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Run Mellum 2.1 locally by choosing either the original JetBrains/Mellum2.1-12B-A2.5B-Thinking checkpoint with vLLM or a quantized GGUF build with llama.cpp. The vLLM route follows JetBrains’ model-card configuration and includes an optional tool-calling setup; the GGUF route is aimed at local runtimes such as llama.cpp, Ollama, and Docker Model Runner, and can expose an OpenAI-compatible API to a coding-agent client.
Mellum 2.1 is a 12-billion-parameter mixture-of-experts thinking model with 2.5 billion active parameters, a 131,072-token context limit, 28 layers, and 64 experts with eight active per token. It is released under Apache 2.0. The official material does not state a universal minimum GPU, system-memory, or disk configuration, so select a format and context length only after checking the limits of your own machine.
What Mellum 2.1 is and why the local setup matters
Mellum 2.1 is JetBrains’ open-weight model for coding agents and other complex reasoning tasks. Its intended work includes repository analysis, code generation, command execution, tool use, mathematics, and multi-step problem solving. The original checkpoint is identified as JetBrains/Mellum2.1-12B-A2.5B-Thinking.
- Total parameters: 12 billion.
- Active parameters: 2.5 billion per token because the model is a mixture of experts.
- Architecture details: 28 layers, 64 experts, with eight experts active.
- Context length: 131,072 tokens in the model card.
- License: Apache 2.0.
A local deployment keeps source code, prompts, tool output, and credentials inside infrastructure you control. In the JetBrains release announcement, author Bulat Salimzianov described this as the ability to “Run Mellum2.1 locally or on your own infrastructure to keep code and data fully under your control.” Local hosting does not automatically make an agent safe: the agent can still run destructive commands or expose data if you grant it excessive permissions.
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Choose a serving path before downloading anything
There are two practical paths in the official material. Your choice is mainly about model format, runtime, and the client you want to connect, not a promise that one path is faster on every computer.
| Path | Model format | Documented runtime | Best fit | Important qualification |
|---|---|---|---|---|
| Original checkpoint | JetBrains/Mellum2.1-12B-A2.5B-Thinking | vLLM | Users following the standard model-card server and tool-calling configuration | The official commands are documented examples; they were not independently tested here. |
| Quantized local model | GGUF | llama.cpp, with Ollama and Docker Model Runner alternatives | Users who need a quantized artifact and a local OpenAI-compatible server | Artifact size is not the same as total runtime RAM or VRAM. |
The official pages do not publish a minimum GPU, system-memory, or storage requirement for either route. Parameter count, quantization-file size, and context length are useful planning signals, but none is a universal hardware specification. A reliable recommendation requires your operating system, available memory, processor or GPU, target context, quantization, runtime, and agent workload.
Path A: serve the original checkpoint with vLLM
1. Get the exact checkpoint
Use the model identifier shown in JetBrains’ official model card: JetBrains/Mellum2.1-12B-A2.5B-Thinking. Keep the identifier exact so the serving command resolves the intended repository and model variant. Confirm the repository’s current instructions before deployment because model repositories and serving options can change.
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The model card documents this command:
vllm serve JetBrains/Mellum2.1-12B-A2.5B-Thinking
--max-model-len 131072
--reasoning-parser qwen3--max-model-len 131072 requests the model-card context length. That is a maximum setting, not a statement that every machine can hold a 131,072-token conversation. Longer context consumes more memory and can reduce throughput. If your deployment cannot accommodate that setting, consult the current vLLM and model-card guidance for a smaller context value rather than assuming the model has failed.
--reasoning-parser qwen3 selects the reasoning-output parser shown by JetBrains. Keep the parser aligned with the model’s current chat and reasoning format; changing parser settings without checking the card can produce malformed reasoning or tool output.
3. Enable the documented tool-calling configuration when required
For an agent that sends structured tool definitions, the model card adds automatic tool choice and the Hermes parser:
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vllm serve JetBrains/Mellum2.1-12B-A2.5B-Thinking
--max-model-len 131072
--reasoning-parser qwen3
--enable-auto-tool-choice
--tool-call-parser hermesUse this variant only when your client expects tool calls and your vLLM version supports the documented flags. The flags tell the server how to select and parse tools; they do not provide tools, a shell sandbox, a repository policy, or an autonomous-agent loop. Your client still has to define functions, validate arguments, execute approved actions, and return results to the model.
