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There is no single RAM requirement for running a local large language model (LLM). The amount depends on the exact model and quantization, whether it runs on the CPU or GPU, how much context it handles, and what else is using the computer. For CPU inference, system RAM is the main memory pool; for GPU inference, the relevant limit is usually the GPU’s VRAM. Some devices use unified memory shared by the system and graphics workload.
Use the model’s weight-file size as a starting point, not as the whole budget. You also need memory for the context-related KV cache, the runtime, and other active work. The practical answer is to check the exact model, runtime settings, and available memory on the device you plan to use.
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Memory needs are specific to a model and workload, not just to a label such as “7B” or “35B.” Two configurations can use different amounts of memory even when they refer to the same model family, because they may use different quantizations, context lengths, runtimes, or inference hardware.
Model weights and quantization
The model’s weights are the starting point for estimating memory. Quantization stores weights at lower precision to reduce their file size and memory footprint. Check the exact quantized checkpoint that the runtime will load; do not assume that one file-size figure applies to every version of a model.
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For example, a listed LLaMA-7B GGUF repository from TheBloke gives a Q4_K_M file size of 4.08 GB and a maximum RAM figure of 6.58 GB. Its Q8_0 version is listed at 7.16 GB for the file and 9.66 GB maximum RAM. The page does not state a publication year, and those figures describe those particular variants and setup—not a universal formula for all 7B models.
Inference placement: system RAM, VRAM, or unified memory
The memory pool matters as much as the model. Ollama distinguishes system memory for CPU inference from VRAM for GPU inference. A computer with a discrete GPU does not automatically combine its system RAM and GPU VRAM into one pool for a model; check where the runtime places the workload and what memory it can use.
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Context length, parallel requests, and runtime overhead
Weights are not the entire inference footprint. The context—the tokens the model can consider while responding—requires a KV cache, and a longer context can increase the cache allocation. Ollama documents that required RAM scales with OLLAMA_NUM_PARALLEL multiplied by OLLAMA_CONTEXT_LENGTH. This describes how those settings affect demand; it is not a fixed multiplier that can be applied to every model and machine.
The runtime itself and other active applications also need memory. Leave usable capacity for the operating system and the rest of your workload. The cited figures do not establish one universally correct buffer percentage, so a precise estimate needs to account for the actual device and what will be running alongside the model.
How do I estimate RAM for a particular local LLM?
Start with the exact checkpoint and intended workload, then compare their memory needs with the capacity available to the inference runtime. A model’s advertised size alone cannot tell you whether a specific computer will load it comfortably.
- Choose the exact model and quantization. Find the file size for the particular checkpoint and quantized variant you intend to use. A different quantization can change both file size and memory needs.
- Identify where inference will run. For CPU inference, check available system RAM. For GPU inference, check available VRAM. On a unified-memory device, consider that the same pool serves the relevant system and graphics work.
- Set the context and concurrency you actually need. A larger context or more parallel requests can raise memory demand. Estimate for your intended runtime settings rather than relying on a model’s smallest demonstration configuration.
- Account for the rest of the workload. Check how much memory is available—not merely how much is installed—and leave room for the operating system, runtime, and other applications.
- Check hardware and software limits. Confirm the runtime supports the device and that the computer can use the memory you plan to rely on. A system-RAM upgrade does not increase a discrete GPU’s VRAM.
As a concrete, platform-specific reference, NVIDIA’s 2026 llama.cpp playbook says its Qwen3.6-35B-A3B example needs about 30 GB of free memory and about 40 GB of free disk for the download and build artifacts. The memory and disk figures serve different purposes, and this example is not a minimum for all models in that size range. NVIDIA’s hardware guide also groups recommendations by target VRAM ranges; those recommendations apply to the hardware and recipes it describes, not as universal thresholds.
Can I run a local LLM with 16 GB of RAM?
Possibly, but 16 GB is not a reliable yes-or-no test. Whether it works depends on the model’s exact weights and quantization, the context length, other applications, and—if inference uses a GPU—the GPU’s available VRAM rather than system RAM alone.
Compare the available memory with the chosen configuration, not just with the model’s parameter count or file size. The TheBloke LLaMA-7B example illustrates why: its listed Q4_K_M file is 4.08 GB while its listed maximum RAM is 6.58 GB; the Q8_0 file is 7.16 GB while maximum RAM is 9.66 GB. Those particular figures are below 16 GB, but they do not establish that every 7B model, runtime, context, or concurrent workload will fit in a 16 GB computer.
If the model does not fit or leaves too little room for the rest of the computer, options include choosing a smaller model or a lower-footprint quantization, reducing the context or parallel workload, or using hardware with a more suitable memory pool. Which adjustment is available depends on the model and runtime. Do not interpret a successful load as proof that the setup has enough headroom for a larger context or more simultaneous requests.
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How much VRAM do I need for a local LLM?
There is no cross-model VRAM minimum. Match the exact model, quantization, context, and concurrency to the VRAM available to the runtime, and treat vendor recommendations as platform-specific guidance.
