Local AI does not require an expensive workstation. But memory matters.
Two computers with similar CPUs can have very different local AI capabilities simply because one has more RAM or VRAM. The important question is not “What is the biggest model my PC can technically load?” It is: “What can my PC run comfortably enough to be useful?”
RAM and VRAM do different jobs
RAM is your computer’s general system memory. It is shared by the operating system, applications and—depending on your setup—some or all of the AI model.
VRAM is memory attached to your GPU. When a supported AI runtime can place model data in VRAM, inference can be dramatically faster than relying mainly on the CPU and system RAM.
A model does not always need to fit entirely inside VRAM. Tools such as llama.cpp can split work between the GPU and CPU, allowing part of a model to run from VRAM while the rest stays in system memory. That expands what can technically run, but usually at the cost of speed.
8 GB RAM: very small models
A computer with 8 GB of system RAM can experiment with local AI, but the available headroom is limited.
- 1B–3B models
- Low-memory quantizations
- Short context windows
- Lightweight chat or simple text tasks
The operating system also needs memory, so trying to consume nearly all 8 GB with a model is rarely a good experience. For serious everyday local AI use, 8 GB is increasingly restrictive.
16 GB RAM: a practical starting point
For many users, 16 GB RAM is where local AI starts becoming genuinely useful.
- 3B–8B quantized models
- Some larger models with careful memory management
- Basic coding assistants
- Summarization, writing and document experiments
- General chat
A machine with 16 GB RAM and a supported GPU with 6–8 GB VRAM can already be a surprisingly capable local AI computer. Gaming laptops are a good example—you may already own enough hardware to get started.
32 GB RAM: the sweet spot for many users
For a general-purpose local AI machine, 32 GB RAM is currently one of the most useful configurations.
- 8B–14B models
- Larger context windows
- CPU/GPU hybrid inference
- Document workflows and coding models
- Running AI alongside normal desktop applications
It also makes partial GPU offloading far more practical. If a model does not fully fit into VRAM, the remaining weights can use system RAM without immediately pushing the entire computer to its limits.
For many people considering a RAM upgrade, moving from 16 GB to 32 GB may provide more practical value than replacing the entire computer.
64 GB RAM: larger local models become realistic
At 64 GB of system memory, the range expands considerably. Depending on the GPU, runtime and quantization, you can start experimenting with:
- 20B–32B-class models
- Larger Mixture-of-Experts models
- Larger context windows
- Heavier document and coding workloads
- CPU-heavy inference when necessary
But more RAM does not automatically mean good performance. A large model running mostly on the CPU may technically work while still being too slow for comfortable interactive use. Capacity and practicality should be treated separately.
How much VRAM do you need?
VRAM usually has a stronger impact on interactive inference speed. This rough guide applies to consumer hardware:
| VRAM | Practical starting point |
|---|---|
| 4 GB | Small models, limited GPU acceleration |
| 6 GB | 4B–8B models, often with partial offload |
| 8 GB | Strong entry point for 7B–9B models |
| 12 GB | Comfortable mid-range local AI |
| 16 GB | Larger quantized models become practical |
| 24 GB+ | Serious consumer/workstation local AI |
These are not hard limits. Quantization, architecture, context length and runtime overhead can change memory requirements significantly. The Latu Solutions Hardware Checker therefore considers more than VRAM alone: RAM, GPU, operating system, workload and practical model requirements all matter.
Why 6 GB VRAM can still be useful
A common misconception is that a 6 GB GPU is already obsolete for AI. It is not.
Many laptops with an RTX 4050-class GPU combine 6 GB VRAM, 16 or 32 GB RAM and a modern CPU. That is enough for useful small and mid-sized local models. The biggest limitation is that larger models may not fit completely into VRAM, but partial offloading can still allow the GPU to accelerate part of the workload.
The result may not match a high-end workstation, but it can still be perfectly usable for learning, coding, writing and private experimentation.
Unified memory changes the calculation
Apple silicon works differently. Instead of separate system RAM and GPU VRAM, the CPU and GPU use a shared unified memory pool. That makes simple VRAM comparisons misleading.
A Mac with 24 GB unified memory, for example, does not behave like a Windows PC with 24 GB RAM plus a separate GPU. The model, macOS and applications all share the same memory. This can make Apple systems very capable for local AI, but you still need to leave enough memory for the operating system and runtime.
Quantization can matter as much as hardware
A model’s parameter count does not tell you its actual memory requirement. Quantization reduces the precision used to store model weights, so a 4-bit version can require dramatically less memory than its full-precision version.
That is why formats such as Q4 are so common in local AI. For many users, a well-supported Q4 quantization provides a useful balance between memory usage, model quality and inference speed.
The largest model your computer can barely load is often not the best model to run.
Do not forget context memory
Model weights are only part of the memory requirement. Long context windows also consume memory. A model that runs comfortably with a short conversation may run into memory pressure when you load a long document, a large codebase, a long chat history or a large RAG context.
Leave headroom. If your system sits at almost 100% RAM or VRAM usage before you begin real work, the model is probably too large for that setup.
A practical hardware ladder
| Level | Configuration | Best for |
|---|---|---|
| Entry | 16 GB RAM + 6–8 GB VRAM | Learning and useful small models |
| Balanced | 32 GB RAM + 8–12 GB VRAM | Strong general-purpose local AI |
| Advanced | 32–64 GB RAM + 16 GB VRAM | Larger models and more demanding workflows |
| High-end | 64 GB+ RAM + 24 GB+ VRAM | Substantially larger models and serious experimentation |
There is no reason to buy the high-end option before you know you need it.
Check your existing PC first
The easiest upgrade is the one you do not need to buy. Before replacing your GPU or computer:
- Check your current RAM.
- Find the exact GPU and VRAM amount.
- Decide what you actually want the AI to do.
- Try a model that comfortably fits.
- Upgrade only when you know what is limiting you.
The Latu Solutions Local AI Hardware Checker is built around exactly this idea: start with the hardware you already own and determine what it can realistically run.