Local LLM VRAM Calculator
Calculate if your GPU can run Large Language Models
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Ready to Run
22.43
GB
Total VRAM Required
You have 16 GB available
Est. Speed: 7.3 t/s
Weights
KV Cache
System
Recommended: RTX 3090 / 4090
24GB VRAM is the golden standard. Upgrade your local GPU, or rent a cloud instance right now.
New to Cloud GPUs? Read our 1-minute setup guide.
đ§ Expert Hardware Analysis
Qwen 3.5 35B MoE utilizes a Mixture of Experts (MoE) architecture. While only 3B parameters are active during inference, the entire 35B model must be loaded into memory, demanding 22.43 GB of VRAM at 4-bit quantization. For exact tasks like coding generation, running without system offloading is highly recommended to preserve processing speed. The RTX 5080 (16GB) is limited to 16 GB. You will face a deficit of 6.4 GB, forcing the system to offload to your CPU/RAM. Expect a severe degradation in inference speed.
Hardware Guide & FAQ
Yes, for the mid-sized models.
16GB VRAM is the recommended entry point for 2026. It allows you to run:
However, for massive models like Llama 3.3 70B, 16GB (or even 24GB) is not enough. For those, you need 48GB+ VRAM (like RTX 6000 Ada) or a Dual-GPU setup (2x RTX 3090/4090).
16GB VRAM is the recommended entry point for 2026. It allows you to run:
- Llama 4 Scout (17B): Comfortably at Q4 quantization.
- DeepSeek R1 Distill (14B): Runs perfectly with long context.
- Mistral Small (22B): Requires efficient quantization (Q4) to fit.
However, for massive models like Llama 3.3 70B, 16GB (or even 24GB) is not enough. For those, you need 48GB+ VRAM (like RTX 6000 Ada) or a Dual-GPU setup (2x RTX 3090/4090).
These are different file formats for running models locally:
- GGUF (Best for Beginners): The most flexible format. Works on CPU & GPU (perfect for offloading). Use this if you are unsure.
- EXL2 (Fastest): Exclusive to NVIDIA GPUs. It's incredibly fast but requires the model to fit 100% in VRAM. It crashes if you run out of memory.
- AWQ: An older GPU-focused format. Good for accuracy but less flexible than GGUF.
Slow generation usually means System RAM Offloading.
GPU memory (VRAM) is ultra-fast (1,000+ GB/s). System RAM is much slower (~60 GB/s).
If your model is 16GB and your GPU has 12GB, the remaining 4GB spills to your slow RAM, acting like a bottleneck.
The Fix: Use this calculator to find a smaller quantization (e.g., Q4_K_M) or reduce the Context Window until the entire model fits in VRAM (Green Result).
GPU memory (VRAM) is ultra-fast (1,000+ GB/s). System RAM is much slower (~60 GB/s).
If your model is 16GB and your GPU has 12GB, the remaining 4GB spills to your slow RAM, acting like a bottleneck.
The Fix: Use this calculator to find a smaller quantization (e.g., Q4_K_M) or reduce the Context Window until the entire model fits in VRAM (Green Result).
The 5090 is the current best consumer card, but NO.
With 32GB VRAM, the RTX 5090 can easily run every model up to 34B parameters (Gemma 2, Qwen 2.5) at full speed.
However, running full 70B models (like Llama 3.3 70B) requires **46GB VRAM** and will always result in significant **CPU Offloading** on the 5090. If you need full speed 70B, you must use a 48GB Pro card or a Dual-GPU setup.
With 32GB VRAM, the RTX 5090 can easily run every model up to 34B parameters (Gemma 2, Qwen 2.5) at full speed.
However, running full 70B models (like Llama 3.3 70B) requires **46GB VRAM** and will always result in significant **CPU Offloading** on the 5090. If you need full speed 70B, you must use a 48GB Pro card or a Dual-GPU setup.
Context is the model's "short-term memory".
Tip: If you are out of memory, lowering context from 32k to 8k is often the easiest way to fix it without changing the model intelligence.
- 8k (Standard): Enough for most chats. Low VRAM usage.
- 32k (Long Docs): Great for summarizing PDFs. Uses ~2-4GB extra VRAM.
- 128k (Extreme): Uses massive amounts of VRAM (KV Cache).
Tip: If you are out of memory, lowering context from 32k to 8k is often the easiest way to fix it without changing the model intelligence.