Run OpenCode Offline: Ollama Setup, Context Windows & Custom Models
This walkthrough shows how to run OpenCode fully offline by pairing it with Ollama and the Qwen 3.6 local model, covering install via Homebrew or npm, tuning Ollama's context window for load speed versus capacity, saving custom models with a pinned context size, and registering the model in OpenCode's JSON config so it appears automatically.
Darren Builds AI10 minTranscript found
Quick learning frame
Read this before watching.
Coding-agent workflow is the loop of inspect, plan, edit, verify, summarize, and route the next task to the right tool.
New playlist item from Darren Builds AI; queued for transcript-backed review, topic mapping, and a practical learning artifact.
Skill you build: The ability to set up and tune a local open source coding model (Ollama plus OpenCode), matching context window size to the task and machine, and wiring custom models into OpenCode's provider config.
Watch for the shift from claim to mechanism. The learning value is the point where the transcript reveals a repeatable action, tool boundary, context move, review habit, or artifact.
Concept diagram
Where this video fits.
01Inspect
02Plan
03Edit
04Verify
05Review
06Route
Deep lesson
Turn this video into working knowledge.
2,031 cleaned transcript words reviewed across 586 timed caption segments.
Thesis
Run OpenCode Offline: Ollama Setup, Context Windows & Custom Models teaches a practical codex + claude workflows move: This walkthrough shows how to run OpenCode fully offline by pairing it with Ollama and the Qwen 3.6 local model, covering install via Homebrew or npm, tuning Ollama's context window for load speed versus capacity, saving custom models with a pinned context size, and registering the model in OpenCode's JSON config so it appears automatically.
The goal is not to remember the video. The goal is to extract the operating principle, tie it to timestamped evidence, test how far the claim transfers, and make something reusable.
0:00
Ollama plus OpenCode setup
“Do you want to use an open source model on your local machine to work with open code where you can actually reliably code and actually perform tasks? And are you also looking for a few tips that...”
You need two installs, Ollama (direct download) and OpenCode (npm, Bun, Yarn, or Homebrew), then pull Qwen 3.6 with 'ollama run': it is a 24 GB model that runs fine on 64 GB unified memory and workable on 32 GB, while smaller options like the 27B variant or Qwen 3.5 9B tested unreliable, so stick with 3.6 for results. Install both tools, run 'ollama run qwen3.6' to pull the model, and verify both 'ollama' and 'opencode' launch from your terminal before doing anything else.
3:49
Context window tradeoffs
“uh uh, already there, which is great cuz now I can actually start using this. For example, it'll One thing to note, this model can take a while to load up with your open code session. All right,...”
Ollama's settings menu now lets you change the context window, but bigger windows (128K or 256K) make the model load much slower and can break machines that cannot handle them; Darren uses 32K for quick function work, had to bump to 64K when the default build agent ran out, and notes Qwen 3.6's full context is 256K per its Ollama model page. Pick one coding task, set the context window to the smallest size that fits it (for example 32K or 64K), run the task, and note when you actually hit the threshold before increasing.
7:38
Custom models and config
“with Darren's model latest. And this will always have this context set to 65K, which is really great. So, I if I want to upload this instead of config file for open code, I can do that. One...”
You can pin a context size permanently by running the model, using set parameter (for example 65K), and saving it under a new name that then appears in 'ollama list'; to make any Ollama model show up inside OpenCode without the 'ollama launch opencode' path, add an Ollama provider entry using the npm AI SDK OpenAI-compatible package and the model name to your .opencode.json config. Create your own named model with a pinned context size via set parameter and save, then add it to .opencode.json so it appears in OpenCode's model list on a plain launch.
01
Inspect
Start with this video's job: This walkthrough shows how to run OpenCode fully offline by pairing it with Ollama and the Qwen 3.6 local model, covering install via Homebrew or npm, tuning Ollama's context window for load speed versus capacity, saving custom models with a pinned context size, and registering the model in OpenCode's JSON config so it appears automatically. Treat "Inspect" as the outcome you are trying to make visible, not a topic label. Anchor it to 0:00, where the video says: “Do you want to use an open source model on your local machine to work with open code where you can actually reliably code and actually perform tasks? And are you also looking for a few tips that...”
02
Plan
Use "Plan" to locate the part of the codex + claude workflows workflow the video is demonstrating. Ask what changes in your real setup if this claim is true. Anchor it to 3:49, where the video says: “uh uh, already there, which is great cuz now I can actually start using this. For example, it'll One thing to note, this model can take a while to load up with your open code session. All right,...”
03
Edit
Turn "Edit" into the reusable artifact for this lesson: A routing matrix for when to use Codex, Claude, browser checks, or manual review. This is where watching becomes something you can inspect and reuse.
04
Verify
Use "Verify" as the application surface. Decide whether the idea touches a browser flow, a local file, a model choice, a source document, a UI, or a review step.
05
Review
Use "Review" to prove the lesson. The evidence should connect back to the video title, transcript anchors, and a concrete output, not a generic best-practice claim.
06
Route
Use "Route" to carry the idea forward: save the prompt, checklist, diagram, or operating rule that would make the next agent run better.
Example
Source-backed work packet
Convert the video into a scoped task that includes the transcript claim, target workflow, acceptance criteria, and proof. The output should be a routing matrix for when to use codex, claude, browser checks, or manual review..
Example
Claim vs. demo brief
Separate what the speaker claims, what the demo actually proves, and what still needs outside verification before you adopt the workflow.
