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There is no universal RAM or VRAM requirement for local AI. The right amount depends on the model’s parameter count, quantization, context length, runtime overhead, and whether you run it on the GPU, CPU, or shared unified memory. Start with the exact quantized model file, then reserve capacity for its context cache, inference engine, operating system, and other applications. Published examples show the range: OpenAI says its gpt-oss-20b can run on an edge device with 16 GB of memory, while gpt-oss-120b is designed to run efficiently on one 80 GB GPU. Those figures describe named models, not universal minimums.
What determines how much memory a local AI model needs?
The model weights are only the starting point. A reliable capacity decision accounts for five separate demands that can vary substantially between runtimes and workloads.
- Weights: The downloaded model file contains the learned parameters. Parameter count matters, but the file’s data type and quantization determine how many bytes each parameter occupies.
- Quantization: Lower-bit formats reduce weight memory. Ollama’s current gpt-oss documentation, accessed in 2026, says most of the model’s mixture-of-experts weights use MXFP4 at 4.25 bits per parameter; those weights represent more than 90% of the total parameter count. This is a model-specific example, not a conversion rule for every model.
- Context and the KV cache: As the prompt and conversation become longer, the runtime stores more key-value (KV) data for attention. A model that fits at a short context can fail at a long one.
- Runtime buffers: The inference engine needs temporary workspaces, graph data, and other buffers. Their size depends on the engine, model format, batch size, and hardware.
- Everything else using memory: The operating system, desktop, browser, monitoring tools, and other applications reduce the capacity available to inference. Multiple simultaneous conversations add more cache and scheduling overhead.
A model file that almost fills a GPU may therefore load successfully but leave too little room for a useful context or stable generation. Treat the file size as a lower bound, not as the required system specification.
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The clearest figures come from named models and a vendor’s workflow guide. They are useful reference points, but none is a guarantee for an arbitrary model with the same parameter count.
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| Example | Published model details | Memory figure | What the figure means |
|---|---|---|---|
| OpenAI gpt-oss-20b | 21 billion total parameters; 128k maximum context; MXFP4 is used for most MoE weights in the model library | 16 GB of memory | OpenAI’s August 5, 2025 launch material says the model can run on edge devices with 16 GB for on-device use and local inference. It is a publisher claim for this model. |
| OpenAI gpt-oss-120b | 117 billion total parameters; 128k maximum context | One 80 GB GPU | OpenAI presents 80 GB as the configuration on which this model runs efficiently. It is not a universal requirement for every 120-billion-parameter model. |
| NVIDIA local-inference guide | Workflow recommendations rather than one parameter-count formula | 8–16 GB, 16–24 GB, and 24–48 GB VRAM tiers | NVIDIA organizes the models and workflows in its guide into these VRAM ranges. The tiers are vendor guidance, not independently validated fit or speed thresholds. |
For a sense of scale, multiplying parameters by 4.25 bits and dividing by eight gives a rough raw-weight estimate for the MXFP4 example: about 11.2 GB for 21 billion parameters and about 62.2 GB for 117 billion parameters. Those calculations exclude metadata, alignment, KV cache, runtime buffers, and the operating system, so they cannot replace the model’s own hardware guidance. They do explain why the 120b example needs substantially more than its compressed weight data alone.
Are system RAM, VRAM, and unified memory interchangeable?
No. They are different pools with different performance and placement implications.
System RAM
System RAM is the computer’s general-purpose memory. CPU inference keeps model weights and caches there. It can also hold layers that are offloaded from a GPU when the model does not fit entirely in VRAM. That flexibility can make a larger model load, but moving work between CPU memory and a GPU changes performance and is dependent on the runtime and hardware interconnect.
Dedicated VRAM
VRAM is memory physically attached to a discrete GPU. Full GPU placement avoids repeatedly transferring layers over the system bus and leaves the CPU free for other work, but the entire working set—including cache and temporary buffers—must fit in the usable VRAM. A graphics card advertised with 24 GB provides a capacity tier; it does not promise that every model, context length, or format will fit.
Unified memory
On systems with unified memory, the CPU and GPU share one pool. This removes a hard VRAM-versus-RAM boundary, but the operating system and applications consume the same pool. A machine advertised with a given unified-memory capacity therefore has less than that amount available to the model, and the runtime still needs hardware support for the selected format.
