Determining VRAM Requirements for Local LLM Deployment
When deploying local Large Language Models (LLMs), a fundamental consideration is whether the model fits within your GPU's memory. This capability is determined by the model's size, the quantization level, and the context length. This guide offers a practical framework for selecting the appropriate VRAM capacity.
The Impact of Quantization on VRAM
Quantization reduces the precision of stored model weights, resulting in smaller file sizes and reduced VRAM consumption, albeit with some trade-off in quality.
| Quantization Level | Bits per Weight | Typical Application |
|---|---|---|
| Q8_0 | 8 | Exceptional quality |
| Q6_K | ~6.6 | High quality |
| Q5_K_M | ~5.5 | Balance of quality and size |
| Q4_K_M | ~4.5 | Optimal balance of size and quality |
| Q3_K_M | ~3.5 | Reduced VRAM usage with higher quality loss |
Q4_K_M is a standard selection when VRAM is constrained. With greater VRAM availability, Q5 or Q6 quantization allows for running the same model with higher precision and less aggressive compression.
Estimated VRAM Usage by Model Size
The following figures are approximate estimates for model weights. Total VRAM requirements are higher because the runtime, KV cache, and context also consume 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 |
These values are estimates rather than strict limits. Variations in model architecture and quantization formats can influence the actual memory footprint.
VRAM Capacity and Model Compatibility
| VRAM | Practical Range | Current Examples |
|---|---|---|
| 8 GB | Small models, approximately 4B to 9B parameters | Gemma 4 E4B, Qwen3.5 9B |
| 12 GB | Small to mid-sized models, approximately 9B to 14B parameters | Gemma 4 12B, Qwen3.5 9B |
| 16 GB | 12B to 27B models with lower quantization | Gemma 4 26B-A4B, Qwen3.6 27B at Q4 |
| 24 GB | 27B to 35B models at Q4 to Q6 quantization | Qwen3.8 27B, Gemma 4 31B |
| 32 GB | 27B to 35B models at higher quantization | Qwen3.8 27B, Gemma 4 31B |
| 48 GB | Large dense models at lower quantization | 70B-class models at Q3 to Q4 |
| 80 GB | Large dense models at higher quantization | 70B-class models at Q4 to Q6 |
These ranges apply to models that can fully reside on the GPU. Large MoE models present a different scenario: although only a subset of parameters is active per token, the entire weight set must still be stored. Consequently, a model with 100B or more total parameters will not fit within a VRAM budget sized for its active parameter count alone.
Mixture-of-Experts (MoE) Models
MoE models consist of multiple parameter groups known as experts. Since only specific experts are activated for each token, inference can be more efficient compared to dense models with the same total parameter count.
However, inactive experts still occupy memory. Large MoE models may therefore demand significantly more memory than their active parameter count implies. Very large models may necessitate multiple GPUs or the offloading of some weights to system RAM.
Context Length and VRAM Consumption
Model weights represent only a portion of the total memory requirement. The KV cache expands as context length increases, meaning that running the same model with a 64K context requires substantially more VRAM than with a 4K context.
- Extended context lengths consume additional VRAM.
- KV-cache precision influences memory usage.
- Batch size and concurrent user requests also elevate memory demand.
- Reserve some VRAM for the runtime rather than saturating the GPU entirely with model weights.
Practical Recommendations
- Verify the actual size of the specific quantized model you intend to run.
- Avoid using the model file size as the exact VRAM requirement; ensure adequate headroom for the KV cache and runtime.
- If a model does not fully fit in VRAM, partial offloading to system RAM is possible, though inference speed will typically decrease.
- For long-context or agentic workloads, budget for more VRAM than the model weights alone suggest.
- Multiple GPUs can distribute a model if a single GPU lacks sufficient VRAM.
Execute on DaDesktop
There is no need to purchase physical hardware to run local LLMs. DaDesktop provides a cloud desktop with the necessary VRAM, enabling you to run models directly without owning the underlying hardware.
Select the VRAM tier that matches your model, load it, and begin usage immediately. This eliminates the need for setup, hardware purchases, or driver management. View the available GPUs to see the options.