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Watch the vLLM startup output for model-loading errors, unsupported arguments, out-of-memory failures, or a context-length rejection. Confirm that the server has reached its ready state and that the client can reach the API address exposed by your deployment. Do not give an agent write access to a valuable repository until a simple, read-only request succeeds.
Path B: run a quantized GGUF build locally
Why choose GGUF
The official Mellum 2.1 GGUF card documents local serving through llama.cpp and also lists Ollama, Docker Model Runner, vLLM, and a Pi agent connection. GGUF lets you select a quantization that trades artifact size and numerical fidelity against the resources available to your runtime. It is a model-file choice, not a guarantee of a particular speed or coding quality.
| Quantization | Example artifact size in the official card | How to interpret it |
|---|---|---|
| Q4_K_M | 8.1 GB | Labeled as a balanced recommended quantization in the card. |
| Q6_K | 10.9 GB | Larger example artifact with higher precision than Q4_K_M. |
| MXFP4_MOE | 7.0 GB | Smallest of these listed examples; verify runtime support before choosing it. |
Those are example file sizes, not minimum RAM, VRAM, or storage requirements. Runtime memory also includes model metadata, working buffers, the key-value cache for the active context, the server process, and your operating system. A 131,072-token context can require substantially more memory than a short prompt. Leave free disk space for downloads, temporary files, logs, and updates rather than planning around the model file alone.
Use the llama.cpp server route
Follow the current GGUF card’s llama.cpp server instructions to download the selected file and start the server. The card documents an OpenAI-compatible local API, which is useful because many coding-agent clients already know how to send chat requests to an OpenAI-style endpoint. Use the exact server command, model-file path, context setting, and host or port options from that card for your platform; those operational details can change as llama.cpp evolves.
After startup, verify the server locally with a small prompt before connecting an agent. Ask for a short explanation of a harmless code fragment, then test a structured tool request if your client needs tools. A successful text response proves that inference works; it does not prove that tool-call formatting, long-context handling, or agent permissions are configured correctly.
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Use llama-cli for a terminal-only test
The GGUF card also documents terminal inference with llama-cli. This is a useful diagnostic because it removes the agent client from the equation. First confirm that the selected GGUF file loads and produces text. If the command-line test fails, fix the model path, runtime support, available memory, or quantization choice before investigating agent configuration.
Consider Ollama or Docker Model Runner
The same official GGUF material lists Ollama and Docker Model Runner as alternatives. Choose one only if it fits your existing deployment and provides the API behavior your coding-agent client expects. Import the exact Mellum 2.1 GGUF variant described by the current card, then use that runtime’s documented local-server configuration. Do not assume that a runtime accepting GGUF automatically supports every mixture-of-experts quantization or every tool-calling format.
Connect Mellum 2.1 to a coding agent
Understand the connection architecture
The model server and the agent are separate components:
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- The coding-agent client sends a prompt, repository context, and optional tool definitions.
- The model returns text, reasoning content, or a structured tool call according to the server and parser configuration.
- The client validates the response, runs an approved tool, and sends the result back for the next turn.
This separation matters. Running Mellum 2.1 on your own machine does not turn it into a complete coding-agent harness. You still need an agent client, a repository working directory, tool implementations, approval rules, logging, and a way to stop or recover a run.
Configure an OpenAI-compatible client
For the llama.cpp route, point your agent client at the local OpenAI-compatible API documented by the GGUF card. Enter the local server’s base address in the client’s provider or endpoint field, select the Mellum 2.1 model identifier used by that server, and provide any authentication value the client requires for a local provider. Do not send requests to a hosted service by leaving an old cloud endpoint in the configuration.
For vLLM, use the API address exposed by your vLLM deployment and make sure the client sends tools in the format expected by the selected parser. The automatic-tool-choice and Hermes settings affect server-side parsing; the client must still declare each function’s name, description, and argument schema.
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Connect Pi using the documented local-provider pattern
The GGUF card includes a Pi example that configures the local llama.cpp server as a provider and then launches Pi in the project directory. Reproduce the provider fields from the current card rather than guessing variable names. Start the model server first, configure Pi to use that local provider, change into the repository you want the agent to work on, and launch Pi there. Begin with read-only tasks such as listing files or explaining a function, then add write and command tools deliberately.
Define safe tool permissions
- Start in a disposable clone or a test repository.
- Require confirmation before file deletion, dependency installation, database changes, network access, or shell commands with broad scope.
- Restrict the working directory so a tool cannot accidentally modify unrelated files.
