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VRAM is the memory that most directly limits local AI performance and model size. Keep the model weights, attention KV cache, and runtime buffers in VRAM when possible. System RAM provides a larger but slower fallback for CPU inference, model loading, unified-memory graphics, and CPU-offloaded layers. An NVMe SSD stores model files and datasets and makes loading or switching faster; it does not substitute for inference memory.
Choose hardware from the largest quantized model and context length you actually plan to run. Then leave headroom for the operating system, runtime, KV cache, and other applications.
The short answer: what each type of memory does
Local inference uses the three resources for different jobs:
- GPU VRAM: the GPU’s immediate workspace. It is the best place for model weights, the KV cache created by long conversations, and temporary runtime buffers. VRAM capacity sets the practical limit for running a model entirely on the GPU.
- System RAM: the CPU’s main memory. It can hold models for CPU inference, stage files while they load, support layers that are offloaded from a GPU, and supply memory to integrated graphics in a unified-memory design. It is more flexible than fixed VRAM, but moving work or data there is slower.
- NVMe SSD: persistent storage. It keeps model files, adapters, datasets, and multiple runtime installations. A fast NVMe drive reduces startup and model-switching time, but it is not an execution pool and cannot turn into VRAM during token generation.
For a fully GPU-resident model, buy the GPU first and size its VRAM from the model’s quantization and context length. Add system RAM when you need CPU fallback, CPU offload, unified-memory graphics, or more loading headroom. Add NVMe capacity when your model library and datasets no longer fit comfortably.
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Why VRAM sets the fully-GPU model limit
When a runtime places every layer on a discrete GPU, the selected model must fit in that GPU’s VRAM along with non-weight allocations. NVIDIA’s troubleshooting documentation states that a GPU out-of-memory error occurs when the model needs more VRAM than the GPU provides. In practice, “the model size” is only the starting point: weights, the KV cache, temporary workspaces, and the runtime itself all compete for the same pool.
Model parameters consume memory according to their numerical format. Lower-precision formats use fewer bytes per parameter, allowing a larger model in the same card. Higher precision can improve numerical fidelity for some workloads but reduces the available capacity for model size and context.
Reference memory figures
| Model or deployment example | Format or use case | Memory figure | How to interpret it |
|---|---|---|---|
| Llama 2 7B | FP16 illustrative calculation | 28 GB minimum memory | NVIDIA’s example uses 7 billion parameters, 2 bytes per parameter, and a 2× overhead factor. It is a planning illustration, not a guarantee for every runtime. |
| Llama 8B | NVIDIA NIM deployment guidance | Approximately 15 GB | A rough deployment guideline accessed in 2026; hardware and configuration can change the requirement. |
| Llama 70B | NVIDIA NIM deployment guidance | Approximately 131 GB | A rough deployment guideline accessed in 2026, not a universal card specification. |
| Llama 70B | BF16 weights alone | Approximately 140 GB | NVIDIA’s figure is for weight memory and excludes additional KV-cache and runtime buffers. |
The 131 GB deployment guideline and the approximately 140 GB BF16 weight figure are not contradictory specifications. They describe different configurations and accounting methods. A deployment estimate can include implementation choices, while a weight-only estimate excludes the memory needed for context and other buffers.
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A card advertised with exactly the estimated weight requirement leaves no safe space for a prompt, conversation history, CUDA or other runtime allocations, desktop use, or another application. Treat the estimate as a floor. A practical purchase should leave visible unused VRAM after the model loads at your target context length. If a model loads only at a short context, it is not a comfortable fit for long documents or multi-turn chats.
Context length and the KV cache can cause a late out-of-memory error
The model’s weights are allocated first, but each generated or supplied token can increase the attention KV cache. NVIDIA’s OOM guidance describes a common failure: weights load successfully, then KV-cache allocation fails because the requested context is too large for the remaining VRAM.
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This explains why the same model may start with a short prompt and fail with a long document, or why a chat that worked yesterday fails after its history grows. Context limits are therefore a memory setting, not merely a quality setting.