Example
Teach-back module
Transform the lesson into a definition, a mechanism diagram, one misconception, one practice exercise, and a check-for-understanding question.
Do not learn it wrong
Treating the title as the lesson without checking what the transcript actually says.
Letting the prompt drift into generic advice that could apply to any video in the playlist.
Copying the tool setup without identifying the operating principle that transfers to your own stack.
Skipping the artifact, which means the learning never becomes operational or inspectable.
Do not count this as learned until these are true.
01
State the transcript-backed claim in your own words: This walkthrough shows how to run OpenCode fully offline by pairing it with Ollama and the Qwen 3.6 local model, covering install via Homebrew or npm, tuning Ollama's context window for load speed versus capacity, saving custom models with a pinned context size, and registering the model in OpenCode's JSON config so it appears automatically.
02
Explain the practical stakes without hype: New playlist item from Darren Builds AI; queued for transcript-backed review, topic mapping, and a practical learning artifact.
03
Map the idea onto the Inspect -> Plan -> Edit -> Verify -> Review -> Route sequence and name the weakest link.
04
Produce the artifact and include the evidence that proves it: A routing matrix for when to use Codex, Claude, browser checks, or manual review.
Put it into practice
Give this grounded prompt to Codex or Claude after watching.
You are helping me turn one specific YouTube video into real, durable learning.
Source video:
- Title: Run OpenCode Offline: Ollama Setup, Context Windows & Custom Models
- URL: https://www.youtube.com/watch?v=uBmF5I_4gWg
- Topic: Codex + Claude Workflows
- My current learning frame: Set up Ollama with Qwen 3.6 and OpenCode on your machine, build a small Next.js landing page at a 32K context window, then bump to 64K and register a custom-named model in .opencode.json, comparing load time and how much guidance the local model needs versus a hosted one.
- Why this matters: New playlist item from Darren Builds AI; queued for transcript-backed review, topic mapping, and a practical learning artifact.
Transcript anchors from this exact video:
- 0:00 / Evidence 1: "Do you want to use an open source model on your local machine to work with open code where you can actually reliably code and actually perform tasks? And are you also looking for a few tips that..."
- 1:32 / Evidence 2: "going to be looking at running a local model from Ollama. And what we're going to be doing is we're actually going to be using the Qwen 3.6 model. So, if you just go Qwen 3.6 in Ollama..."
- 3:49 / Evidence 3: "uh uh, already there, which is great cuz now I can actually start using this. For example, it'll One thing to note, this model can take a while to load up with your open code session. All right,..."
- 5:38 / Evidence 4: "can always increase the performance and then, sorry, the context window, and it'll just take a bit of time to load up, but at least you can use it that way. This is the site that the local..."
- 7:38 / Evidence 5: "with Darren's model latest. And this will always have this context set to 65K, which is really great. So, I if I want to upload this instead of config file for open code, I can do that. One..."
- 9:39 / Evidence 6: "local models. Also, you're going to probably have to have some custom agents really trim it down and have uh system prompts that actually make it a lot better. Anyway, I hope you guys really enjoyed this video..."
Your task:
1. Use the transcript anchors above as the primary source packet. If you add outside context, label it clearly as outside context and keep it secondary.
2. Create a source-check table with columns: timestamp, claim, what the demo proves, confidence, and what still needs verification.
3. Extract the actual teachable claims from the video. Do not invent claims that are not supported by the title, lesson frame, or transcript anchors.
4. Build a reusable learning artifact: A routing matrix for when to use Codex, Claude, browser checks, or manual review.
5. Include:
- a plain-English definition of the core idea
- a diagram or structured model using this sequence: Inspect -> Plan -> Edit -> Verify -> Review -> Route
- 3 concrete examples that apply the video idea to real agentic work
- 2 failure modes the video helps prevent
- a checklist I can use the next time I run Codex or Claude
- one practical exercise with a clear done signal
6. Add a "learning transfer" section: what changes in my workflow tomorrow if I actually learned this?
7. Add a "source check" section that cites which transcript anchor supports each major takeaway.
Quality bar:
- Make this specific to "Run OpenCode Offline: Ollama Setup, Context Windows & Custom Models", not a generic Codex + Claude Workflows essay.
- Prefer operational examples, failure modes, and reusable artifacts over broad definitions.
- Call out uncertainty instead of smoothing over weak evidence.
- If evidence is weak, say what transcript segment or timestamp needs review instead of guessing.
- Finish with a concise artifact I could paste into my learning app.
Misconceptions
What to stop believing.
One agent should do every task.
Different tools have different strengths. Routing is part of the workflow.
More context is always better.
Relevant context helps; stale context causes drift and cost.
Practice studio
Learning only counts when you make something.
01
Transcript evidence map
Separate what the video actually says from what you already believe about the topic.
3 source-backed takeaways with timestamps, confidence, and a transfer note.02
One useful artifact
Apply the video to a real workflow and produce a routing matrix for when to use codex, claude, browser checks, or manual review..
A reusable artifact with a done signal and one verification step.03
Teach-back card
Explain the lesson to someone who has not watched the video yet.
A 90-second explanation, one diagram, one example, and one misconception to avoid.
Recall check
Answer first, then reveal — without rewatching.
Which local model does the video recommend for reliable coding with OpenCode, and why not smaller ones?
What tradeoff comes with increasing Ollama's context window setting?
Why might a Qwen model not appear in OpenCode's model list, and how do you fix it?
Source shelf
Use the video as a doorway, then verify with primary sources.