- Keep secrets out of prompts and environment variables visible to untrusted tools.
- Log model messages, tool arguments, command output, and human approvals so a failed run can be reconstructed.
- Use version control checkpoints before allowing multi-file edits.
Plan context length and resource use
The model card’s 131,072-token context limit describes the model’s supported maximum, not the amount your machine can use comfortably. An agent may consume context with system instructions, repository summaries, source files, command output, test logs, and previous tool calls. Large contexts also increase key-value-cache memory and can make each response slower.
Set an initial context target that your runtime can sustain, then increase it only after observing memory use and response latency. Keep repository ingestion selective: start with the files relevant to the task, use directory summaries, and trim repetitive command output. A smaller context can be more reliable than forcing the maximum and triggering memory pressure mid-run.
Mixture-of-experts routing explains why the model has 12 billion total parameters but 2.5 billion active parameters per token. It does not mean the process needs memory only for 2.5 billion parameters; the runtime still stores the model’s weights and supporting state according to its format and implementation.
Validate a fresh installation
- Confirm the model source: use the exact JetBrains checkpoint or an official Mellum 2.1 GGUF artifact.
- Confirm the runtime: use vLLM for the original checkpoint, or a GGUF-compatible runtime documented by the GGUF card.
- Start with conservative settings: use a short context and a small test prompt if your machine is close to its memory limit.
- Test plain generation: ask for a short code explanation and verify that the response is coherent.
- Test structured tools: send one harmless tool definition and inspect whether the client receives a valid name and argument object.
- Test the repository boundary: run the agent from a disposable project directory and verify that it cannot access paths outside the intended workspace.
- Test recovery: stop the server, restart it, and confirm that the client reports the interruption instead of silently repeating a command.
Troubleshooting common failures
The model will not load
Check that the model identifier or GGUF path is exact, that the runtime supports the selected format, and that enough memory remains for weights, buffers, and the requested context. A smaller quantization or shorter context may reduce pressure, but the official sources do not define a universal hardware threshold. Treat any recommendation as system-specific rather than as a Mellum requirement.
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Startup rejects a vLLM flag
Compare your installed vLLM version with the current model-card instructions. The reasoning and tool-call flags are documented examples and may depend on runtime support. Verify the spelling of --reasoning-parser, --enable-auto-tool-choice, and --tool-call-parser, then consult the live model card and vLLM documentation for version-specific changes.
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Check all three layers: the client must send a valid tool schema, the server must use the parser and automatic-choice settings appropriate to the model, and the agent must correctly decode and execute the returned call. Test one tool with simple arguments before enabling a large tool catalogue. Never execute raw model text as a shell command without validation.
The agent cannot reach the local API
Confirm that the server is running, that the client uses the same local address and port shown by the runtime, and that a firewall or container network is not blocking the connection. If the server runs inside a container, ensure the client can reach the published interface rather than an address available only inside the container.
Long tasks run out of memory or become unusably slow
Reduce the requested context, trim repository and tool output, use a smaller quantization, or move inference to infrastructure with more available memory. The GGUF artifact sizes listed by JetBrains cannot predict this failure by themselves because cache and runtime overhead vary with context and implementation.
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Keep the model files and runtime packages updated from the official repositories, but review release notes before changing a production setup. Serving flags, parser names, quantization availability, and client compatibility can change. Pin versions for repeatable deployments and record the model revision, quantization, context setting, runtime version, and agent configuration used for each project.
Limit network exposure. A local API bound to a loopback interface is different from one reachable by every device on a network. If remote access is necessary, add authentication and transport protection appropriate to your environment, and avoid placing secrets in prompts or logs. Local inference protects data from a hosted provider only when your own host, containers, logs, and tools are controlled.
Which setup should you use?
- Choose vLLM with the original checkpoint if you want to follow JetBrains’ standard serving example and need its documented reasoning and tool-calling flags.
- Choose GGUF with llama.cpp if you need a quantized local artifact and an OpenAI-compatible local API for an agent client.
- Choose Ollama or Docker Model Runner if one of those runtimes already fits your environment and supports the exact GGUF variant and API behavior you require.
- Start with Pi’s documented provider example if Pi is your agent client and you want a concrete local-server connection path.
There is no evidence-supported universal “best” machine or quantization for Mellum 2.1. Match the route to your actual memory, compute, context, and tool-use requirements, then validate it with a read-only agent task before permitting repository changes.
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