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Yes—Cloudflare OS can run against a local Ollama model, but getting a useful app may require more than connecting an endpoint. In an August 2026 hands-on test of early-access Cloudflare OS v2 on Windows 11, akari.log used Ollama with Qwen3 models and eventually produced a playable tic-tac-toe app. The process took about 90 minutes, five rounds and roughly 16,000 tokens. The failures exposed practical limits that matter more than the simple “local versus cloud” choice: desktop memory overhead, context-window pressure, Gadget’s sandbox-specific RPC interface and the absence of an observed repeated-error stop condition.
Can you run Cloudflare OS locally with Ollama?
In the reported setup, yes. The author selected the Ollama model connection and supplied a local API URL, allowing Cloudflare OS early-access v2 to use a model running on the same Windows computer. That result is a documented experience with one software version, one task and one machine—not a compatibility guarantee for every release, operating system or model.
The complete account is akari.log’s August 2026 report. It is a first-person technical account rather than a controlled benchmark or an official statement from Cloudflare or Ollama.
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| Component | Reported value | Qualification |
|---|---|---|
| Operating system | Windows 11 | Specific to the author’s computer |
| Node.js | 24.13.0 | Version used in the August 2026 run |
| Package manager | pnpm 11.15.1 | Version used in the run |
| Graphics card | RTX 5060 with 8 GB VRAM | Not a recommendation or a comparative result |
| System memory | 32 GB RAM | Background applications reduced memory available to the model |
| Cloudflare OS | Early access v2 | Observed in August 2026; later builds may differ |
| Models | qwen3:8b, then qwen3:30b-a3b |
Ollama models used for this task |
Local inference avoided API usage charges in that setup, but it was not cost-free: the computer, graphics card, electricity and time still had to be supplied. The test also illustrates why headline specifications can mislead. Before loading a model, the author measured about 6,759 MiB of free VRAM in the active desktop session, less than the card’s nominal capacity.
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What must be in place before connecting Cloudflare OS?
A reproducible local run depends on four layers working together: the host environment, the Ollama model server, the agent’s context budget and Cloudflare OS’s Gadget execution conventions.
- Host tools: The reported machine had Windows 11, Node.js 24.13.0 and pnpm 11.15.1. Use the versions required by the Cloudflare OS build you actually install; early-access requirements can change.
- Available memory: Count free VRAM and system RAM after Windows and other applications start, not only the figures printed on hardware boxes.
- Ollama endpoint: The author used Cloudflare OS’s Ollama connection option and a local API URL. The exact endpoint and interface labels should be checked against the current release.
- Model choice: The run began with
qwen3:8band later usedqwen3:30b-a3b. A larger model can change speed, memory use and how well the agent follows environment-specific instructions. - Gadget-compatible application design: The generated app needed the injected
gadgetclient interface and a server class namedGadget, rather than an ordinary browser-to-server request.
These are dependencies, not a universal minimum specification. The account does not establish a minimum VRAM requirement, a supported-model list or a current Cloudflare OS compatibility matrix.
How do you connect a local Ollama model to Cloudflare OS?
The tested workflow was straightforward at the connection level, but the surrounding configuration determined whether the agent could finish the job.
- Prepare the host. Install the Node.js and pnpm versions required by the Cloudflare OS build, then close memory-heavy applications before loading the model. Record free VRAM and RAM so later failures can be separated from application errors.
- Make the model available in Ollama. The author used
qwen3:8band subsequentlyqwen3:30b-a3b. Load the model through the normal Ollama workflow for your installation and verify that its local service responds before opening the agent. - Select the Ollama connection in Cloudflare OS. Use the model-connection option for Ollama and enter the local API URL exposed by your Ollama service. Do not assume that a cloud-provider URL or a browser-side request is interchangeable with this setting.
- Set a context budget deliberately. The reported run used a 4,096-token context initially. If the agent begins forgetting tool definitions or system instructions, test a larger value such as the author’s 16,384-token experiment only after checking available memory.
- Describe the Gadget boundary in the task. Tell the agent that the client runs inside the Gadget environment and that server functionality must be exposed through the expected Gadget RPC pattern. Supplying a concise server skeleton can prevent the model from reverting to conventional HTTP code.
- Watch each turn. Stop and inspect the run when the same error repeats. The tested flow did not show an automatic repeated-failure check, so allowing an agent to continue can consume context, time and compute without changing the result.
Because Cloudflare OS was early access in the report, treat menu names and integration details as version-sensitive. The procedure describes the author’s path, not a promise that current releases use identical labels.
Why did the tic-tac-toe app need five attempts?