How to plan context headroom
- Choose the maximum context length you will actually use, not only the model’s advertised maximum.
- Load the model at that context and test a prompt representative of your workload.
- If the runtime reports a KV-cache or allocation error, reduce context before assuming the model weights are incompatible.
- Keep other GPU applications closed while measuring available headroom; a browser, game, image model, or display workload can consume part of VRAM.
Increasing context can therefore require more VRAM even when the model file and quantization stay unchanged. A larger card may be a better solution than repeatedly lowering context if long documents are central to your use case.
Quantization trades precision for capacity
Quantization reduces parameter precision so the weights occupy less memory. NVIDIA describes this as a direct reduction in memory requirements; AMD identifies 4-bit Q4 K M as a common quantization format for local large-language-model use.
A 4-bit file is not automatically “one quarter the size” of every FP16 deployment. Metadata, scales, tensors that remain at higher precision, runtime buffers, and the KV cache add overhead. Different quantization families also make different quality and speed trade-offs. Use the actual file size and the runtime’s memory estimate as a starting point, then reserve room for context.
When a lower-bit model is the right compromise
- Use a lower-bit quantization when the unquantized or higher-precision weights do not fit your VRAM tier.
- Prefer a quality level that still meets your task: coding, extraction, translation, and creative writing can react differently to quantization artifacts.
- Compare files by quantization, parameter count, and context requirement together. A smaller high-precision model can be easier to run than a larger low-bit model with a long context.
- Do not count an SSD’s free space as model capacity. A quantized file can fit on disk while still exceeding available VRAM at runtime.
What system RAM contributes
System RAM is the flexible second pool. A CPU-only runtime can keep the entire model there and execute inference on the processor. A GPU runtime can keep some layers or staging buffers in RAM when VRAM is insufficient. Loading a model from disk also requires RAM in many software paths before or while data is transferred to the accelerator.
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CPU inference and offloaded layers
If the model does not fit in VRAM, runtimes can place part of it in system RAM and send work between the CPU and GPU. This is a capacity fallback: it may let a model run that otherwise would not start, but transfers across the CPU-GPU path add latency. Vulkan documentation notes that moving resources from VRAM into system RAM is much slower than keeping them in VRAM.
Offload can be useful for occasional experiments or for a model that is only slightly larger than the card’s capacity. It is a poor substitute for sufficient VRAM when you need consistently responsive generation. The exact slowdown depends on model architecture, the number of offloaded layers, CPU speed, memory bandwidth, interconnect, context, and runtime implementation, so no single tokens-per-second penalty applies to every setup.
Unified-memory graphics
Integrated graphics share physical system memory rather than having a separate VRAM pool. AMD’s 2025 guidance describes Variable Graphics Memory on Ryzen AI systems: a BIOS-level allocation can trade spare system RAM for dedicated graphics memory, while reducing the RAM available to the CPU by the same amount.
| Example unified-memory tier | AMD’s 2025 framing |
|---|---|
| 16 GB | Entry tier in a progression of local-model and graphics-memory allocations |
| 32 GB | Mid-level tier in the same progression |
| 64 GB | Larger tier for progressively larger local models and graphics allocations |
| 128 GB | Largest example tier in AMD’s progression |
These figures describe example system-memory tiers, not a promise that a particular model will achieve a particular speed. Allocating more to graphics can make a larger model addressable, but it simultaneously removes that memory from CPU tasks. Leave enough RAM for the operating system and the application that feeds the model.
What an NVMe SSD contributes
An NVMe SSD is where the model library lives. It stores downloaded weight files, quantized variants, adapters, tokenizer data, datasets, logs, and multiple runtime environments. Its capacity determines how many models you can keep locally without deleting and redownloading them.
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Fast storage matters most during loading. NVIDIA’s storage guidance says fast storage is useful when loading large datasets into GPU memory is important. A supplementary local-AI hardware guide likewise reports faster startup and model switching with NVMe, while token generation remains primarily constrained by compute and memory bandwidth.
What an SSD cannot do
- It cannot serve as a high-speed replacement for VRAM during normal layer execution.
