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There is no single RAM requirement for local AI coding. Start with the exact model file and quantization, then account for the inference device, context length, concurrent requests, operating system, and your editor and other applications. A 4.7 GB download is not a promise that 4.7 GB of RAM is enough.
For CPU inference, the model occupies system memory. For discrete-GPU inference, the important pool is usually GPU VRAM; a model can also be split between VRAM and system RAM. On unified-memory computers, CPU and GPU share the same pool. The practical target is enough available memory for the model plus runtime and workload overhead, with room left for everything else you keep open.
What “RAM needed” actually means
Local coding assistants use several kinds of memory at once. Treating any one published number as the whole requirement is the most common sizing mistake.
Parameter count is a label, not a capacity specification
A model’s parameter count (for example, 7B or 32B) describes its scale. It does not tell you the exact amount of system RAM or VRAM required by a particular runtime. Two files with the same parameter count can have different quantization, metadata, context settings and runtime overhead.
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Download size is only the starting point
The file size shown in a catalog is the storage required to download that variant. During inference, the runtime also needs working buffers, the model’s runtime representation and a key-value (KV) cache for the conversation context. Operating-system memory, your IDE, browser, language servers and build tools compete for the same available pool when you run on the CPU.
RAM, VRAM and unified memory are different pools
- CPU inference: the model is loaded into system RAM.
- Discrete GPU inference: the model is loaded into GPU VRAM when it fits; some runtimes can split layers between VRAM and system RAM.
- Unified memory: CPU and GPU share one physical pool, as on Apple-style designs. A model using the GPU still reduces the memory available to the operating system and applications.
Ollama documents these placement modes and exposes whether a loaded model is on the CPU, GPU or split across both in its process information. See the Ollama FAQ for the runtime’s memory behavior.
Published model sizes you can use for planning
The following figures are catalog download sizes, not guaranteed peak-memory requirements. They are useful for comparing relative scale and for checking whether a machine has any realistic chance of loading a model.
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| Variant | Catalog download size | Catalog context listing |
|---|---|---|
| 0.5B | 398 MB | 32K |
| 1.5B | 986 MB | 32K |
| 3B | 1.9 GB | 32K |
| 7B | 4.7 GB | 32K |
| 14B | 9.0 GB | 32K |
| 32B | 20 GB | 32K |
These values come from the Ollama Qwen2.5-Coder catalog, inspected in 2026. The 32K label is the advertised context capacity for the listed variants; it is not a claim that every computer can run a full 32K context comfortably.
Other coding-model scale examples
| Family and variant | Published catalog size | What it illustrates |
|---|---|---|
| DeepSeek Coder V2 16B | 8.9 GB | A mid-size model with a file larger than the 7B Qwen variant |
| DeepSeek Coder V2 236B | 133 GB | A model whose file is workstation or server scale |
| Code Llama | 7B, 13B, 34B and 70B variants | One family can span several hardware tiers |
See the DeepSeek Coder V2 catalog and Code Llama catalog for those published variant lists. Catalog entries can change, so verify the exact tag you intend to download.
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Why quantization changes the answer
Quantization stores model weights with fewer bits, reducing the model file and usually the memory pressure, with a possible quality trade-off. You must identify the precise quantized file rather than relying on “3B” or “7B” alone.
Qwen’s 3B GGUF model card gives a concrete example. The model has 3.09B parameters and a 32,768-token full context, but the downloadable files differ substantially:
| Quantization | Published file size | How to interpret it |
|---|---|---|
| Q2_K | 1.38 GB | Smallest listed file; lowest-weight precision of these examples |
| Q4_K_M | 2.1 GB | Middle-size file in this set |
| Q8_0 | 3.62 GB | Largest listed file; higher weight precision |
All three figures are download sizes from the Qwen2.5-Coder 3B GGUF model card (2024). None is a complete system-RAM or VRAM requirement. The runtime still needs buffers and context memory, and a higher-precision file leaves less room for your editor and operating system.
