terminal, confirm the app is running before the model download. Ollama manages the package files, so no manual model folder tree is needed.

Download Ollama for this operating system Official model package

Download the exact package and set a short context

In Terminal, pull qwen3.8-flash-next:125b-mlx. Start with 8,192 tokens when connecting the agent; the published maximum context is not a safe default for every machine. The download is large and can take time.

Package identity and disk size

Bash

ollama pull qwen3.8-flash-next:125b-mlx
ollama show qwen3.8-flash-next:125b-mlx

Install VS Code and Cline

On your own Windows, Mac or Linux computer, install VS Code and the Cline extension. Keep approval required for file writes and terminal commands.

Download VS Code Install Cline extension

Connect the exact Qwen package

In Cline choose the OpenAI-compatible provider, Base URL http://127.0.0.1:11434/v1, and exact Ollama Model ID qwen3.8-flash-next:125b-mlx. Start at 8,192 context. Keep Ollama running locally. Confirm a short response before allowing repository access.

Cline custom model settings

Make a reviewed code change

Open a disposable project in VS Code. Ask Cline to add a small button and a test. Review the proposed diff and any Terminal command before approval, run the test, and open the app to confirm the button. If this exact large package fails at tool calls, do not present the route as a verified coding assistant.

Inspect the result and stop compute

Keep the reviewed code change or discard the disposable project. Check the actual test result and app behavior, then stop the model server or rented GPU when done.

SUCCESS CHECK · Qwen3.8-Flash-Next · 125B MLX

A reviewed, tested code change

Cline edited the disposable repository with your approval; the diff is relevant, the test passes, and the changed behavior works. A chat response or file-reading probe does not pass.

The agent route is source-backed but has not been benchmarked by YouRunAI. The Linux multi-GPU fit is a capacity estimate, not a tested deployment.

Back to setup steps

When it doesn’t go to plan

The full package does not fit or answers slowly.

Use the exact package size, leave runtime and context headroom, begin at 8K–16K context, and choose a larger machine or a smaller model if it still offloads heavily.

The agent returns text but no tool call.

Check the selected Ollama package has tool support, update Ollama, then retry the bounded script. Do not promote plain chat to a working agent path.

The client cannot connect to the local server.

Keep Ollama running, check http://127.0.0.1:11434/v1, and use an SSH tunnel for a rental. Never expose the unauthenticated port publicly.

REFERENCE LIBRARY

Sources and files

Original instructions, model files, and compatibility notes behind this setup.

4 SOURCES

01Official Ollama package sizes and tool supportollama.com 02Qwen publisher modelhuggingface.co 03Hermes custom endpoint setuphermes-agent.nousresearch.com 04Cline OpenAI-compatible providerdocs.cline.bot

The model behind this workflow

Qwen3.8-Flash-Next

Keep exploring

Text

SETUP GUIDE

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SOFTWAREComfyUI

LOCALRENTED GPU

Verified source