The failures were not five unrelated coding bugs. They followed a progression from misunderstanding the execution environment to using the correct client/server contract.
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| Attempt | What the model did | Why it failed | What changed next |
|---|---|---|---|
| 1 | Treated a server-side binding as though it were a browser global. | The client and server contexts were confused. | The author let the next turn clarify the runtime boundary. |
| 2 | Issued a direct HTTP request from the client. | The Gadget iframe’s reported Content Security Policy used connect-src 'none', blocking that request. |
The model tried to diagnose the browser restriction. |
| 3 | Interpreted the CSP failure as a CORS problem and repeatedly changed headers. | Header changes could not solve a request prohibited by the iframe policy. | The author stopped the attempt manually. |
| 4 | Switched to the injected gadget RPC interface. |
The server class name did not match the required Gadget class. |
The author supplied a more explicit server structure. |
| 5 | Used a Durable Object skeleton with methods such as getGameState() and makeMove(index). |
The expected RPC shape was finally present. | The tic-tac-toe app became playable through the Gadget interface. |
The sequence shows why a local model can appear capable yet struggle with a specialized sandbox. It knew common browser and HTTP patterns, but those patterns were wrong for the Gadget iframe. The successful turn did not come from endlessly tuning CORS; it came from giving the model the environment’s actual abstraction.
What is the Gadget RPC pattern, and why did ordinary fetch fail?
In the author’s test, the browser client was not free to call an arbitrary server URL. The iframe policy blocked the direct request, while the environment supplied an injected gadget interface intended for RPC-style calls.
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This distinction matters for any generated application, not just a game. When a model sees a familiar web task, it may automatically create REST endpoints, add CORS headers or call a localhost URL. In a sandbox that controls network access, those fixes address the wrong layer. State the available RPC object, method names and class conventions in the prompt or starter code so the model has fewer opportunities to substitute a conventional architecture.
Was the working game persistent?
No. The successful example kept game state in memory, so a server restart would lose the board. The author points to Durable Object storage guidance for applications that need state to survive restarts. In-memory state is adequate for a short demonstration but should be treated as an explicit limitation when evaluating the result.
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What context length should you use for a local coding agent?
The report connects the initial 4,096-token context with tool and instruction loss, then describes better behavior after increasing the setting to 16,384 tokens. That is an observed diagnosis, not proof that Ollama always drops instructions at 4,096 tokens or that 16,384 is optimal for every model and computer.
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Context is not free memory. Increasing it enlarges the key-value cache and can push work from the GPU to the CPU. The author reports that, for Qwen3 8B, model memory use rose from 5.2 GB at 4,096 tokens to 7.8 GB at 16,384 tokens. On an 8 GB card, that left little headroom after desktop overhead.
Use symptoms, not a fixed number, to choose a setting
- Stay near the current setting when short tasks complete and the model retains tool names and parameters.
- Test a larger context when the agent forgets its runtime contract, repeats already-resolved mistakes or truncates turns.
- After increasing context, check free VRAM, system-memory pressure and whether inference has shifted to the CPU.
- If a larger context makes generation unusably slow, shorten the task, remove unnecessary tool history or use a model that fits the available memory rather than assuming more tokens will solve every error.
The environment variable used for the reported experiment was OLLAMA_CONTEXT_LENGTH. Set it through the supported configuration method for your operating system and Ollama version, then restart the service so the new value is actually applied.
What performance and memory results did the author observe?
The following figures belong to one desktop session and should not be read as general Ollama benchmarks.
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| Measurement | Reported observation | How to interpret it |
|---|---|---|
| Free VRAM before model loading | About 6,759 MiB | Desktop and background applications consumed part of the nominal 8 GB |
| Qwen3 30B-A3B generation speed | About 28 tokens per second | Hybrid GPU/CPU run reported by the author; speed depends on model, context and system load |
| Qwen3 8B memory at 4,096 context | About 5.2 GB | Observed model-memory use in that setup |
| Qwen3 8B memory at 16,384 context | About 7.8 GB | Higher context increased memory and caused more CPU/GPU hybrid execution |
| Complete tic-tac-toe task | Five rounds, roughly 16,000 tokens and about 90 minutes | End-to-end effort for one app, not a productivity benchmark |
Ollama’s context behavior can vary with available VRAM, according to the account, but the author did not independently verify current defaults. Check the version-specific documentation or runtime logs on your own machine before planning capacity around any default.
Why might an agent keep retrying the same mistake?
The author inspected packages/workshop-backend/src/agent.ts in the tested version and found a shouldStopAfterTurn condition covering a human abort, a 30-turn limit, a pending external connection and a pending state-changing-action decision. The inspection did not reveal a repeated-failure check.
