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There is no universal RAM or GPU minimum for local AI. The right specification depends on the model, quantization format, context length, number of simultaneous requests, runtime, and the speed you expect. For GPU inference, available VRAM is usually the first fit constraint: it must hold the model weights plus context/KV-cache data and runtime overhead. System RAM or Apple unified memory can run models too, but moving work onto the CPU can greatly reduce throughput.
Start with the workload, not a RAM number
“Run an AI model locally” can mean a small chat model answering one prompt at a time, a coding assistant with a long context window, image generation, speech recognition, or several users sharing one server. Those workloads do not have the same memory or compute requirements.
Before choosing hardware, write down five constraints:
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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Repair Windows errors before they cause bigger problemsFix Now →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →- Model: the exact model and parameter size, not just a family name.
- Quantization: the file’s precision and quantization method.
- Context: how many tokens the model must retain in one request.
- Concurrency: how many requests may run at once.
- Performance target: acceptable time to first token and generation speed.
A machine that can load a model is not necessarily a machine that can run it responsively. A model may fit only after reducing context, lowering precision, or moving some layers to system memory; each workaround changes speed, quality, or both.
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What memory must hold
Model weights
The downloaded model file primarily represents its weights, but that file size is not a complete hardware requirement. The runtime needs additional allocations while loading and executing the model. A model that appears to fit exactly in a GPU’s advertised capacity can fail when the context cache and backend overhead are added.
Context and the KV cache
During generation, the runtime stores key-value (KV) cache data for the conversation’s context. Longer prompts and larger context windows consume more memory. If you increase the context limit, leave additional headroom rather than treating the model’s weight size as a fixed total.
Ollama documents a direct relationship between parallelism and context: memory use scales with OLLAMA_NUM_PARALLEL multiplied by OLLAMA_CONTEXT_LENGTH. Raising either setting can increase the amount of context data held at the same time. A server configured for several simultaneous requests therefore needs substantially more memory than a single-user desktop running the same model and context.
Runtime and operating-system overhead
CUDA, ROCm, Metal, Vulkan, or a CPU backend all reserve memory for buffers and execution. Your operating system, desktop, browser, and other applications also compete for system or unified memory. Plan usable capacity, not the number printed on a product box.
VRAM is the practical GPU fit constraint
For a dedicated GPU, ask whether the model weights, KV cache, and runtime allocations can remain in VRAM. Keeping the workload on the GPU generally avoids the latency of repeatedly transferring data through system memory and the CPU, although the exact behavior depends on the backend and model.
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VRAM capacity and GPU compute power solve different problems:
- Capacity determines fit: insufficient VRAM can force offloading, reduce context, or prevent loading.
- Compute determines throughput: a faster GPU can generate tokens more quickly once the workload fits.
- Memory bandwidth and backend support affect both: two cards with the same VRAM can deliver different results.
Do not choose a card solely because a model’s download is smaller than its VRAM. Reserve room for context, concurrency, and the runtime. If your workload must support long documents or multiple users, those allocations can become the dominant constraint.
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How quantization changes the requirement
Quantization stores weights at lower precision so they occupy less memory. That can make a model practical on a smaller GPU or on a system that would otherwise need CPU offload. It is not a universal conversion from “large” to “small,” however.
Quality and speed vary with the model, quantization method, backend, and task. NVIDIA’s local-inference guidance recommends Q4_K_M for llama.cpp workflows and NVFP4 for vLLM or PyTorch workflows. Those recommendations are tied to those software paths; a file in one format is not automatically supported or optimal in another.
KV-cache quantization can reduce context memory as well. Ollama notes that the quality effect depends on the model and task, so test representative prompts before adopting it for a production assistant or coding tool. A lower-memory configuration may be worthwhile for experimentation but unsuitable when exact output quality matters.
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A repeatable way to size a local AI system
- Choose the exact model and file. Record the parameter size, quantization format, and runtime format. “A 14B model” is not enough information to predict memory use.
- Set the real context length. Use the longest prompt and response window you actually need. Do not size for a short demonstration if your application will process long documents.
- Set concurrency. Decide whether one person, a background job, or several users will generate at once. In Ollama, review
OLLAMA_NUM_PARALLELandOLLAMA_CONTEXT_LENGTHtogether because their product drives context allocation. - Add headroom. Leave capacity for KV-cache data, runtime buffers, and the operating system. A configuration that uses every available megabyte is vulnerable to small changes in prompt length or software behavior.
- Pick the execution path. Decide whether the model will stay on a dedicated GPU, run on the CPU, use Apple unified memory, or be split between GPU and system memory.
- Check backend compatibility. Confirm the operating system, GPU architecture, driver, model format, and inference runtime before buying hardware.
- Define an acceptable speed. Measure or estimate time to first token and sustained generation, not just whether the model launches. If your application needs several simultaneous requests, test that load rather than a single prompt.
- Validate with your prompts. Context size, tool calls, output length, and concurrency can change memory use. A short chat test cannot validate a long-context coding or retrieval workload.