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When comparing two computers, record the usable capacity after the operating system starts, not just the number printed on the specification sheet.
How can you estimate the requirement before buying hardware?
Use the model and workload first, then select hardware. There is no single sizing equation that applies to every inference engine, but this process exposes the variables that matter.
- Name the exact model revision. Record the parameter count, architecture (for example, dense or mixture-of-experts), maximum context, and the runtime you intend to use. A model family can have several quantized files with different memory footprints.
- Choose the format and quantization. Read the publisher or model-library description for the actual file size and data type. Do not infer the size from parameter count alone. If the documentation reports bits per parameter, you can calculate a rough raw-weight lower bound, then add headroom.
- Set a real context target. Decide whether you need a short chat, document analysis, or the model’s full advertised context. Longer contexts increase KV-cache demand; the maximum context number is not a promise that every computer can sustain it.
- Decide on concurrency. One interactive session and several simultaneous users have different cache requirements. Include the number of active sequences and any batching configured by the runtime.
- Choose placement. Mark whether you require full GPU placement, accept CPU inference, or will use partial GPU offload. A model may be loadable with offload even when it cannot fit entirely in VRAM, but the resulting speed depends on the exact platform and engine.
- Add operating headroom. Leave room for the operating system, desktop, browser, runtime buffers, and temporary allocations. A configuration that uses nearly all memory at load time is vulnerable to failures when a prompt grows.
- Verify software support. Confirm that the inference engine supports the model’s quantization, GPU architecture, driver, and operating system. Capacity without format support does not produce a usable deployment.
For a purchase decision, compare usable memory with the model file plus expected cache and runtime overhead. If the model documentation does not state those overheads, treat the result as uncertain and choose additional capacity or a smaller context rather than assuming a precise minimum.
How much RAM do you need for CPU-only inference?
CPU inference needs enough system RAM for the complete working set. That normally includes the quantized weights, KV cache, runtime buffers, and the rest of the operating system. The 16 GB figure from OpenAI is specifically an example for gpt-oss-20b on edge devices; it is not a blanket recommendation for all CPU workloads.
As a practical check, compare the model file size with the computer’s usable RAM. If a file consumes nearly all available memory, there is no safe room for a long context, another application, or a temporary allocation. Closing applications can recover some capacity, but it does not change the model’s intrinsic requirement. If the working set exceeds RAM, the operating system may page data to storage, producing unpredictable and usually unacceptable responsiveness.
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CPU-only execution can be the right choice when compatibility or privacy matters more than generation speed. It also provides a fallback for models that exceed a single GPU’s VRAM, provided the runtime supports the required offload path.
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How much VRAM do you need for a local LLM?
Match VRAM to the chosen model, format, context, and placement strategy. NVIDIA’s current local-inference guide uses 8–16 GB, 16–24 GB, and 24–48 GB tiers for the workflows it lists. Read those as selection bands for that guide, not as universal model-size limits.
What a 24 GB GPU can and cannot tell you
A 24 GB card gives more capacity than the 8–16 GB and 16–24 GB bands and can be a sensible target for readers who want room for larger quantized models or longer contexts. It still may not fit a particular model after cache and buffers are included, and the card’s compute capability, driver, and supported format affect usability. Check the selected model’s documented file size and runtime requirements before treating 24 GB as sufficient.
When 48 GB or 80 GB becomes relevant
Higher-capacity GPUs are aimed at larger models, larger contexts, or multiple concurrent sequences. OpenAI’s gpt-oss-120b example uses one 80 GB GPU for efficient operation, while NVIDIA’s guide groups some workflows in a 24–48 GB tier. These examples show the spread between model families; they do not establish a universal boundary at 48 or 80 GB.
Can you run an AI model with 16 GB of RAM?
Yes, for some models and workloads. OpenAI states that gpt-oss-20b can run on edge devices with 16 GB of memory, and describes that configuration as suitable for local inference. The model has 21 billion total parameters and a 128k context capability, but using the full context or running other applications may require more headroom than a minimal launch.