The same account says giveUp was registered only for callback-initiated work. In the standard chat path examined, a model could therefore continue trying until a person intervened or the turn limit was reached. This is a code-reading result for that version, not a claim about the current repository.
How to make long retries safer
- Track the last several tool errors outside the model conversation.
- Stop when the same error, failed file change or invalid parameter recurs without progress.
- Preserve the failing request and environment details for a human handoff instead of deleting the history.
- Give the model a bounded number of repair attempts for one class of error, then require a new plan.
- Keep a turn limit even when the model appears to be making progress; a large context can hide repetition rather than eliminate it.
These safeguards are design recommendations derived from the author’s analysis. They are not described as built-in features of the tested Cloudflare OS flow.
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How should you troubleshoot a local Cloudflare OS run?
Match the symptom to the layer that owns it instead of changing unrelated settings.
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- “The browser request is blocked.” Inspect the Gadget iframe policy first. If it reports
connect-src 'none', stop adding CORS headers and move the client call to the injectedgadgetRPC interface. - “The RPC object exists but methods fail.” Compare the server class and exported method names with the conventions expected by the installed Cloudflare OS version. The reported run required a class named
Gadget. - “The model forgets tools halfway through.” Check context shifting and the effective context length. A larger setting may help, but verify memory use immediately afterward.
- “Generation becomes slow after increasing context.” Look for CPU/GPU hybrid execution and reduced free VRAM. Lower the context, close other applications or shorten the task if the larger cache overwhelms the machine.
- “The agent repeats a fix.” Stop it manually and preserve the logs. The inspected version did not expose a standard repeated-failure stop check.
- “The game resets after restart.” Treat that as expected for in-memory state. Use the platform’s Durable Object storage design when persistence is a requirement.
What should you compare when choosing a local agent setup?
The report does not compare products, but it identifies the dimensions that determine whether a local coding agent will finish work reliably.
| Decision axis | Question to answer | Why it matters |
|---|---|---|
| Effective memory | How much VRAM and RAM remain after the desktop and tools are running? | Available capacity, not the headline specification, determines whether the model and context fit. |
| Context versus cache | Can the chosen context retain system instructions and tools without exhausting memory? | A larger context may reduce forgetting while forcing more CPU work. |
| Inference placement | How much of the model runs on the GPU versus the CPU? | Hybrid execution can change response speed and the usefulness of interactive repair. |
| Environment compatibility | Does the model understand the sandbox’s RPC, class and tool conventions? | General web knowledge is not enough when direct requests are prohibited. |
| Failure handling | What stops repeated errors and how is a person notified? | Without a handoff path, an agent can spend turns repeating an ineffective repair. |
For this reason, a smaller model with enough context and clear Gadget scaffolding may be more practical for a constrained task than a larger model that leaves no memory headroom. The five-round account cannot establish which choice is best in general; it shows why these variables must be measured together.
Is running Cloudflare OS locally worth it?
Local execution is worthwhile when avoiding per-request usage charges, keeping the model on your own machine or experimenting with the integration itself matters more than turnkey reliability. It is less attractive when you need predictable latency, a large context without careful memory tuning or an agent that automatically recognizes repeated failure.
- Advantages in the reported scenario: no API usage charge for the local inference path, direct control over the model, and a working Cloudflare OS/Gadget experiment without relying on a hosted model endpoint.
- Costs and limits: hardware and electricity remain real costs; desktop applications reduce available VRAM; larger context can trigger CPU/GPU hybrid execution; and the agent may need human intervention.
- Engineering lesson: provide the platform’s actual RPC skeleton and persistence design early instead of asking a general-purpose model to infer them from common web patterns.
The safest conclusion is narrow: akari.log demonstrated that the integration can work locally and documented why one small app took repeated repairs. It did not demonstrate universal model quality, current-version compatibility or a hardware buying recommendation.
Frequently Asked Questions
Does local Ollama inference make Cloudflare OS free to use?
It can avoid API usage charges for the model calls, but the computer, graphics card, electricity and setup time still have costs. The reported run should therefore be described as avoiding metered inference charges, not as having zero cost.
How can I tell whether a context problem is causing an agent failure?
Look for context-shifting messages in the Ollama log together with forgotten tool names, lost bindings, invalid parameters or truncated turns. Change one variable at a time and record memory use, because a larger context can improve retention while making the model spill more work onto the CPU.
What should I log when reproducing this experiment on another computer?
Record the operating-system version, Cloudflare OS build, Ollama model tag, context setting, free VRAM before loading, generation speed, turn count and the exact Gadget error. Those details distinguish a runtime-contract problem from a memory or context problem.
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