You can run a local AI coding assistant on Linux without a dedicated GPU, but the right hardware depends on the model, its context length, and how you use it. As a practical starting point, Ollama lists 8 GB of RAM for 7B models, 16 GB for 13B models, and 32 GB for 33B models. These are broad model-size guidelines, not guarantees for a responsive coding workflow. A GPU can accelerate inference when the runtime supports its backend and the card has enough VRAM; it is optional, not a prerequisite. Plan separately for model files on disk and for the extra memory used by long context windows and concurrent requests.
There is no single minimum PC specification
“Local AI coding assistant” can mean anything from asking a model questions about a few pasted lines to running an agent that reads and edits a project through a coding tool. Those workflows do not impose identical demands. The hardware decision starts with the model you intend to run, the context length your tool needs, and whether your workload is autocomplete and chat or more involved agentic coding.
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A useful way to compare Linux systems is to look at five things together:
- System RAM: how much memory is available for the model and its runtime.
- GPU VRAM and backend: whether the chosen runtime can use the particular GPU through a supported acceleration path.
- Model-file size and free storage: whether the download fits, with room for other models and normal system use.
- Context length: how much conversation or code the model must be able to consider at once.
- Workflow: whether you mainly want completion and chat or a coding-tool configuration that makes larger context useful.
A model that fits in memory at a short context setting may not fit at the longer context required by a particular tool. Likewise, a GPU’s presence alone does not tell you whether it can accelerate a selected model: backend support, drivers, and available VRAM matter. The documentation cited below provides guidance and examples, not a universal Linux specification or a speed ranking for particular CPU and GPU combinations.
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How much RAM do you need for local AI coding?
Ollama’s quickstart gives these broad starting points by model size. It does not present them as a guarantee that the model will be comfortable for every interactive coding setup.
| Model size | Ollama quickstart RAM guidance | How to interpret it |
|---|---|---|
| 7B | At least 8 GB | A starting point for this model size, not a coding-workflow guarantee. |
| 13B | At least 16 GB | A starting point; the chosen context and runtime workload also affect memory needs. |
| 33B | At least 32 GB | A starting point, not a promise that every 33B configuration or long context will fit. |
These figures are useful for deciding what to try first, but do not read them as a complete bill of materials. Ollama also says RAM requirements scale with context length and parallel requests. A coding tool that keeps a larger amount of project context available, or a setup serving multiple requests at once, can therefore need more memory than a simple one-request trial. The quickstart does not establish a universal extra-RAM amount for either case.
Choosing a model for the PC you already have
If your system is near the relevant starting point, begin with a smaller model and a modest context rather than assuming a larger model will work because its listed RAM figure matches your installed memory. Leave room for Linux, the coding application, and the runtime; the published starting points are not a measured interactive workload or a guarantee about free memory after other software is open.
For a midrange machine, use the model size and the context configuration together to judge fit. On a machine with 32 GB of RAM, for example, the quickstart’s 33B figure is not a promise that a 33B model will also have room for an especially long context and other processes. Treat the listed numbers as the first filter, then test the intended setup and adjust if it runs out of memory.
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Can you run a local coding assistant without a GPU?
Yes. Ollama documents CPU inference as well as optional GPU acceleration. A dedicated graphics card is not mandatory to try a local coding model on Linux. CPU-only inference is a valid starting configuration, particularly when the goal is to see whether a smaller model suits the workflow before buying or upgrading hardware.
GPU acceleration is an option when the runtime supports the card and its software stack. Ollama’s current development documentation lists CUDA, ROCm, and Vulkan backends and describes Linux prerequisites. That is not a claim that every NVIDIA, AMD, or Intel GPU works automatically, nor that any supported card has enough memory for every model. Check the backend and driver requirements for the exact GPU and runtime combination you plan to use.
The available sources do not provide comparable Linux benchmarks across CPUs, GPUs, model quantizations, and coding-agent workloads. As a result, there is no supported token-per-second estimate or universal speed comparison to apply to a particular system here. Consider acceleration as a compatibility and workload decision, rather than assuming that the presence of a GPU guarantees a particular response speed.
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There is no single VRAM minimum established for all local coding models. The amount depends on the model and context configuration, and the available Ollama example illustrates why the qualifiers matter: in a January 23, 2026 article, Ollama gives an estimate of about 23 GB of VRAM for glm-4.7-flash with a 64,000-token context. That is one model at one context length, not a general requirement for local coding.
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If that exact model-and-context target is important to you, searching for a graphics card with at least 24 GB of VRAM is a reasonable way to narrow candidates, while still checking the runtime backend and other system requirements. Do not turn that search phrase into a rule for smaller models, shorter contexts, CPU inference, or other model configurations. The example does not establish a universal amount of VRAM for those cases.
System RAM and VRAM are separate capacity considerations. A GPU can provide an acceleration path, but the example does not mean that simply buying a card with 24 GB of VRAM ensures the whole workflow will fit or be accelerated: the chosen backend must support the card, and the model and context must match the target configuration.
Why context length changes the hardware decision
Context length is the amount of text the model can consider in a request, including relevant code and conversation. More context can be useful when a coding tool needs to work with a broader body of project information, but it has a memory cost. Ollama states that required RAM scales with context length as well as parallel requests, so capacity that is adequate for a shorter chat may not be adequate after increasing the context setting.
