Best GPU for Local LLMs: 5 Practical Picks for 2026

The best GPU for a local LLM is usually the card with enough memory for the model, context window, and runtime overhead you actually plan to use. Raw compute matters after the workload fits. If it does not fit, part of the model spills into system memory and the experience changes dramatically.

That is why this guide does not rank cards by gaming benchmarks or repeat a universal tokens-per-second number. Inference speed changes with the model, quantization, context, batch size, backend, driver, and prompt-processing settings. The hardware specifications in the product cards above are stable decision inputs; a single benchmark result is not.

Why VRAM comes first

Model weights are only part of GPU memory use. The runtime also needs memory for the KV cache, context, temporary buffers, and sometimes multiple concurrent requests. A model that technically loads with a short context can still fail or slow down when the context window grows.

As a planning rule:

These are planning bands, not promises. Check the actual model file and runtime before buying hardware. The detailed VRAM planning guide shows the arithmetic.

RTX 3090: the capacity-first used option

The RTX 3090 remains relevant because 24GB is still rare below the highest new-product tier. Its 936 GB/s of memory bandwidth also remains competitive for memory-bound generation.

The tradeoff is acquisition risk. An RTX 3090 is now a used purchase with an unknown thermal and workload history. A defensible buying process includes:

1. A seller-funded return window.
2. Clear photographs of the power connector, PCB area, fans, and heatsink.
3. A sustained GPU stress test.
4. A dedicated VRAM error test.
5. Monitoring for memory-junction temperature, fan noise, clock instability, and visual artifacts.

Do not use a single renewed Amazon listing as the “market price.” Compare completed sales and local-market listings for the exact board model and condition.

RTX 5060 Ti 16GB: the efficient new-card default

The RTX 5060 Ti 16GB replaced the RTX 4060 Ti 16GB as NVIDIA’s 16GB entry card. It has 16GB of GDDR7 on a 128-bit bus (448 GB/s) and a 180W total graphics power rating, and NVIDIA recommends a 600W system power supply.

Its 180W rating is far below the RTX 3090 and RTX 5090, which simplifies power and cooling for an always-on host. Actual behavior depends on board design, workload, power settings, and software. Two buying details matter. The 8GB RTX 5060 Ti is a different proposition and should not be substituted into a 16GB recommendation. Also compare its current price with the RTX 5070 Ti and a used RTX 3090, because this recommendation assumes it costs meaningfully less than both.

RTX 5070 Ti 16GB: bandwidth without more capacity

The RTX 5070 Ti 16GB keeps the same 16GB capacity but doubles the 5060 Ti’s memory bandwidth to 896 GB/s. That can help when the model already fits and throughput, prompt processing, image generation, or concurrent requests are the bottleneck.

It is not an upgrade for a workload that simply needs more than 16GB. In that case, a used 24GB card or the 32GB RTX 5090 solves the actual constraint more directly.

RTX 5090: maximum single-card GeForce capacity

The RTX 5090 combines 32GB of GDDR7 with 1,792 GB/s of memory bandwidth. That makes it the strongest current consumer option for workloads that need more than 24GB on one card.

NVIDIA lists no NVLink support for the RTX 5090, so multi-GPU inference splits the model over PCIe. The Founders Edition is a dual-slot card, but partner cards are often thicker, so check the exact model’s dimensions.

The 575W board-power specification, and NVIDIA’s 1000W system power recommendation, make system design part of the purchase. Verify PSU capacity, connector routing, transient headroom, chassis airflow, and UPS sizing before treating the GPU as a drop-in upgrade.

RX 9070 XT: viable ROCm hardware with a software check

AMD lists the RX 9070 XT and the other RDNA 4 cards as supported GPUs in its ROCm requirements for Linux, and Ollama’s GPU documentation lists them as well. The card has 16GB of GDDR6 on a 256-bit bus and a 304W board power rating.

The remaining risk is application compatibility. Many local-AI instructions, extensions, containers, and prebuilt wheels still assume CUDA. Before buying, test or verify:

• The exact operating system and ROCm release.
• Ollama or llama.cpp support for the selected build.
• PyTorch wheels for the required target.
• Any attention kernels, quantization extensions, or UI plugins in the workflow.

AMD can be a rational choice for a Linux-first user who is comfortable validating the stack. NVIDIA remains our default recommendation for the broadest tool compatibility.

Final buying rule

Choose the lowest-power card that provides enough VRAM for your workload. Do not pay for faster compute when the model does not fit, and do not buy 32GB hardware for a workload that comfortably lives in 16GB.

Our pick is a properly tested used RTX 3090, because 24GB on one CUDA card covers the widest range of models, up to 32B at Q4_K_M. If you want a new card with a warranty and your models fit in 16GB, start with the RTX 5060 Ti 16GB. If you need more than 24GB on one card, the RTX 5090 is the only current GeForce option. For AMD, validate the exact ROCm path before placing the order.