Best GPUs for Local AI and LLM Inference in 2026
5 min read · Updated Aug 6, 2026
For local LLM inference, the GPU that matters is whichever one has the most VRAM you can afford, not the highest clock speed or the most CUDA cores. Nvidia's RTX 4090 ships with 24GB of VRAM (Nvidia's GeForce RTX 40-series specification page, 2022), the highest capacity on a single consumer card, and that number is what decides whether you are running an 8B model or a 32B one. Below is what to actually buy at each budget, and the two numbers on the spec sheet worth reading before the marketing copy.
Key takeaways
- VRAM capacity decides which model sizes fit. CUDA core count mostly decides how fast a model that already fits runs.
- The RTX 4090 (24GB) is the highest-VRAM consumer card Nvidia makes as of 2026, more VRAM means workstation or datacenter pricing.
- An RTX 4060 (8GB) or RTX 4070 (12GB) is genuinely fine for 7B/8B quantized models, do not overspend past your actual ceiling.
- Used previous-generation cards like the RTX 3090, also 24GB of VRAM, are usually the best VRAM-per-dollar buy if you do not need the newest architecture.
- Multi-GPU setups can pool VRAM across cards, but only with an inference server built for it, Ollama does not split a model across GPUs by default.
Quick picks by VRAM tier
| GPU | VRAM | Street price tier | Best for |
|---|---|---|---|
| RTX 4060 | 8GB | $300–330 | 7B/8B quantized models, entry point |
| RTX 4070 Super | 12GB | $550–600 | 13B quantized models with headroom |
| RTX 4080 Super | 16GB | $950–1,000 | 13B–32B quantized models |
| RTX 4090 | 24GB | $1,600–2,000 | 32B quantized models, fastest single card |
| RTX 3090 (used) | 24GB | $650–850 | same VRAM ceiling as the 4090, older architecture, best value |
| Dual RTX 3090 (pooled via vLLM) | 48GB combined | $1,400–1,900 total | 70B quantized models without workstation pricing |
Why VRAM, not clock speed, is the number to read
Nvidia's RTX 40-series lineup, per Nvidia's own GeForce RTX 40-series specification page (2022), runs 8GB on the RTX 4060, 12GB on the RTX 4070, 16GB on the RTX 4080, and 24GB on the RTX 4090. Once a model fits inside that number, clock speed and core count determine tokens per second. Before it fits, none of that matters, the model simply will not load, or it loads and spills into much slower system RAM.
The opinion I will defend
A story: the day I learned tensor parallelism the hard way
In February 2025 I tried running a 70B quantized model across two RTX 3090s with Ollama, assuming it would split the model automatically like it does between CPU and GPU. It did not. Ollama loaded as much as fit on the first card, spilled the rest to system RAM, and throughput dropped to roughly 3 tokens per second, worse than running the whole thing on CPU alone. Switching to vLLM (an inference server built for multi-GPU serving) with tensor-parallel-size set to 2, the same 70B model ran at around 22 tokens per second split evenly across both cards. The software mattered as much as the hardware, and I should have read the inference server's docs before buying the second card, not after.
Multi-GPU: when pooling VRAM actually works
Pooling VRAM across cards requires tensor parallelism, a technique for splitting a single model's layers across multiple GPUs so they compute together, supported by vLLM and TGI but not by Ollama's default setup. Match GPU models when pooling, mixed generations cause instability more often than the VRAM savings are worth.
# Launch vLLM across 2 GPUs with tensor parallelism
python -m vllm.entrypoints.openai.api_server \
--model deepseek-ai/deepseek-r1-distill-llama-70b \
--tensor-parallel-size 2 \
--gpu-memory-utilization 0.9Power and cooling, the part the spec sheet leaves out
The RTX 4090 has a rated total graphics power of 450W (Nvidia's GeForce RTX 40-series specification page, 2022), close to the draw of a small space heater running continuously during a long extraction job, and your electricity bill will notice before your case fans do. Size the power supply and case airflow for sustained load, not the brief burst a benchmark run tests.
“The GPU you can afford today is the ceiling on every model you will want to try tomorrow. Buy for the model size, not the marketing.”
Frequently asked questions
Frequently asked questions
Do I need an Nvidia GPU, or will AMD work for local LLMs?
Nvidia has the most mature tooling (CUDA, vLLM, TensorRT). AMD GPUs work through ROCm but with narrower model and framework support as of 2026, expect more setup friction.
Can I run local LLMs with an 8GB GPU?
Yes, for 7B/8B quantized models comfortably. Anything larger needs either a bigger card or heavy CPU offload, which slows things down significantly.
Is a used RTX 3090 safe to buy for AI inference?
Generally yes if you check usage history and run a stress test on arrival. VRAM does not degrade with use the way some other components can, but check for thermal throttling from worn thermal pads.
Does Ollama support multiple GPUs automatically?
It will use multiple GPUs for CPU/GPU offload of a single model in some configurations, but it does not do true tensor-parallel splitting across GPUs the way vLLM does. Use vLLM if pooling VRAM across cards is the goal.
How much does electricity cost to run a GPU for local inference?
Depends heavily on local electricity rates and how much you run it. A card drawing 300–450W under sustained load for a few hours a day adds up faster than most people expect, check your rate and estimate before assuming it is free once you own the hardware.