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Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →An on-premises AI coding agent needs a development environment to run the agent and its sandbox; if the language model also runs on-premises, it needs a separate inference service sized for that model and workload. The agent application can have modest requirements, but those do not tell you how much memory or compute the model server needs. Size the two layers separately, then confirm that the agent can securely reach the model endpoint.
Separate the agent host from the model server
Think of the deployment as two connected components. The agent application and sandbox handle repositories, tools, and commands. The inference service loads and serves the language model. They can run on one machine or on separate hosts, but their requirements are not interchangeable.
OpenHands recommends a modern processor and at least 4 GB of RAM for its local application setup, which supports Linux, macOS with Docker Desktop, and Windows with WSL and Docker Desktop. That is an application recommendation, not a specification for local model inference, builds, tests, browser or tool processes, or multiple sandboxes. See the OpenHands local setup documentation.
The sandbox also needs access to the repositories and commands the agent is permitted to use; OpenHands’ setup documentation describes mounting local code into the sandbox. Beyond that, CPU, memory, storage, and isolation needs depend on the repository, build and test workload, number of concurrent jobs, and security policy. There is no universal parts list established for every coding-agent product.
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Size local inference for a specific model
Model capacity depends on the model and quantization, context length, runtime overhead, latency target, and number of simultaneous requests. A memory threshold for loading a model is not, by itself, a promise of fast responses or multi-user throughput.
OpenHands’ Qwen3.6-35B-A3B example
In a recommendation note dated May 21, 2026, OpenHands’ local LLM guide gives a model-specific starting point for quantized Qwen3.6-35B-A3B: a recent GPU with at least 24 GB of VRAM, or Apple Silicon with at least 64 GB of unified memory. The guide recommends a context length of at least 22,000 for lower-VRAM systems, or 32,768 for better performance in the described configuration, and says to enable Flash Attention. These are guide settings for that configuration, not universal minimums or performance guarantees. Consult the OpenHands local LLM guide.
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Keep older model examples separate
OpenHands’ March 31, 2025 announcement said its distinct OpenHands LM 32B model could run locally on hardware such as a single RTX 3090. That historical example concerns a different model and should not be used as a substitute for sizing the Qwen3.6-35B-A3B configuration. The same announcement reported a 37.2% resolve rate on SWE-Bench Verified for OpenHands LM 32B; that is the publisher’s reported benchmark result, not an independent test or a measure of hardware throughput. See the OpenHands announcement.
Check the serving runtime and accelerator compatibility
The inference framework narrows which operating systems, language runtimes, accelerators, and deployment configurations are supported. For example, vLLM’s current stable GPU installation guide lists Linux and Python 3.10–3.13. It lists NVIDIA GPUs with compute capability 7.5 or newer, specified AMD GPU families subject to ROCm qualifications, and supported Intel data-center or Arc GPUs. Check the current vLLM GPU installation guide for the exact hardware and platform conditions before choosing a server.
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Apple Silicon is a separate path in that guide: vLLM points users to a community-maintained vLLM-Metal plugin rather than presenting it as ordinary vLLM GPU support. Containerized vLLM also needs host shared memory; the guide gives ipc=host or an explicit shared-memory allocation as options, particularly for tensor-parallel inference.
Make the model endpoint reachable without exposing it unnecessarily
The agent must be able to connect to the model server’s base URL. A specific pitfall documented by OpenHands involves Docker on Linux and LM Studio: LM Studio listens on 127.0.0.1 by default, and in that described arrangement the OpenHands Docker container cannot reach the host-local address. This is a configuration issue, not a general rule that containers cannot access host services.
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Choose and test the endpoint configuration for your deployment, and decide how authentication and firewall rules limit access. The cited setup guidance does not establish a general production network design or recommend exposing an unauthenticated model API.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Plan a shared server from the workload, not one memory figure
The available model examples do not establish a workload-independent sizing formula for multiple developers. Before buying or assigning a shared inference server, define the conditions it must meet:
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- Model and quantization: Specify the exact model and quantized variant to be served.
- Context: Set the context length the agent needs for repository and tool use.
- Concurrency: Estimate simultaneous generations and whether users share one model process or use isolated instances.
- Latency: Set acceptable first-token and completion times.
- Competing work: Decide whether inference will share resources with builds, tests, or other services.
- Operations: Check accelerator count, power and cooling, chassis constraints, maintainability, and vendor support for the intended host.
Benchmark the exact deployment under those conditions. A single-user model-loading recommendation does not establish concurrent capacity or response speed.
Quick Recap
Compare deployment options on the constraints that matter
| Decision area | What to compare |
|---|---|
| Model capacity | Usable GPU VRAM or unified memory for the chosen model and quantization, including room for context and runtime overhead. |
| Context and workload | Context length, repository size, tool-call pattern, and build or test activity. |
| Concurrency and latency | Simultaneous requests, acceptable response times, and serving-framework behavior under load. |
| Compatibility | Supported operating system, Python or other runtime, accelerator family, drivers, and model-server interface. |
| Operational separation | Whether agent, sandbox, and inference endpoint share one host or run across machines, and how the endpoint is reached and secured. |
| Expansion and support | Accelerator expansion, power and cooling, chassis limits, maintainability, and vendor support. |
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