- It cannot prevent a KV-cache allocation failure once the runtime needs more working memory than the GPU has.
- It cannot make a CPU-offloaded model behave like a fully GPU-resident model.
- It can reduce the wait before inference begins, but it does not establish a universal generation speed.
Choose capacity from the total size of the model files and datasets you intend to retain. Choose higher sustained read performance when you frequently switch large models or reload datasets. Do not buy a larger SSD in the expectation that it will increase the maximum context that fits in VRAM.
VRAM, RAM, and NVMe compared
| Resource | Model-weight capacity | Context and KV-cache headroom | Bandwidth and latency role | CPU offload | Persistence | Upgrade behavior |
|---|---|---|---|---|---|---|
| Discrete GPU VRAM | Defines how much can remain on the GPU at once | Best headroom when weights and cache share a large pool | Closest memory to the GPU; preferred for active layers and cache | Can receive layers from system RAM, but fixed VRAM still limits the fully resident portion | Volatile; contents disappear when the workload ends | Usually tied to the selected GPU; adding capacity generally means replacing or adding a card |
| System RAM | Large fallback pool for CPU inference, staging, and offload | Can hold CPU-side data, but transfers to the GPU are slower | Slower path for GPU resources than VRAM | Yes; it is the main offload and CPU-inference pool | Volatile; contents disappear when the workload ends | Often expandable with compatible memory modules, subject to the platform’s limits |
| NVMe SSD | Stores model files rather than executing them | Does not provide runtime cache headroom | Improves reads during loading and switching, not steady token generation | No; it is not a practical execution-memory fallback | Persistent; retains models and datasets while powered off | Usually scalable by replacing or adding a drive if the system has a compatible slot |
A step-by-step way to size a local AI system
- List the largest model and context you need. Record parameter count, intended quantization, maximum prompt or conversation length, and whether you need image or other multimodal inputs. The largest occasional model may determine the GPU purchase even if most sessions use smaller models.
- Estimate weight memory. Start with the model publisher’s file size and runtime estimate. For a high-precision sanity check, NVIDIA’s Llama 2 7B FP16 illustration reaches 28 GB by applying 2 bytes per parameter and a 2× overhead factor. Treat that as an illustrative floor, not a universal calculator.
- Add context and runtime headroom. Reserve VRAM for the KV cache, temporary buffers, the operating system’s display use, and other applications. Test the target context instead of checking only whether the weight file appears to fit.
- Select the VRAM tier. Prefer a GPU with enough unused VRAM after loading the model. If your choice is between a faster card with too little VRAM and a slower card that fits the intended workload, the latter can avoid constant offload or context reductions.
- Size system RAM for fallback. Add RAM when you plan CPU inference, layer offload, integrated graphics, large model-loading operations, or several applications alongside the runtime. In a unified-memory system, account for the amount reserved for graphics because it reduces CPU-available RAM.
- Size the NVMe library. Add the storage consumed by every model variant, adapter, tokenizer, dataset, and runtime you expect to keep. Include room for downloads and temporary copies so updates do not force immediate cleanup.
- Validate with the real runtime. Load the exact quantized file in the application you use, set the intended context, and observe VRAM and RAM usage. Ollama, LM Studio, and other runtimes can make different allocation choices, so a result from one is not a guarantee for another.
Planning for Ollama, LM Studio, and similar runtimes
The same hardware principles apply whether a runtime exposes a simple model command or a graphical model manager. Look for controls or status information showing GPU layers, CPU layers, context length, and memory use.
If the runtime offers a GPU-layer setting
- Begin with automatic placement if it is available.
- Confirm how many layers were placed on the GPU and how much VRAM remains after loading.
- If the model does not fit, lower the GPU-layer count only after confirming that the resulting CPU transfer rate is acceptable for your task.
- Reduce context length if the failure occurs after weights load and the error mentions KV cache or allocation.
If the runtime offers no explicit offload control
Its loader may decide placement from available VRAM and RAM. Close other GPU applications, reduce context, and retry with a smaller quantization. If it still starts only with heavy system-RAM use, treat that as a CPU-offloaded configuration rather than a full-GPU result.