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If the model loads but the coding workflow fails or exhausts memory after you raise context, the configuration may be the issue rather than proof that local inference is impossible on the machine. Continue’s guide recommends reducing the context length or choosing a smaller model when memory is insufficient. That trade-off is straightforward: use less context or a smaller model to ease memory pressure, while accepting that the tool will have less context available or use a different model.
How much storage do model downloads need?
Model files can take from under 1 GB to tens or hundreds of gigabytes, according to examples in Ollama’s quickstart. Its listed download sizes include 4.7 GB for Llama 3.1 8B and 40 GB for Llama 3.1 70B. Those examples make clear why storage should be checked separately from RAM: a model may be a reasonable memory target yet still require more free disk space than expected.
Check the download size for the model you actually intend to pull and make sure the target drive has adequate free space. If you plan to keep multiple models, account for each file rather than planning only for the first download. The examples are specific to the named models in the quickstart, not a universal size estimate for every 8B or 70B model.
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Match the setup to your Linux PC and workflow
| Situation | Practical approach | What to verify |
|---|---|---|
| Existing or modest Linux PC | Try a smaller model first; CPU inference is an option. | Available RAM, the model download size, and whether a reduced context makes the workflow usable. |
| Midrange system | Choose model size and context together; consider GPU acceleration if the supported backend and VRAM fit. | Actual RAM headroom, exact GPU/backend/driver support, and the selected context. |
| Large model with long context | Treat VRAM as a major selection factor if targeting GPU inference. | The exact model and context. Ollama’s roughly 23 GB example applies to glm-4.7-flash at 64,000 tokens. |
| Autocomplete or chat | Start with a model and context that fit the available system. | Do not assume the memory needs of a coding-tool configuration are identical to a basic chat setup. |
| Agentic coding with coding tools | Check the tool’s context recommendation before choosing the model and hardware. | Ollama’s described coding workflow recommends at least 64,000 tokens; longer context raises memory needs. |
The table is a planning framework, not a compatibility list: the official guidance does not provide a universal GPU threshold or comparable performance results. If your Linux PC is constrained, the lowest-risk route is to test a smaller model with a context length appropriate to the tool, then change one constraint at a time if memory is insufficient.
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Install and check a local Ollama setup
Ollama’s quickstart documents this Linux installation command:
curl -fsSL https://ollama.com/install.sh | shAfter installation, the quickstart’s commands for managing and inspecting models include:
ollama pull MODEL_TAG
ollama list
ollama psReplace MODEL_TAG with the exact tag you intend to download. Ollama’s quickstart includes an ollama pull step, while ollama list and ollama ps let you check available models and inspect the running model’s memory use. The latter is useful after loading a model: compare what the runtime reports with the memory pressure or failure you are seeing, then try a smaller model or shorter context if necessary.
- Check the target model and its context needs. Use the model size as a first RAM filter, and check whether your coding tool recommends a particular context.
- Check free storage before downloading. Model files range widely in size; use the selected model’s stated download size, not a generic assumption based on its parameter label.
- Install Ollama and pull the exact model tag. Keep the chosen tag consistent with the configuration in your coding tool.
- Inspect the model state. Use
ollama listto see available models andollama psto inspect memory use for the running model. - Adjust after testing the intended workflow. If memory is insufficient, reduce context length or select a smaller model, following Continue’s guidance.
Using Ollama with Continue: avoid tag and context mismatches
Continue documents Ollama as a local provider. Its guide supplies specific model tags and configuration examples, but notes that the guide is based on Ollama v0.11.x and Continue v1.1.x. Treat those recommendations and examples as version-specific; check the current versions and current model tags when setting up a newer installation.
A model-not-found error can arise when the tag in the Continue configuration does not match the model you pulled. Compare the configured tag with the exact tag available in Ollama rather than changing hardware to solve a naming mismatch. Separately, if the model is found but the workflow encounters memory pressure, lowering contextLength or using a smaller model are the adjustments Continue describes. These are different failure modes: one is a name/configuration mismatch, the other is a capacity problem.
A practical buying or upgrade checklist
Before spending money, identify the constraint in the workflow you actually want. The documentation supports system RAM upgrades and additional SSD capacity as plausible ways to address insufficient capacity or storage, but it does not establish specific product recommendations or prices.
- Model does not fit comfortably: compare available RAM with Ollama’s broad starting guidance and consider a smaller model.
- Long-context workflow runs out of memory: consider reducing the configured context or choosing a smaller model; do not assume the model-size RAM figure covers every context.
- Considering a GPU: verify Linux driver and backend support for the exact card and runtime, then compare VRAM with the exact model/context target.
- Download cannot complete: check free storage against the actual model size and account for any other model files you want to keep.
- Performance is the deciding factor: do not infer a speed guarantee from RAM or VRAM alone. The cited guidance does not include comparable benchmark results for Linux coding workflows.
The most defensible starting point is usually the hardware you already own: choose a model that matches its available memory, ensure you have enough disk space, and test the context setting required by your coding tool. Buy a GPU only if your intended model and context make acceleration valuable and the card’s supported backend and VRAM suit that specific configuration.
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