What Devstral 2 is
Devstral 2 is Mistral's full-scale agentic coding model for software engineering tasks. It excels at using tools to explore codebases, editing multiple files, and powering software engineering agents that need deep project context. Ollama lists text input, tool support, and a 256K context window on the cloud tag.
On Ollama today, the Terminal Glass cloud tag is
devstral-2:123b-cloud (high usage tier).
A local devstral-2:123b build is also
available (~75 GB) for teams with the GPU memory and storage to host it on
NoCloudGPT Private Instances.
Deploy cloud access with clear rules about what repositories may leave your network.
The Terminal Glass trade-off
Terminal Glass gives you the same OpenWebUI chat experience and locally stored RAG index you would use on a private deployment — but routes each inference request to Ollama Cloud. Your server runs the Ollama client, OpenWebUI, and your document index; a small cloud VM, NUC, or mini-PC is usually enough. You do not need to provision multi-GPU servers or ~75 GB of model storage to start evaluating a full-scale coding agent.
Honest reality: Prompts, source-code snippets, repository context, RAG passages, and tool outputs leave your infrastructure and are processed on Ollama Cloud. This is not air-gapped and is not equivalent to NoCloudGPT Private Instances, where inference stays on hardware you control. Devstral 2 cloud is rated high usage on Ollama — expect higher per-token cost than compact coding models. You also accept network-dependent latency and less control over strict reproducibility. When sensitive or regulated material must never leave your network, choose a NoCloudGPT Air Gapped deployment instead.
Source: Wikimedia Commons
What about the video card?
This is the question most buyers are actually trying to answer.
Running these models without a proper GPU usually means falling back to CPU only. This works for very light use, but it becomes extremely slow for normal workloads. Many people discover this the hard way after spinning up a cloud server that ends up being too expensive or too slow.
Some teams want to avoid dealing with GPUs altogether. For them, Terminal Glass Cloud is usually the simpler option — you get capable models through a familiar OpenWebUI interface without having to select, size, or manage video cards.
Other teams prefer to run on infrastructure they control. In that case, they typically choose a GPU-based instance (on AWS Lightsail, Digital Ocean, or their own servers) and install NoCloudGPT on it. Because these models are designed to run with local Ollama, you become responsible for selecting and managing the GPU.
Some organizations need everything to stay fully inside their own environment from day one. For those teams, a NoCloudGPT Air Gapped deployment on their own hardware is the right approach.
There isn’t a single right answer. The right answer depends on your constraints around cost, privacy, compliance, latency, and long-term ownership.
Who This Model Family is For
👤 Personal Use
Useful for experienced developers who want a stronger coding agent for large refactors, cross-repo exploration, and complex debugging without buying 123B-class hardware first. Every prompt and code excerpt is sent to Ollama Cloud — do not paste private keys, credentials, client code, or proprietary snippets. Choose NoCloudGPT Private Instances if keeping data fully private is your priority.
🏢 Business / Team Use
A good fit for engineering teams evaluating agentic coding workflows before committing to owned inference hardware — internal tooling, onboarding, ticket triage, code review preparation, and test planning when your data policy permits cloud processing. Use NoCloudGPT Private Instances when repositories contain confidential IP, regulated data, or customer-specific code.
⚙️ Developer & Technical Use
Strongest for demanding agentic coding: deep file-tree exploration, multi-file edits, tool-assisted terminal loops, and complex software-engineering agents. It outperforms Devstral Small 2 on benchmark tasks but still needs human review, tests, and rollback discipline before merging generated changes.
🔬 Data Science & Research Use
Practical for public-code analysis, benchmark harness design, experiment automation, and pipeline review on non-sensitive material. Cloud inference does not pin model versions the way a local checkpoint does. Confidential, unpublished, or regulated datasets should stay on fully local NoCloudGPT Private Instances.
How Terminal Glass Works with This Model
You interact through OpenWebUI on your Terminal Glass host
(or via the command line). Chat history, user accounts, and your locally built RAG document index
stay on hardware you control. When you submit a prompt to
devstral-2:123b-cloud,
the request — including code excerpts and retrieved passages — is forwarded to Ollama Cloud;
the response streams back to your browser.
Cloud access requires an Ollama account (ollama signin)
on the Terminal Glass machine. Devstral 2 cloud is rated high usage on Ollama — budget accordingly for
long 256K contexts and frequent agentic tool loops.
ollama signin
ollama run devstral-2:123b-cloudWhat leaves your infrastructure: prompt text, code snippets, RAG-retrieved passages, and tool context. Latency includes network round-trips. Cost is usage-based at the high-usage tier.5 text-cyan-200">devstral-2:123b on NoCloudGPT Private Instances when private inference becomes the better fit.
Honest Guidance: When to Choose a Different Path
- Regulated, confidential, or strict data-residency requirements — Choose NoCloudGPT Private Instances with a local Devstral build. Terminal Glass routes prompts and code to Ollama Cloud.
-
Steady daily team usage on capable hardware —
Local
devstral-2:123bon owned multi-GPU hardware may cost less over time than recurring high-usage cloud tokens. - Lighter coding workloads or tighter budgets — Devstral Small 2 Cloud offers a 24B coding agent at a lower Ollama usage tier when full 123B capacity is not required.
- Unreviewed automation — Multi-file edits, generated patches, and terminal-agent actions need human review, tests, and rollback plans regardless of which model you use.
- Model-version pinning or audit-grade reproducibility — A pinned local deployment on NoCloudGPT Private Instances provides more control than a cloud-only tag.
Recommended Terminal Glass Deployment
| Variant / Tag | Recommended For | Notes | |
|---|---|---|---|
| devstral-2:123b-cloud | Teams evaluating full-scale coding agents | 123B model, 256K cloud context, text input, tools, high usage tier. Only verified Devstral 2 cloud tag on Ollama. Deep codebase exploration, multi-file editing, and tool-assisted software-engineering agents when content may leave your network. | Get quote → |
Local tag devstral-2:123b (~75 GB)
is also available on Ollama for fully local deployment when you have the hardware. Confirm availability at
ollama.com/library/devstral-2
before deployment.
Licensing & Access
Devstral 2 is released under a Modified MIT license per the Ollama model card. Organizations with global consolidated monthly revenue exceeding $20 million may need a commercial license from Mistral AI for local weight use — review the model card before production deployment. Cloud access requires an Ollama Cloud account with usage-based pricing; rate limits and tiers apply and may change. Ollama Cloud usage is separately governed by Ollama's terms.
Official catalog: ollama.com/library/devstral-2
What Terminal Glass adds
Terminal Glass packages a unified OpenWebUI interface, locally stored private RAG over your documentation, multi-user access controls, and guidance on monitoring cloud usage so spend stays predictable.
We help you size the local host and choose the right cloud variant for your team.