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Troubleshooting common memory failures
“Out of memory” while loading the model
- Check the model’s quantization and actual file size.
- Close applications using VRAM and restart the runtime.
- Choose a smaller model or lower-bit quantization.
- Use CPU offload only if the resulting speed is acceptable and system RAM is sufficient.
Weights load, then a long prompt fails
This pattern points to insufficient remaining VRAM for the KV cache. Lower the context length, shorten the conversation, or select a GPU with more headroom. Replacing the SSD will not change this allocation.
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The model runs but generation is unexpectedly slow
Check whether layers were offloaded to system RAM, whether unified-memory graphics are competing with CPU tasks, and whether the model is actually using the intended GPU. Slow disk reads explain a long startup, not necessarily slow token generation after the model is resident. Generation speed also depends on model architecture, quantization, CPU, GPU, context, and interconnect; the available evidence does not support one universal tokens-per-second figure.
The computer becomes unstable during loading
Insufficient system RAM can force the operating system to page aggressively while the runtime stages a model. Close other applications, reduce concurrent workloads, and add RAM if CPU inference or offload is part of your normal plan. A larger SSD does not remove this RAM pressure.
The drive fills even though only one model is active
Model managers often retain multiple quantizations, older versions, cache files, and datasets. Remove unused variants only after confirming which file the runtime references, and keep enough free space for future downloads and temporary extraction.
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Which upgrade should come first?
Buy more VRAM when
- Your target model’s weights fit only by using substantial CPU offload.
- You need a longer context and the KV cache consumes the remaining capacity.
- You want predictable GPU-resident performance for repeated sessions.
- You are choosing between two GPUs and the larger-VRAM option matches your model roadmap.
Buy more system RAM when
- You run models on the CPU or expect to offload layers.
- Loading a model competes with browsers, development tools, or datasets.
- Your integrated GPU uses a large Variable Graphics Memory allocation.
- You routinely run several models or data-processing jobs in parallel.
Buy a larger or faster NVMe SSD when
- Your model library, datasets, and runtime files no longer fit with comfortable free space.
- You switch between large models often and startup time is a bottleneck.
- You are moving models from a slower drive and want faster reads during loading.
These upgrades solve different bottlenecks. More RAM cannot increase a discrete card’s fixed VRAM, and more SSD space cannot provide working memory. Match the purchase to the failure you observe rather than treating all gigabytes as interchangeable.
Worked planning examples
A small model on a discrete GPU
Suppose your largest planned workload resembles the Llama 8B NIM example, for which NVIDIA lists approximately 15 GB as a rough deployment guideline. A GPU with exactly that capacity leaves little room for context, runtime buffers, and display use. Select a card with additional headroom, verify the intended context in the actual runtime, and keep system RAM available for loading and normal desktop work. NVMe capacity matters mainly if you keep several quantized variants.
A 7B model in FP16
NVIDIA’s illustrative Llama 2 7B FP16 calculation reaches 28 GB after accounting for 2 bytes per parameter and a 2× overhead factor. A card below that figure cannot provide the illustrated fully GPU-resident configuration. A card near the figure may still fail at a long context because the calculation does not represent every KV-cache and runtime allocation. Quantization or a shorter context can change the requirement, but you should validate the exact file.
A 70B-class model
The approximately 131 GB NIM guideline and approximately 140 GB BF16 weight estimate show why a single consumer GPU is often not the natural fit for this class of model. The BF16 number excludes KV cache and other buffers, so a multi-GPU or CPU-offloaded design needs additional planning for system RAM, interconnect behavior, and the performance cost of moving data. Do not infer that an SSD with hundreds of gigabytes free can execute the model.
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Token generation is affected by the model architecture, quantization, context length, CPU, GPU, memory bandwidth, interconnect, placement of layers, and runtime implementation. Storage primarily changes how long you wait before the model is ready. Because those variables differ between systems, a single tokens-per-second claim would be misleading without a controlled cross-platform benchmark. Use your own target model and prompt to test the configuration you intend to keep.
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