Best GPU for Running Local LLMs in 2026

An LLM occupies memory in two parts. The weights are roughly the parameter count multiplied by the bytes-per-parameter of your quantization: at 4-bit (Q4) that is about half a gigabyte per billion parameters, so a 7B model is roughly 4-5GB, a 14B is roughly 8-10GB, a 34B is roughly 18-20GB, and a 70B is roughly 38-42GB. On top of that sits the KV cache, which grows with your context length and can add several gigabytes at long contexts. Both must live in VRAM at the same time.

When everything fits, the speed you actually feel during generation is governed mostly by memory bandwidth, not compute. Token generation is memory-bound: the GPU streams the entire model through its memory bus for every token, so a card with faster memory produces tokens faster even at the same FLOPS. This is why an RTX 5090's roughly 1.8TB/s of GDDR7 bandwidth meaningfully out-generates a 4090's roughly 1TB/s, and why two slow-bandwidth cards rarely beat one fast one.

The practical takeaway: pick the quantization and context you want, add up weights plus KV cache, then buy the cheapest card whose VRAM clears that total with a little headroom. Buying more compute than your VRAM can feed is wasted money. These figures are estimates that vary with the runtime, the specific quant, and context length, so treat them as planning numbers rather than guarantees.

Sixteen gigabytes is the entry point where local LLMs stop being a science project and start being useful. A 16GB card like the RTX 4080 SUPER comfortably runs 7B and 13-14B models at 4-bit with room for a healthy context window, which covers the models most people actually reach for day to day: coding assistants, summarizers, chat, and retrieval-augmented setups.

Where 16GB runs out of room is the 30B-and-up class and long contexts on bigger models. You can sometimes squeeze a 34B in at an aggressive quant, but quality degrades and the KV cache leaves you cramped. If your workload is firmly in the 7B-14B range and you also want strong gaming or general GPU performance, a 16GB card is a reasonable buy. For pure LLM work, though, the value math usually points one tier up.

Twenty-four gigabytes is the single best value bracket for local LLMs, and three cards fight over it. The used RTX 3090 (24GB) is the budget hero: street prices in 2026 sit roughly in the $800-1,000 range, and it offers the same 24GB capacity as cards that cost two to three times more. Its memory bandwidth is lower than the newer cards, so generation is slower, but for a single user running one model at a time that gap is far smaller than the price gap. If you are buying your first serious LLM card, this is the one to beat.

The RTX 4090 (24GB, roughly 1TB/s) is the comfortable new-card choice, typically around $1,500-2,000 depending on availability, and the current-gen RTX 5090 pushes the bracket to 32GB of faster GDDR7 at roughly 1.8TB/s for around $2,000 or more at street prices. The 5090's extra 8GB is genuinely useful: it lets you run a 34B at a higher quant or a 70B at a tight low quant on a single card, and its bandwidth makes everything noticeably snappier. These prices are approximate and move with supply.

All three of these 24GB-class cards handle 7B through 34B models comfortably, and can run a 70B at a low quant if you accept some quality loss and a modest context. For most developers and hobbyists, a single 24GB card is the right amount of GPU. Buy the used 3090 if budget is the constraint; buy the 4090 or 5090 if you want speed and warranty and the headroom of newer memory.

Running a 70B model in full, at a quality-preserving 4-bit quant with usable context, needs roughly 40-48GB of VRAM. That puts you in 48GB territory, and there are two ways to get there. The clean path is a single 48GB professional card like the RTX A6000: one card, no multi-GPU plumbing, and enough room to hold a 70B at Q4 or Q5 entirely in memory. The tradeoff is price, since these cards cost several thousand dollars.

The budget path is two 24GB cards run in parallel, most famously two used 3090s for a combined 48GB. This works and it is dramatically cheaper than a single A6000, but it comes with real friction. The model is split across cards, and tokens have to cross the PCIe bus between them, so generation is slower than a single card with the same total VRAM, and you take on the complexity of a dual-GPU power, cooling, and case setup. Our dual RTX 4090 workstation build leans into this approach for people who want maximum local capability without professional-card pricing.

Choose a single 48GB card if you value simplicity, sustained throughput, and lower power draw, and you can absorb the cost. Choose dual 24GB cards if you want the most VRAM per dollar and are comfortable managing a more complex machine. Either way, be honest about whether you actually need 70B locally, because the jump in cost and complexity from the 24GB tier is steep.

Buying a discrete GPU is not always the right answer. Apple Silicon Macs with large unified memory are a genuinely strong option for local inference, because the CPU and GPU share one big memory pool. A Mac with 64GB or 128GB of unified memory can hold models that would require expensive multi-GPU rigs on the PC side, and it does so quietly and at low power. The catch is that memory bandwidth and raw throughput trail a dedicated NVIDIA card, and the CUDA-centric training and fine-tuning ecosystem is far less mature on Mac. For inference and light experimentation, a Mac is excellent; for serious fine-tuning, it is a compromise.

The cloud is the right call when your usage is bursty or your model is genuinely huge. Renting an A100, H100, or similar by the hour costs nothing when idle and gives you VRAM no consumer card can match, which is ideal for occasional large jobs, training runs, or evaluating a 70B-plus model before committing to hardware. The math flips once you are running a GPU many hours a day: at that point a card you own pays for itself, keeps your data on your own machine, and removes per-hour anxiety. Map your real duty cycle before deciding, because the break-even point between renting and buying is mostly a function of how many hours per week the card is actually busy.