What Amount of VRAM is Required for Local LLM Execution?
When deploying a local Large Language Model (LLM), the fundamental question remains straightforward: can it be accommodated within your GPU's memory? The answer hinges on the model's size, its quantization level, and the desired context length. This guide provides a practical foundation for determining the appropriate amount of VRAM.
The Impact of Quantization on VRAM
Quantization is the process of reducing the precision used to store model weights. By employing lower-bit quantization, the model footprint shrinks, thereby consuming less VRAM, although this often comes with a slight trade-off in output quality.
| Quantization Level | Bits per Weight | Common Application |
|---|---|---|
| Q8_0 | 8 | Extremely high quality |
| Q6_K | ~6.6 | Very good quality |
| Q5_K_M | ~5.5 | Optimal balance of quality and size |
| Q4_K_M | ~4.5 | Strong compromise between size and quality |
| Q3_K_M | ~3.5 | Reduced VRAM usage with greater quality loss |
Q4_K_M is a frequent choice when VRAM resources are constrained. However, if you have additional VRAM headroom, opting for Q5 or Q6 allows you to run the same model with minimal loss in fidelity.
Estimated VRAM Requirements by Model Size
The figures below provide rough estimates for model weights alone. Actual VRAM consumption will be higher, as the runtime environment, KV cache, and context window also demand memory.
| Model Size | Q8_0 | Q6_K | Q4_K_M | Q3_K_M |
|---|---|---|---|---|
| 4B | ~5 GB | ~4 GB | ~3 GB | ~2.5 GB |
| 8B | ~9 GB | ~7 GB | ~5.5 GB | ~4.5 GB |
| 12B | ~13 GB | ~10 GB | ~8 GB | ~6.5 GB |
| 14B | ~16 GB | ~12 GB | ~9 GB | ~7.5 GB |
| 27B | ~30 GB | ~22 GB | ~17 GB | ~13 GB |
| 32B | ~36 GB | ~27 GB | ~20 GB | ~16 GB |
| 70B | ~80 GB | ~60 GB | ~42 GB | ~34 GB |
Please note that these are approximations rather than fixed limits. Variations in model architectures and specific quantization formats can influence the actual memory footprint.
Capabilities Based on VRAM Capacity
| VRAM | Practical Range | Current Examples |
|---|---|---|
| 8 GB | Compact models from 4B to 9B | Gemma 4 E4B, Qwen3.5 9B |
| 12 GB | Small to mid-sized models from 9B to 14B | Gemma 4 12B, Qwen3.5 9B |
| 16 GB | 12B to 27B models using lower quantization | Gemma 4 26B-A4B, Qwen3.6 27B at Q4 |
| 24 GB | 27B to 35B models at Q4 to Q6 | Qwen3.8 27B, Gemma 4 31B |
| 32 GB | 27B to 35B models with higher quantization | Qwen3.8 27B, Gemma 4 31B |
| 48 GB | Large dense models at lower quantization levels | 70B-class models at Q3 to Q4 |
| 80 GB | Large dense models at higher quantization levels | 70B-class models at Q4 to Q6 |
These ranges apply to models whose weights can reside entirely on the GPU. Large Mixture-of-Experts (MoE) models operate differently: while only a subset of parameters is active per token, the system must still store the complete weight set. Consequently, a model with 100B or more total parameters will not fit within a 100B VRAM budget simply because its active parameter count is lower.
MoE Models
Mixture-of-Experts (MoE) architectures consist of multiple groups of parameters known as experts. Since only a portion of these experts is utilized for any given token, inference can be more efficient compared to a dense model with an equivalent total parameter count.
Nevertheless, inactive experts remain part of the model's memory footprint. Therefore, large MoE models can demand significantly more memory than their active parameter count would suggest. Extremely large models may necessitate the use of multiple GPUs or offloading to system RAM.
Context Length and VRAM Usage
Model weights represent only a fraction of the total memory requirement. The KV cache expands as the context length increases, meaning that running the same model at 64K context can require considerably more VRAM than at 4K context.
- Extended context lengths demand additional VRAM.
- The precision of the KV cache influences overall memory consumption.
- Batch size and the number of concurrent users also elevate memory usage.
- It is advisable to reserve some VRAM for the runtime rather than filling the GPU entirely with model weights.
Practical Recommendations
- Verify the actual size of the specific quantized model you intend to run.
- Avoid assuming the model file size is the exact VRAM requirement; ensure sufficient headroom for the KV cache and runtime.
- If a model cannot fit entirely within VRAM, parts of it can be offloaded to system RAM, though this typically results in slower inference speeds.
- For long-context or agentic workloads, allocate more VRAM than what is required for the model weights alone.
- When a single GPU lacks sufficient VRAM, multiple GPUs can be used to split the model load.
Run It on DaDesktop
There is no need to purchase a GPU to run a local LLM. DaDesktop provides a cloud desktop equipped with the necessary VRAM, enabling you to run models directly without owning the underlying hardware.
Select the VRAM tier that suits your model, load it, and begin using it immediately. This eliminates setup complexities, hardware purchases, and driver issues. View the available GPUs to see your options.