Best GPU for Local LLM Models
Running LLMs locally requires matching VRAM budgets to quantization levels and context sizes. Use our live calculator below to estimate memory footprints and browse model-specific guides.
Browse LLM Model Guides
Qwen3.8-27B
A 27B dense vision-language model whose hybrid linear attention keeps the KV cache tiny — it runs a 128k+ context on a single 16-24 GB card, with a native 262k window extensible to 1M.
DeepSeek-V4-Flash
A 284B Mixture-of-Experts model with just 13B active params and a 1M-token context. Weights are large enough to need multi-GPU or CPU-offload, but only 13B activate per token so decode stays fast.
gpt-oss-120b
OpenAI's 117B open-weight Mixture-of-Experts model with 5.1B active params. Its native MXFP4 weights fit a single 80-96 GB GPU, and the tiny active count keeps decode fast.
Qwen3.6-Coder-27B
State-of-the-art dense model with deep math, coding, and engineering comprehension.
Qwen3.6-35B-A3B
Massive Mixture-of-Experts coder optimized for 16GB–24GB VRAM target setups.
Gemma 4 26B-A4B
Google's premier high-density MoE architected for complex coding and reasoning.
Gemma 4 12B
Lightweight, high-speed dense model optimized for standard consumer configurations.
DeepSeek-R1-Distill-14B
Top-tier reasoning distilled into a compact 14B model. Strong STEM and coding performance on any 12GB+ GPU.
DeepSeek-R1-Distill-32B
Mid-size reasoning powerhouse for 24GB cards. Matches frontier models on math, coding, and logic benchmarks.
Mistral Small 3.1 24B
Efficient dense model with broad multilingual support and native function calling. Fits 16GB cards at Q4.
Llama 4 Scout 109B-A17B
Meta's most accessible MoE flagship — 17B active parameters deliver strong quality on workstation hardware.
Hy-MT2-7B
Versatile 7B dense model built for efficient local inference on entry-level to mid-range GPUs.
gpt-oss-20b
OpenAI's consumer reasoning model, built to run uncompromised on mainstream local hardware.
NVIDIA Nemotron-3-Nano-4B
Ultra-compact 4B NVIDIA model that fits comfortably on nearly any modern GPU — ideal for edge and portable setups.
Qwen3-Next-80B-A3B
Huge 80B Mixture-of-Experts with only 3B active parameters — needs a 48GB workstation card, but generates as fast as a small model.
GPUs for Custom Model Config
Footprint Estimate
Frequently Asked Questions
Common questions about choosing a GPU for local LLM inference.
How much VRAM do I actually need for a local LLM in 2026?
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.
What is CPU offloading and why is it slow?
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.
What does Q4_K_M or Q8_0 quantization mean?
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.
What is a MoE model and why does it run faster than its size suggests?
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.
Should I choose a consumer GeForce card or a workstation card?
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+.
Can I run a local LLM on an AMD GPU?
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.
Can I run a local LLM on an Intel Arc GPU?
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.