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There is no single memory requirement for a local large language model (LLM). Start with the model file size at your chosen quantization, then add memory for the key-value (KV) cache, runtime buffers, the operating system and other GPU workloads. Context length and simultaneous requests can increase the total substantially. Decide whether the model will run in GPU VRAM, system RAM, or a split CPU/GPU configuration. A model that does not fit entirely in VRAM may still run through CPU or hybrid execution, but usually with a different speed profile.
As a first estimate, treat the published weight size as a floor rather than a specification. For example, a Q4_K_M 7–8B model is roughly 4.8 GB in one August 2026 sizing guide, but the same guide advises reserving another 1–3 GB for cache, buffers and desktop use. Your exact requirement depends on the model file, runner, context and workload.
What “memory” means when you run an LLM
Local inference can consume several kinds of memory at once. Confusing these categories is the main reason a model that appears to fit on paper fails at runtime.
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The weights contain the learned parameters. Their size is determined by the parameter count, architecture and file format. A 70B model therefore needs much more storage and working memory than an 8B model, but parameter count alone cannot predict an exact footprint because formats and runtime implementations differ.
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KV cache
During generation, the runner stores attention keys and values for the active context. This KV cache grows as the context gets longer and can become a large part of the working set. A short chat and a 128,000-token document session can use very different amounts of memory even with the same model file.
Runtime and operating-system overhead
Runners allocate compute buffers and other temporary memory. Your desktop environment, display driver, browser, background applications and any other loaded model also compete for available VRAM or system RAM. The memory left after those users—not the headline capacity printed on a graphics card—is what the model can actually use.
A useful mental model is total working memory ≈ model weights + KV cache + runtime buffers + operating-system and application use. It is an estimate, not a universal formula: implementations may allocate memory differently.
Model size and quantization set the baseline
Quantization stores weights with fewer bits, reducing the model file and usually the memory needed to hold it. The llama.cpp project documents 1.5-, 2-, 3-, 4-, 5-, 6- and 8-bit integer quantization formats, with quality and performance trade-offs that vary by model and runtime. A smaller file is not automatically equivalent in output quality to the original weights, so choose a format that meets your accuracy needs rather than selecting the smallest number blindly.
| Model example | Original size | Q4_K_M size | Source qualification |
|---|---|---|---|
| Llama 3.1 8B | 32.1 GB | 4.9 GB | Figures shown in the llama.cpp quantization documentation; publication year is not stated on that forked page. |
| Llama 3.1 70B | 280.9 GB | 43.1 GB | Figures shown in the llama.cpp quantization documentation; publication year is not stated on that forked page. |
These are model-file examples, not promises that a 4.9 GB file needs only 4.9 GB of VRAM. The quantization documentation is available at llama.cpp’s quantization README. General runtime and backend details are maintained in the llama.cpp README.
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Approximate memory tiers by model size
The following estimates come from an OllamaLab guide published August 1, 2026 and updated August 18, 2026. They describe Q4_K_M weight sizes and an illustrative VRAM tier, not minimum specifications. The guide says to budget an additional 1–3 GB for KV cache, compute buffers and desktop use.
| Model size | Approximate Q4_K_M weights | Illustrative VRAM tier | How to interpret it |
|---|---|---|---|
| 7–8B | About 4.8 GB | 8 GB | Can suit modest context; headroom may be tight. |
| 13–14B | About 8.4 GB | 12 GB | Described as a stretch for short context; 16 GB is more comfortable. |
| 27–32B | About 19 GB | 24 GB | Described as a comfortable Q4_K_M choice. |
| 70B | About 42 GB | 48 GB | Presented as a moderate-context tier. |
Hardware, exact quantization, context length, runner and concurrent requests can move the result above or below these tiers. Read the guide’s assumptions at OllamaLab’s VRAM sizing guide before treating a row as a purchase target.
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Longer context
Every additional token retained in the active prompt can enlarge the KV cache. If you increase a model’s context setting, leave room for that cache instead of sizing only for the weights. The Ollama project states that Flash Attention “is a feature of most modern models that can significantly reduce memory usage as the context size grows.” Whether it is available and beneficial depends on the model and backend.
Ollama also documents quantized KV-cache options. They can reduce memory consumption, with a corresponding precision trade-off, so check the runner’s current settings and model support rather than assuming every model uses the same cache format.
Parallel requests
Two simultaneous conversations need more cache than one. Ollama’s FAQ explains that required RAM scales with the parallel-request count multiplied by context length. A configuration sized for one 8,000-token chat may therefore fail when four requests are allowed, even though the model weights have not changed.
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Multiple loaded models
Keeping several models resident consumes their weight memory at the same time. Unload models you are not using, or configure the runner to limit how many remain loaded, before diagnosing a single-model fit problem.
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VRAM, system RAM and hybrid execution
Full GPU placement
When all weights, cache and buffers fit in available VRAM, GPU inference avoids moving most model data across the system bus. You still need capacity left after the display stack and other GPU applications. Full placement is often the simplest way to obtain predictable GPU performance, but fitting the weights alone is not enough.