Context length and simultaneous requests can dominate memory use
The model weights are only one part of working memory. The KV cache grows as the prompt and generated conversation grow. A short coding request and a repository-wide task can therefore have very different footprints even with the same model file.
Ollama’s documentation states: “By default, Ollama uses a context window size of 2048 tokens.” The Qwen catalog advertises up to 32K context for its variants, but selecting that capacity can require much more memory than the default. Increase context only when the task needs it, and monitor available memory while doing so.
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Concurrency multiplies this effect. Ollama explains that parallel processing increases the allocated context by the number of parallel requests. Two simultaneous requests can therefore consume roughly two context allocations for the same model, in addition to the shared model weights. If your editor, terminal integration and a separate chat client all send requests at once, set a lower concurrency or context value before assuming that a larger model is the problem.
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- Record the exact model variant. Write down the family, parameter tier, tag and quantization. Use the published file size as your baseline. For example, “Qwen2.5-Coder 7B, 4.7 GB catalog file” is actionable; “a 7B model” is not.
- Identify where inference will run. Check whether your runtime is using CPU system RAM, a discrete GPU’s VRAM, or a shared unified-memory pool. Do not add RAM and VRAM as if they were automatically interchangeable. A model that fits in system RAM may not fit in a 6 GB graphics card, and a model that fits in VRAM still leaves the CPU and applications needing their own memory.
- Set the context and concurrency you actually need. Start with the runtime’s default context, then increase it in small steps for larger files or longer conversations. Decide how many requests can run simultaneously. Both settings affect the KV cache and therefore working memory.
- Reserve practical headroom. Leave capacity for the operating system, runtime, IDE, browser, language servers, compilers and build artifacts. This is a prudent operating rule, not a published universal allowance. If memory pressure causes swapping or an out-of-memory error, reduce context or concurrency before moving to a larger model.
Can your computer run a 7B, 14B or 32B coding model?
There is no honest universal “minimum RAM” table without naming the quantization, context, runtime, operating system and inference device. Use the following decision logic instead.
| Published model file example | What to check on your machine | Likely decision |
|---|---|---|
| Qwen2.5-Coder 0.5B (398 MB) or 1.5B (986 MB) | Available memory after the OS and editor; CPU support is usually the limiting factor rather than file storage | Often the easiest starting tier, but quality and repository comprehension are lower than larger tiers |
| Qwen2.5-Coder 3B (1.9 GB catalog file; 1.38–3.62 GB for the cited GGUF quantizations) | Quantization, context and whether the model shares RAM with your development tools | A practical small-model test case; choose the file and context deliberately |
| Qwen2.5-Coder 7B (4.7 GB) | Several gigabytes beyond the file are needed for runtime, cache and applications; check VRAM separately if using a GPU | Possible on many modern systems, but not guaranteed by the 4.7 GB figure |
| Qwen2.5-Coder 14B (9.0 GB) | Whether the remaining system RAM or VRAM can hold the model while your IDE and tools stay responsive | Requires a larger memory pool and careful context settings |
| Qwen2.5-Coder 32B (20 GB) | Unified-memory or VRAM capacity, spillover behavior and substantial headroom | Consider only when the machine has a correspondingly large usable pool; a 20 GB download alone is not sufficient evidence |
For a quick check, compare model file + runtime overhead + context/cache + applications with available memory, not the computer’s advertised installed capacity. A computer with 16 GB installed may have materially less available after the operating system and development stack load.
How to check placement and memory pressure in Ollama
- Load the exact model tag you intend to use, rather than a similarly named default.
- Open Ollama’s process information while the model is loaded. Confirm whether it reports CPU, GPU or a split placement.
- Record available system memory and, for a discrete GPU, available VRAM before sending a long request.
- Run a representative coding task: a short completion, a multi-file explanation and the longest context you expect in daily work.
- Repeat with your normal IDE, browser, language server and build tools open. A setup that works in an otherwise empty desktop may fail during real development.
