Determine the VRAM Needed for Local LLM Deployment
When deploying Large Language Models (LLMs) locally, the primary consideration is straightforward: can the model fit within your GPU's memory? The outcome relies on a balance between model scale, quantization settings, and the length of the context. This article provides a practical framework for selecting the appropriate amount of Video RAM (VRAM).
The Impact of Quantization on VRAM Usage
Quantization lowers the precision required to store model weights. Reducing the bit depth shrinks the model's footprint and reduces VRAM consumption, although this may result in a slight degradation of output quality.
| Quantization Type | Bits per Weight | Common Application |
|---|---|---|
| Q8_0 | 8 | Exceptional quality |
| Q6_K | ~6.6 | High quality |
| Q5_K_M | ~5.5 | Optimal balance of quality and size |
| Q4_K_M | ~4.5 | Well-balanced size and quality |
| Q3_K_M | ~3.5 | Lower VRAM usage, noticeable quality reduction |
Q4_K_M is frequently selected when VRAM resources are constrained. However, if you have additional VRAM headroom, opting for Q5 or Q6 allows you to execute the same model with higher precision.
Estimated VRAM Requirements by Model Scale
The following figures are approximate estimates for model weights alone. The total VRAM demand is typically higher, as it must also accommodate the runtime environment, KV cache, and active context.
| 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 are general guidelines rather than absolute limits. Variations in model architecture and specific quantization formats can influence the actual memory footprint.
Capacities Based on Available VRAM
| VRAM Capacity | Viable Model Range | Illustrative Examples |
|---|---|---|
| 8 GB | Compact 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 using 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 levels | 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 levels | 70B-class models at Q4 to Q6 |
These ranges apply to models where the weights can be fully loaded onto the GPU. Large Mixture-of-Experts (MoE) models present a different scenario: while only a subset of parameters is active per token, the system must still retain the entire weight set. Consequently, a model with 100B or more total parameters will not fit within a 100B VRAM budget solely because its active parameter count is lower.
Understanding MoE Models
Mixture-of-Experts (MoE) models consist of multiple parameter groups known as experts. For each token, only specific experts are activated, which often allows for more efficient inference compared to a dense model of the same total size.
Nevertheless, the inactive experts remain part of the model's structure. As a result, large MoE models can demand significantly more memory than their active parameter count implies. Extremely large models may require multi-GPU setups or offloading to system RAM.
The VRAM Demand of Context Length
Model weights represent only a portion of the total memory requirements. The KV cache expands as the context length increases; therefore, operating the same model with a 64K context may consume substantially more VRAM than with a 4K context.
- Extended context lengths necessitate higher VRAM usage.
- The precision of the KV cache influences memory consumption.
- Batch size and the number of concurrent users also elevate memory demands.
- Reserve some VRAM for the runtime environment rather than occupying the GPU entirely with model weights.
Best Practices
- Verify the actual size of the specific quantized model you intend to use.
- Avoid treating the model file size as the precise VRAM requirement; always account for the KV cache and runtime overhead.
- If the model exceeds VRAM capacity, portions can be offloaded to system RAM, though this typically reduces inference speed.
- For long-context or agentic workflows, allocate more VRAM than the model weights alone require.
- Utilize multiple GPUs to partition a model when a single device lacks sufficient VRAM.
Deploy with DaDesktop
There is no need to purchase a GPU to execute local LLMs. DaDesktop provides a cloud desktop equipped with the necessary VRAM, enabling you to run models directly without owning the physical hardware.
Select the VRAM tier that corresponds to your model, load it, and begin usage immediately. This eliminates the need for complex setup, hardware purchases, or driver troubleshooting. Explore the available GPUs to view your options.