CPU inference in system RAM
A CPU runner can keep the model in system memory. This removes the VRAM ceiling, but speed depends heavily on CPU capability and memory bandwidth. The model may be usable on a machine with no suitable discrete GPU, while generation is slower than a comparable full-GPU placement.
Hybrid CPU/GPU placement
llama.cpp supports offloading part of a model to the GPU while keeping the remainder in system RAM. Ollama can also distribute a model across available GPUs. This makes models larger than one GPU’s VRAM runnable, but placement and transfer costs vary by hardware and runner; do not assume hybrid execution matches the speed of a model that fits entirely in VRAM. The implementation options are described in the llama.cpp README and placement behavior in the Ollama FAQ.
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How to estimate your own requirement
- Identify the exact model file. Record parameter count, file format and quantization (for example, Q4_K_M). Use the file’s actual size rather than a generic “8B” label.
- Choose the target context. Write down the maximum tokens you expect in a prompt and conversation. Include document retrieval or code windows if you will use them.
- Count simultaneous work. Decide how many requests or users can run at once. Multiply the context demand accordingly when the runner documents that scaling behavior.
- Choose placement. Check available VRAM after the operating system and other GPU workloads. If it is insufficient, check system RAM and whether your runner supports CPU offload or multi-GPU placement.
- Add headroom. For a rough Ollama-oriented estimate, add 1–3 GB beyond Q4_K_M weights for cache, buffers and desktop use. Treat this as the cited guide’s estimate, not a guarantee for every architecture.
- Test the real configuration. Load the exact model, set the intended context and concurrency, then observe VRAM and RAM use during a long prompt and generation. A short test that never fills the context can hide the eventual cache requirement.
Worked planning examples
7–8B model on an 8 GB GPU
A Q4_K_M file around 4.8 GB leaves only a few gigabytes for cache, buffers and the desktop. It can be practical for modest context and one request, but a long context, multiple conversations or another GPU workload can force partial offload or an out-of-memory error. Lowering context or moving some layers to CPU are the first adjustments.
13–14B model on 12–16 GB
The guide places Q4_K_M weights near 8.4 GB. Twelve gigabytes is characterized as a short-context stretch, while 16 GB provides more room for cache and background use. If your application needs long documents or parallel users, validate with a larger margin or use hybrid placement.
27–32B model on 24 GB
At roughly 19 GB of Q4_K_M weights, a 24 GB GPU is described as a comfortable tier, but “comfortable” still assumes a particular context and workload. Reserve capacity for the cache and avoid treating the tier as a fixed maximum context.
70B model
The cited guide estimates about 42 GB of Q4_K_M weights and identifies 48 GB as a moderate-context tier. A 24 GB card cannot hold this example entirely, so CPU or multi-GPU placement is required. Expect speed and usable context to depend on how much of the model remains offloaded.
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Ways to reduce memory pressure
- Use a smaller or more efficient quantization after checking the quality impact for your task.
- Reduce the maximum context when your application does not need long histories.
- Limit parallel requests so cache growth does not multiply unexpectedly.
- Enable Flash Attention or a quantized KV cache when your model and runner support them; review the documented precision trade-offs.
- Unload idle models and GPU applications to return their memory to the workload.
- Use CPU or hybrid offload when capacity matters more than maximum tokens-per-second.
Choosing hardware without overpromising a fit
For GPU inference, compare the VRAM that remains available, not only the card’s advertised capacity. A graphics card with 24 GB VRAM can be a useful category for models around the 19 GB Q4_K_M tier, but exact fit still depends on cache, context and runtime overhead. Higher capacity is not a universal answer: memory bandwidth affects speed after the model fits, and the sources do not establish a single best card.
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For a purchase decision, write down the intended model, quantization, context length, number of simultaneous requests and whether slower hybrid execution is acceptable. Then compare:
- available VRAM or unified memory after the operating system and other workloads;
- system RAM for CPU or hybrid placement;
- memory bandwidth, which influences generation speed;
- the cost and power limits of the hardware in your region.
Storage capacity is separate from runtime memory. Keep enough disk space for the model files you plan to download, but do not mistake free storage for VRAM or RAM available during inference.
Common failure symptoms and fixes
The model will not load
Check the actual quantized file size, then add cache and runtime headroom. Close other GPU applications, reduce context, select a smaller quantization or move layers to system RAM. If several models are loaded, unload the idle ones.
It loads but crashes on long prompts
The weights fit, but the KV cache does not at the larger context. Lower the context limit, reduce parallel requests, or use a supported Flash Attention or quantized-cache option.
It runs but is unexpectedly slow
Inspect placement. A model that spills to CPU or repeatedly transfers data between CPU and GPU can be usable but much slower than full-GPU execution. Check how many layers are offloaded and whether another process is consuming VRAM.
Different runners report different memory use
Runtimes choose different kernels, buffer sizes, cache formats and placement strategies. Compare measurements only with the same model file, context, concurrency and runner settings.
Bottom line
Use weight size to choose a starting class, not a final specification. Add KV-cache and runtime headroom, account for context and concurrency, and decide where the memory will live. Then load the exact model under the exact workload you intend to run. The llama.cpp and Ollama documentation can change as runtimes evolve, so verify current settings before committing to hardware.
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