If the model loads but generation becomes extremely slow, inspect whether it has spilled from VRAM to system RAM. Memory capacity alone does not establish response speed; the cited sources do not provide matched speed benchmarks for these configurations.
Symptoms, causes and fixes
The model will not load
- Cause: The selected quantized file and runtime buffers exceed the available memory pool.
- Fix: Choose a smaller quantization or model tier, close memory-heavy applications, and verify whether the runtime is targeting the intended GPU.
It loads, then crashes on a long prompt
- Cause: Context/KV-cache growth pushed usage beyond available memory.
- Fix: Lower the context length, shorten retrieved files, reduce parallel requests, or move to a larger memory pool.
The computer starts swapping
- Cause: The model, cache and applications exceed physical RAM, so the operating system uses slower storage.
- Fix: Reduce model size or context, close background applications, and treat swapping as a capacity warning rather than a performance setting.
The GPU is full even though system RAM is available
- Cause: Discrete VRAM is a separate pool. Free system RAM cannot automatically make a model fit entirely in VRAM.
- Fix: Use a smaller file, allow a documented split placement if the runtime supports it, or select hardware with more VRAM.
A unified-memory computer feels constrained
- Cause: CPU, GPU, model and applications all draw from the same physical memory.
- Fix: Reduce context and concurrent requests, close other applications, or choose a smaller quantized model. Installing more memory is possible only on systems that support an upgrade.
Choosing between a larger model and more memory
More memory does not automatically make a model more capable, and a larger model does not help if it forces constant swapping. Decide which constraint is blocking your work.
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- Choose a smaller quantized model when the current task is autocomplete, short function generation or local documentation and your machine is memory-constrained.
- Choose more context when the model is adequate but cannot see the files, symbols or instructions your task requires. Increase it gradually and watch cache growth.
- Choose more RAM when CPU inference, the model and your normal development environment together exhaust system memory.
- Choose more VRAM when the model would otherwise fit your workflow but the discrete GPU is the limiting pool.
- Choose a unified-memory configuration with greater capacity when you need the GPU and CPU to access one shared pool and cannot tolerate frequent spillover.
Do not expect an upgradeable RAM kit to add discrete GPU VRAM. Before buying memory, confirm the motherboard and processor’s supported generation, maximum capacity, module type and channel configuration. Many laptops have soldered memory or no upgrade path.
Practical examples
Example: a small laptop for autocomplete
Suppose the catalog file is the 1.9 GB Qwen2.5-Coder 3B variant. Start with the default context, keep one coding session active and test with the IDE and language server open. If that is stable, increase context only when repository tasks require it. The 1.9 GB number is not a promise that a laptop with only that much free memory will work; the runtime and cache still need space.
Example: a desktop with a discrete GPU
A 7B file listed at 4.7 GB may fit a GPU with more than that amount of free VRAM, but you must still account for the graphics stack and runtime buffers. If the process view shows a split CPU/GPU load, system RAM becomes part of the working set. Test the longest prompt you routinely send rather than judging by whether the initial load succeeds.
Example: a 32B model on unified memory
The 20 GB Qwen2.5-Coder 32B catalog file leaves little room on a machine whose total unified pool is close to that value. The operating system, applications, runtime and context cache all share the pool. A smaller quantization or model tier may deliver a more usable experience than running at the edge of capacity.
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What the published numbers do—and do not—prove
The catalogs and model card establish model variants, context labels and downloadable file sizes. Ollama’s FAQ establishes that CPU/GPU placement, context and parallel requests change memory use. They do not establish one end-to-end minimum RAM figure for every operating system, runtime, quantization, context length and coding workflow. They also do not provide matched response-speed benchmarks across CPU, GPU and split configurations.
Use the numbers as an evidence-based starting point, then validate the exact model and settings on the computer you will use every day. Keep a note of the model tag, quantization, context and concurrency that worked; changing any one of them can change the memory requirement.
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