Question 1

How much VRAM do I actually need for a local LLM in 2026?

Accepted Answer

It depends on the model size and quantization. A 12B model at Q4 fits in about 7–8 GB, making a 12GB card workable. For 26–27B models you need 14–18 GB — a 16GB card is the sweet spot. Larger 70B+ models need 40–80 GB, requiring multi-GPU or high-end workstation cards.

Question 2

What is CPU offloading and why is it slow?

Accepted Answer

CPU offloading moves layers that don't fit in VRAM into system RAM. System RAM bandwidth is 40–80 GB/s — roughly 10–20× slower than GPU memory. For smooth inference, keep the entire quantized model in VRAM.

Question 3

What does Q4_K_M or Q8_0 quantization mean?

Accepted Answer

Quantization reduces each model weight from 16-bit to fewer bits to save memory. Q4_K_M uses ~4 bits per weight — about half the VRAM of FP16 with minimal quality loss. Q8_0 uses 8 bits, producing near-lossless quality but requiring roughly twice the VRAM.

Question 4

What is a MoE model and why does it run faster than its size suggests?

Accepted Answer

Mixture-of-Experts (MoE) models have a large total parameter count but only activate a small subset per token. This means inference compute and KV cache scale with the active parameter count, not the total — so a 26B MoE model can run faster than a dense 7B model on capable hardware.

Question 5

Should I choose a consumer GeForce card or a workstation card?

Accepted Answer

Consumer cards offer the best price-per-GB of VRAM for gaming-tier amounts (8–24 GB). Workstation cards offer higher VRAM capacity (32–96 GB) and ECC memory at a significant cost premium. Choose consumer if your model fits in 24 GB; go workstation if you need 32 GB+.

Question 6

Can I run a local LLM on an AMD GPU?

Accepted Answer

Yes. ROCm supports llama.cpp and Ollama on Linux, and increasingly on Windows. Radeon RX 7900 XTX (24 GB) and Radeon PRO W7900 (48 GB) are solid choices. Performance is comparable to equivalent NVIDIA cards for inference workloads.

Question 7

Can I run a local LLM on an Intel Arc GPU?

Accepted Answer

Yes. Intel Arc GPUs (like the A770 16GB) can run local LLMs using Intel's IPEX (Intel Extension for PyTorch) or OpenVINO backend, which is supported by llama.cpp and Ollama. The A770 16GB is one of the most affordable 16GB VRAM options on the market, offering solid budget inference performance, though setup can be slightly more involved compared to NVIDIA CUDA.

Best GPU for Local LLM Models

Browse LLM Model Guides

QWEN-3.6-CODER-27B

QWEN-3.6-35B-A3B

GEMMA-4-26B-A4B

GEMMA-4-12B

DEEPSEEK-R1-DISTILL-14B

DEEPSEEK-R1-DISTILL-32B

MISTRAL-SMALL-3.1-24B

LLAMA-4-SCOUT-109B-A17B

GPT-OSS-20B

GPUs for Custom Model Config

Footprint Estimate

Compatible GPUs

Frequently Asked Questions