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For a different model, 16 GB may be insufficient even when its downloaded file appears close to that size. Check the exact quantization, context target, runtime overhead, and whether the machine is using shared unified memory. A 16 GB label alone cannot answer the compatibility question.
How does context length and concurrency change memory use?
Every additional token retained in a conversation increases the KV cache. The increase depends on the model architecture, precision used for the cache, number of layers, and runtime implementation, so a universal “bytes per token” figure would be misleading. A 128k maximum context is an architectural limit; it does not mean a given computer can run 128k tokens with comfortable speed or capacity.
Concurrent sessions multiply cache pressure. A server handling four conversations can require substantially more memory than an interactive desktop handling one, even with identical weights. Set a context limit and concurrency limit in the runtime, then test whether the remaining memory stays stable during the longest prompts you expect.
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- Use a smaller quantized file. Lower-bit weights reduce capacity requirements, but verify that the runtime supports the format and that the model provider documents its characteristics.
- Lower the context limit. This reduces KV-cache demand and is often the least disruptive fix for a model that loads but fails on long prompts.
- Reduce concurrency or batch size. Fewer simultaneous sequences leave more memory for each active request.
- Offload layers to system RAM. Partial CPU offload can make a model load when VRAM alone is insufficient. Expect performance to depend on the hardware link and engine; no universal speed multiplier applies.
- Close competing applications. Browsers, game overlays, and other GPU programs can consume VRAM or system RAM before inference starts.
- Select a smaller model family. This changes capability as well as memory use, so compare the task requirements rather than choosing by parameter count alone.
If the runtime reports an out-of-memory error during generation rather than loading, suspect context, concurrency, or temporary buffers before assuming the weight file is too large.
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How should you compare two local-AI computer configurations?
Compare the complete workload, not just the headline memory number.
| Comparison axis | Questions to answer |
|---|---|
| Capacity | How much VRAM, system RAM, or unified memory remains after the operating system and applications start? |
| Model and format | What is the exact parameter count, quantization, data type, and downloaded file size? |
| Context and concurrency | What context length, number of simultaneous sessions, and batch size are required? |
| Placement | Must all layers run on the GPU, or is CPU inference or partial offload acceptable? |
| Platform and runtime | Does the inference engine support the model format, GPU architecture, drivers, and operating system? |
| Performance goal | Is the priority that the model loads, or a specific generation rate? Capacity figures alone do not establish speed. |
This framework prevents a common mistake: selecting a card because its VRAM tier sounds large, then discovering that the intended context, runtime, or format is unsupported.
What is the practical bottom line for choosing RAM and VRAM?
Choose hardware from the model outward. Obtain the exact quantized file, identify your context and concurrency targets, decide whether full GPU placement is necessary, and reserve headroom for cache, runtime buffers, and the operating system. Use the 16 GB gpt-oss-20b and 80 GB gpt-oss-120b figures as named reference points, and NVIDIA’s 8–16 GB, 16–24 GB, and 24–48 GB bands as workflow guidance—not as universal guarantees. When the documentation leaves overhead unspecified, more capacity or a lower context target is safer than treating the file size as a minimum.
Frequently Asked Questions
Does a larger model always need more VRAM than a smaller one?
Usually its weights require more storage, but parameter count alone does not determine the working set. Quantization, architecture, context cache, and runtime buffers can make two models with similar counts behave differently, so compare their actual files and deployment guidance.
Can I use system RAM to make up for missing GPU memory?
Some runtimes support partial CPU offload, placing part of the model or its buffers in system RAM. That can make an otherwise oversized model load, but transfers between memory pools change performance and depend on the engine and hardware.
Why did my model load successfully and then run out of memory?
Loading may fit the weights while generation adds KV-cache entries and temporary buffers. Long prompts, high concurrency, or a large batch can consume the remaining capacity after initialization.
Is an 80 GB GPU required for every model with about 120 billion parameters?
No. The 80 GB figure is OpenAI’s efficient single-GPU example for gpt-oss-120b. Another model with a different format, architecture, context target, or offload strategy can have a different requirement.
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What should I check after a driver or runtime update?
Reconfirm that the selected quantization and GPU backend are still supported, then check usable memory and test the longest expected context. Software changes can alter buffer allocation even when the model file has not changed.
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