Local Coding Model RAM and VRAM: What You Need

Updated 6 November 2026 · Standard formula, shown below

For a local coding model, 16 GB of system RAM is a practical minimum for a small quantized model, while 32 GB is a more comfortable starting point; GPU memory needs commonly range from about 8 GB for smaller models to 16–24 GB or more for larger ones. The exact requirement depends on model size, quantization, context length, and whether the model runs on a GPU, CPU, or both.

What RAM and VRAM do

RAM is your computer’s main working memory. VRAM is memory on a graphics card, used to hold model data and other GPU workloads. A model’s weights—the learned numerical parameters that produce its responses—take up most of the memory needed to load it. During generation, the software also needs space for the context cache, temporary calculations, and the operating system and other applications.

When people ask how much memory a model needs, they often mean the memory required to load its weights. That is a useful starting point, but it is not the whole requirement. A model that barely fits may still run slowly, run out of memory when given a long prompt, or leave too little room for the rest of the computer.

Practical memory ranges

These are broad planning ranges, not guarantees. Model formats, inference software, context settings, and background applications change actual use. “B” means billions of parameters; quantized models store those parameters using fewer bits than full-precision versions.

Small models, around 3B–7B parameters: Often a reasonable entry point for local coding assistance. A 4-bit version may need several gigabytes for weights; 8 GB of VRAM can be workable for some models, while 12–16 GB gives more room. Aim for 16 GB of system RAM at minimum, with 32 GB preferable if other apps are open.
Medium models, around 13B–14B: A 4-bit model’s weights alone can occupy roughly 7–10 GB, depending on format and metadata. In practice, 12 GB of VRAM may be tight; 16 GB or more is a safer target. A computer with 32 GB of system RAM is a useful baseline.
Larger models, around 30B–34B: At 4-bit quantization, weights may take roughly 16–22 GB. Plan on a GPU with 24 GB of VRAM for a more straightforward fit, or use CPU offloading and accept lower speed. 32–64 GB of system RAM can help with CPU-based or partially offloaded runs.
Very large models, around 70B: Even 4-bit weights can require roughly 35–45 GB before runtime overhead. These generally need multiple GPUs, substantial system memory with offloading, or a more specialized setup. They are not a realistic fit for most single-GPU desktop computers.

Weight estimates are approximate: a decimal gigabyte (GB) and a binary gibibyte (GiB) are not identical, and files include more than just raw parameter values. Always check the model’s published file size and the requirements for the specific program you plan to use.

Why quantization and context change the answer

Quantization reduces the number of bits used to represent model weights. A 4-bit version is much smaller than a 16-bit version, but it is not exactly one quarter of the memory in every real setup: file metadata, scales, runtime buffers, and implementation details add overhead. Lower-bit models can also differ in output quality, so compare the available versions rather than treating bit depth as a pure memory setting.

Context length is the amount of conversation, code, or documentation the model can consider at once. The key-value cache, often called the KV cache, grows as context grows and consumes additional memory. Its size varies with model architecture, number of tokens, and cache precision. As a result, a model that loads successfully with a short prompt may fail when asked to process a large repository or a very long chat.

For coding, context matters because you may want to include several files, compiler output, and instructions together. More context is not automatically better: it uses more memory, and the model may not use every supplied detail equally well.

GPU, CPU, and shared memory

If the full model and its runtime fit in VRAM, a compatible GPU can usually generate tokens faster than a CPU-only setup. The exact speed depends on the GPU, software, model, and workload. If the model does not fit, some tools can keep part of it in system RAM and move data between the CPU and GPU. This can make a model usable, but transfers may substantially reduce speed.

CPU-only inference is possible, especially with quantized models and enough system RAM. It can be a practical way to experiment or run a smaller model, but generation may be slower than on a suitable GPU. Integrated graphics often use system memory rather than separate VRAM; that memory is shared with the operating system, so “16 GB shared” should not be treated as 16 GB available solely for model weights.

How to choose a setup

Start with the model you actually intend to run, not a general claim that a computer can handle “local AI.” Check the model’s parameter count, quantized file size, recommended context, supported formats, and the inference program’s hardware guidance. Then leave headroom for runtime memory and ordinary desktop use.

Choose the model size: Begin with a 7B-class model if you want modest hardware demands. Move up only if its quality or context limits do not meet your needs.
Check the available memory: Look at dedicated VRAM separately from system RAM. Do not assume that a model file’s size is the total memory needed to run it.
Allow overhead: Leave several gigabytes beyond the weights where possible, especially for a GPU setup. Longer context and multiple simultaneous sessions need more.
Try a smaller context first: If loading fails or memory use is excessive, reduce the context setting, close memory-heavy applications, or select a smaller or more heavily quantized model.
Test your real task: A short code question is much lighter than indexing a large project or pasting many files. Test with the prompts and tools you expect to use.

Common problems and trade-offs

An out-of-memory error does not always mean the model’s weights are too large. The context cache, another GPU application, a display workload, or a second model may be using the remaining memory. Restarting the inference program, lowering context, and checking which device is being used can help identify the cause.

Another misconception is that more RAM guarantees better coding answers. Memory mostly determines which models and context sizes you can run; answer quality depends on the model, its training, quantization, prompts, and the task. A smaller model that fits comfortably may be more useful than a larger one that constantly offloads or cannot load.

Local inference can keep prompts on your own machine, but privacy depends on the whole setup. Extensions, editor integrations, telemetry settings, and tools that fetch online information may send data elsewhere. Review the software’s behavior before submitting private source code or credentials.

Frequently Asked Questions

Is 16 GB of RAM enough to run a local coding model?
It can be enough for a small quantized model, particularly with a short context and few other applications running. It is a tight baseline; 32 GB offers more flexibility for larger models, CPU offloading, and normal multitasking.

How much VRAM do I need for a 7B coding model?
Some 4-bit 7B models can run within 8 GB of VRAM, but runtime overhead and context may make that tight. 12–16 GB provides more headroom, though the model’s file and software requirements are the best guide.

Can I run a coding model without a GPU?
Yes. CPU inference works with suitable software, enough system RAM, and a model that fits. It is often slower than GPU inference, so a smaller quantized model is a sensible starting point.

Does a longer context require more memory?
Yes. The KV cache generally grows with the number of tokens kept in context, using additional VRAM or system RAM. The amount depends on the model architecture and inference settings.

Does a 4-bit model need exactly one quarter the memory of a 16-bit model?
No. Quantization greatly reduces weight storage, but metadata and runtime overhead remain, and context memory is a separate cost. Actual requirements vary by model format and software.

Method: the standard formula for this calculation, rounded as each result states. See how the calculators work or report an error.