Determining the Optimal VRAM for Local LLM Deployment
When deploying a local LLM, the primary consideration is whether the model fits within your GPU's capacity. This compatibility hinges on three critical factors: model size, quantization level, and context length. This guide provides a practical framework for selecting an appropriate VRAM configuration.
Impact of Quantization on VRAM Usage
Quantization lowers the precision of model weights, resulting in smaller file sizes and reduced VRAM consumption, though this comes with a trade-off in output quality.
| Quant | Bits per weight | Typical use |
|---|---|---|
| Q8_0 | 8 | Very high quality |
| Q6_K | ~6.6 | Very good quality |
| Q5_K_M | ~5.5 | Good quality and size |
| Q4_K_M | ~4.5 | Good balance of size and quality |
| Q3_K_M | ~3.5 | Lower VRAM, more quality loss |
In scenarios with limited VRAM, Q4_K_M is frequently the preferred choice. When resources permit, selecting Q5 or Q6 allows for running the same model with reduced quantization, thereby preserving more quality.
Estimated VRAM Requirements by Model Size
The figures below provide rough estimates for model weights alone. In practice, total VRAM usage is higher due to the overhead of the runtime, KV cache, and context processing.
| 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 estimates rather than fixed limits, as specific model architectures and quantization formats can influence the final memory footprint.
Performance Capabilities by VRAM Tier
| VRAM | Practical range | Current examples |
|---|---|---|
| 8 GB | Small models around 4B to 9B | Gemma 4 E4B, Qwen3.5 9B |
| 12 GB | Small to mid-sized models around 9B to 14B | Gemma 4 12B, Qwen3.5 9B |
| 16 GB | 12B to 27B with lower quantization | Gemma 4 26B-A4B, Qwen3.6 27B at Q4 |
| 24 GB | 27B to 35B at Q4 to Q6 | Qwen3.8 27B, Gemma 4 31B |
| 32 GB | 27B to 35B 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 assume the model weights fit entirely on the GPU. Mixture-of-Experts (MoE) models present a different challenge: while only a subset of parameters is active per token, the full set of weights must still be stored in memory. Consequently, a model with 100B or more total parameters cannot fit within a 100B VRAM budget simply because its active parameter count is lower.
Considerations for MoE Models
Mixture-of-Experts architectures utilize 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 equivalent total parameter counts.
However, inactive experts still occupy memory space. As a result, large MoE models often demand significantly more memory than their active parameter count implies. Extremely large models may necessitate the use of multiple GPUs or the offloading of weights to system RAM.
The Impact of Context Length on VRAM
Model weights represent only a portion of the total memory requirements. The KV cache expands as context length increases, meaning that running the same model with a 64K context will consume substantially more VRAM than with a 4K context.
- Extended context lengths directly increase VRAM consumption.
- The precision of the KV cache impacts overall memory usage.
- Both batch size and the number of concurrent users contribute to higher memory demands.
- Reserve sufficient VRAM for the runtime environment rather than allocating all available memory to model weights.
Best Practices
- Verify the exact size of the specific quantized model you intend to deploy.
- Do not rely solely on the model file size as the VRAM requirement; account for additional overhead from the KV cache and runtime.
- If the model exceeds available VRAM, offloading to system RAM is possible, but expect reduced inference speeds.
- For long-context or agentic workflows, budget for more VRAM than what is strictly required for the model weights.
- Leverage multiple GPUs to distribute the model load when a single GPU lacks sufficient capacity.
Access via DaDesktop
There is no need to purchase physical hardware to run local LLMs. DaDesktop provides a cloud-based desktop environment equipped with the necessary VRAM, enabling you to execute models directly without owning the underlying infrastructure.
Select the VRAM tier that aligns with your model requirements, load the model, and begin usage immediately. This solution eliminates the need for setup, hardware acquisition, or driver management. Explore available GPUs to view your options.