For GPU inference, system RAM and VRAM are separate considerations. A computer may have substantial system memory while still being constrained by its GPU’s VRAM. Confirm the GPU’s available memory and the runtime’s supported hardware before choosing a model. If the device uses unified memory, verify the total shared capacity and remember that other system and graphics tasks also draw on it.
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NVIDIA’s hardware guide organizes recommendations by target VRAM ranges, while its llama.cpp playbook gives a specific Qwen3.6-35B-A3B example requiring about 30 GB of free memory. These are useful reference points for the described NVIDIA platform and recipe; neither figure should be converted into a universal VRAM rule. NVIDIA describes llama.cpp as offering more control over speed, memory use, and hardware, while LM Studio and Ollama offer different ease-of-use paths.
Does a bigger context window use more RAM?
Yes. A longer context can require more memory for the KV cache, so the model’s weight file is not enough to estimate the full workload. Parallel requests can add further context-related demand: Ollama says required RAM scales with OLLAMA_NUM_PARALLEL multiplied by OLLAMA_CONTEXT_LENGTH.
Use the context and concurrency settings you expect to run when checking memory. A setup that loads the weights with a short context may not have enough available memory for a much longer context or multiple simultaneous requests. The cited documentation explains the direction and relationship of the scaling, but it does not provide one cache-size formula that applies to every model.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What should I compare before choosing between two computers?
Compare the full workload and the memory pool available to it, rather than comparing installed system RAM in isolation. A practical comparison includes the exact model and quantization, usable system RAM or VRAM, planned context and concurrency, and whether the machine can be upgraded.
| Compare | Why it matters |
|---|---|
| Exact model and quantization | Different checkpoint files and quantizations have different footprints; use the file the runtime will actually load. |
| Available system RAM or VRAM | CPU and GPU inference use different memory pools; unified-memory devices share a pool. |
| Context length and parallel requests | Context-related KV-cache allocation and concurrency can add memory demand beyond the weights. |
| Headroom for other work | The operating system, runtime, and other applications also need usable memory. |
| Speed and hardware control | Runtime choices offer different trade-offs; NVIDIA describes llama.cpp as providing more control over speed, memory use, and hardware, while LM Studio and Ollama provide different ease-of-use paths. |
| Upgradeability and compatibility | Installed capacity is useful only if the computer and runtime support the target memory and inference arrangement. |
If considering a desktop system-RAM upgrade, check the motherboard and processor’s supported memory type and capacity, plus the computer’s upgradeability. Adding system RAM will not solve a discrete GPU’s VRAM limit. Conversely, a GPU-focused setup should be assessed against its actual VRAM rather than assuming installed system memory can substitute for it.
What do the published figures actually tell you?
The available numbers are examples tied to specific models, devices, or software documentation. They show why a single headline minimum is misleading; they do not establish an amount that every local LLM needs.
| Source and example | Published figure | How to interpret it |
|---|---|---|
| NVIDIA, 2026 llama.cpp playbook: Qwen3.6-35B-A3B example | About 30 GB free memory; about 40 GB free disk for download and build artifacts | Specific to NVIDIA’s example; memory and disk are separate requirements. |
| NVIDIA, 2026: DGX Spark | 64 GB or 128 GB unified memory | Capacities listed for that platform, not universal LLM recommendations. |
| TheBloke’s LLaMA-7B GGUF variants; publication year not stated on the accessed page | Q4_K_M: 4.08 GB file, 6.58 GB maximum RAM; Q8_0: 7.16 GB file, 9.66 GB maximum RAM | Specific listed variants and setup; not a formula for all 7B models. |
| Ollama documentation, accessed 2026 | Required RAM scales with OLLAMA_NUM_PARALLEL × OLLAMA_CONTEXT_LENGTH |
Shows that concurrency and context settings affect demand; no universal memory amount is given. |
NVIDIA’s playbook states, “Memory determines what you can run.” That is a useful way to frame the decision: the relevant capacity is the memory available to the particular workload, not a single number detached from model, runtime, and device.
Frequently Asked Questions
Does a model’s download size equal the RAM it will use?
No. A download-size figure describes a file, while running the model also requires runtime and workload memory. The listed LLaMA-7B variants, for example, have maximum RAM figures larger than their respective file sizes.
Can I use my computer’s free disk space instead of RAM?
No. Disk space holds downloads and build artifacts, while system RAM, VRAM, or unified memory serves inference. NVIDIA’s Qwen example lists separate approximate figures for free memory and free disk.
Can I upgrade VRAM by adding system memory?
No; adding system RAM does not increase a discrete GPU’s VRAM. Check the computer’s supported upgrades and the runtime’s hardware support before buying memory.
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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesWhy does the same model show different memory figures in different guides?
Guides may refer to different quantized files, runtimes, devices, context settings, or workloads. Compare the configuration details behind each number before treating two estimates as directly comparable.
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