Realistic hardware routes
| Route | Memory planning | Speed and trade-offs | Compatibility considerations |
|---|---|---|---|
| Dedicated GPU | Size VRAM for weights, context/KV cache, runtime allocations, and concurrent requests. | Keeping the workload on the GPU can avoid slower CPU/system-memory involvement; compute capability, bandwidth, and backend also matter. | Check GPU architecture, driver, operating system, and runtime support before purchase. |
| CPU with system RAM | Can run models without a large GPU, provided system RAM can hold the model and runtime. | Usually trades lower hardware cost for lower throughput, especially with larger models or long contexts. | Verify that the chosen runtime supports the CPU instruction set and model format. |
| Apple Silicon unified memory | Model and application share one memory pool; count the operating system and other applications against total capacity. | Supported frameworks can use unified memory without separate VRAM, but capacity alone does not establish a particular tokens-per-second result. | Check Metal or other framework support for the exact model and macOS software stack. |
What published examples actually show
14B model on a 16GB GPU
In a Windows Central hands-on test, author Ben Wilson ran DeepSeek-R1 14B on an RTX 5080 with 16GB of VRAM at up to a 16K context. When the context was increased far enough that part of the workload moved into system RAM and the CPU, reported output fell from about 70 tokens per second to 19 tokens per second.
Those figures describe one computer, runtime, model build, prompt, and test method. They demonstrate the cost of spilling out of VRAM; they are not a universal performance guarantee or a minimum specification for every 14B model.
20B model and a 24GB target
The same report describes a 20B gpt-oss model with a 14GB file and says 24GB of VRAM was the author’s preferred capacity for using it more freely. The preference allows room beyond the file itself for context and runtime allocations. It should be treated as a workload-specific judgment, not a rule that every 20B model requires 24GB.
A 24GB card, including an example such as an NVIDIA GeForce RTX 3090 24GB, may therefore be relevant when a workload benefits from more VRAM. Before buying, check power supply requirements, physical clearance, cooling, driver support, the model’s actual memory behavior, and current pricing.
Apple unified-memory example
In a March 30, 2026 preview, Ollama described an MLX-powered Apple Silicon workflow for a Qwen3.5-35B-A3B coding example and asked users to have a Mac with more than 32GB of unified memory. That is a condition for that named preview, not a minimum for all 35B models or all local AI tasks.
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A 2025 comparative preprint tested Apple Silicon inference on a 192GB M2 Ultra system. The machine is a test configuration, not a recommendation that ordinary users need 192GB. It shows why benchmark results must be read together with the tested model, context, software, and hardware.
Can you run local AI without a GPU?
Yes. CPU inference with system RAM is a valid route for smaller models, low request volumes, offline experimentation, and applications where response time is not critical. It can also serve as a fallback when a model exceeds available VRAM.
The trade-off is speed. In the RTX 5080 example, increasing context forced CPU and system-RAM involvement and reduced reported generation from roughly 70 to 19 tokens per second. CPU-only performance will vary by processor, memory bandwidth, quantization, thread settings, and runtime, so that example should not be used as a CPU benchmark.
If you choose CPU inference, prioritize enough system RAM for the complete workload and test the longest context you need. A model that technically runs but takes minutes to answer may not meet the practical definition of usable for your application.
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Apple unified memory: what to check
Apple Silicon combines CPU and GPU memory into a shared pool. That can be useful for local inference because supported software can draw from the total unified-memory capacity rather than a separate graphics-card allocation. The same pool is also used by macOS and other applications, so total capacity is not equal to memory available to the model.
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Ollama states that it supports GPU acceleration on Apple devices through the Metal API. It also documents Vulkan support alongside other GPU paths. Confirm that the runtime you plan to use supports the exact chip, operating-system version, model format, and acceleration framework; support differs between software projects.
Compatibility and driver checks
Ollama’s current GPU documentation lists NVIDIA support for compute capability 5.0 or newer and driver 550 or newer. For compute capabilities from 5.0 through 6.2, it specifies driver 570 or newer. These are Ollama-specific requirements and can change as its backend evolves.
For AMD, the same documentation specifies ROCm 7 on Linux and a ROCm 7/HIP7-capable stack on Windows for listed devices. It also documents Apple Metal and Vulkan GPU support. Do not assume that a card supported by one runtime will work identically in another; verify the runtime’s current matrix for your operating system and hardware.
NVIDIA’s guidance recommends selecting an inference backend after checking operating system, model format, GPU architecture and memory, API needs, and throughput target. This order prevents a common mistake: buying a high-capacity card that the chosen software cannot use effectively.
Common sizing mistakes
- Equating download size with VRAM need: weights are only one part of the allocation.
- Ignoring context: a short prompt can fit while the intended long-context workload cannot.
- Ignoring concurrency: several requests require more context storage than one request.
- Assuming all four-bit files are equivalent: quantization format, backend, quality, and speed differ.
- Using a benchmark as a guarantee: the published 70-to-19-token example was one tested configuration.
- Forgetting shared memory: Apple unified memory is also used by macOS and other applications.
- Skipping driver checks: architecture and driver requirements can determine whether acceleration works at all.
- Buying for peak capacity without a power or cooling plan: a physically incompatible or thermally constrained card cannot deliver its advertised performance.
A practical decision rule
For a single-user desktop assistant, start with the smallest quantized model that meets your quality needs and enough VRAM for its weights plus your real context. For long-context work, multiple users, or larger models, prioritize additional VRAM and headroom before chasing raw compute specifications. For occasional offline use, a CPU system with sufficient RAM may be adequate. For Apple users, compare total unified memory with the model, context, and applications that must remain open.
No generally applicable specification establishes one RAM or VRAM minimum for every local model. The reliable answer comes from sizing the exact model and workload, checking backend compatibility, and validating performance under the context and concurrency you will actually use.
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