Best GPUs for Local AI in 2026

Most GPU buying advice is written for gamers, and it is close to useless for AI work. Frame rates measure something a language model never does. The three specifications that decide whether a card is any good for local AI — memory capacity, memory bandwidth, and framework support — barely appear in a typical review.

This guide is organised around those three, in that order.

How we chose

Our full approach is in the review methodology. For graphics cards specifically, we weight in this order:

1. Memory capacity, because it determines what you can run at all. This is a threshold, not a gradient: a card that is 2 GB short of your model is the wrong card, however fast it is.
2. Memory bandwidth, because once a model fits, token generation is almost entirely bandwidth-bound. A useful ceiling is bandwidth ÷ model size in memory.
3. Framework support, because a theoretical capability you cannot reach through your toolchain is not a capability.
4. Power and thermals, because a 575 W card imposes real costs on the rest of the build.

Compute throughput matters too, but it only becomes the binding constraint for image generation and training. For the text generation most people are doing, it is rarely what limits you.

The specifications side by side

The recommendations

Best overall

The RTX 5090 earns this position on one number: 32 GB.

That figure clears a specific and important threshold. A 32-billion-parameter model at 4-bit quantisation needs roughly 19 GB for weights, plus several gigabytes of KV cache at a useful context length. On a 24 GB card that fits, but only just, and you spend your time trimming context. On 32 GB it fits with room to work.

The bandwidth is the second reason. At 1,792 GB/s the 5090 has roughly 78% more memory bandwidth than an RTX 4090, and since generation speed scales with bandwidth, that translates fairly directly into tokens per second on any model that fits both cards.

What it does not do is reach 70B. A 70B model at 4-bit needs about 42 GB of weights before context, and no amount of optimisation closes a 10 GB gap. If 70B is your requirement, this is not your card.

The costs are real and worth stating plainly. 575 W of board power needs a 1,000 W-class supply, a case that can move the heat, and a tolerance for the noise that comes with removing it.

Best value

The RTX 5070 Ti is the card we recommend most often, because the specification that matters is nearly identical to the RTX 5080’s.

Both have 16 GB on a 256-bit bus. The 5080 has 896 GB/s versus… 960 GB/s. That is a 7% bandwidth advantage for a meaningful price premium, on the specification that most determines AI performance. The 5080’s extra shader count matters for gaming and for image generation; for language models it largely does not.

At 300 W it also drops into an existing mid-range build without a power supply upgrade, which the 5080 and 5090 frequently do not.

The limitation is the same 16 GB ceiling. That comfortably covers 14B-class models — which is where a great deal of genuinely useful local AI happens — and does not reach 32B.

Best budget

If the goal is simply to get 16 GB of CUDA memory into a machine for the least money, this is the card.

It comes with an honest caveat that most coverage glosses over: the 128-bit bus gives it 448 GB/s, roughly half the RTX 5070 Ti. It will load the same models and run them at roughly half the speed. That is capacity without the bandwidth to exploit it.

For an always-on inference box, a homelab node, or a machine where 180 W and a small case matter more than tokens per second, that trade is sensible. If you are going to sit and watch it generate, it is not.

Be careful with the name. There is an 8 GB variant sold under the same model number. For AI work they are different products, and the 8 GB card is not one we would recommend.

Best high-end

This is the single-card answer to 70B, and the first genuine one.

96 GB of ECC GDDR7 at the same 1,792 GB/s as an RTX 5090 means a 70B model at 8-bit fits entirely in memory, with context to spare — no layer splitting, no PCIe bottleneck between cards, no multi-GPU runtime quirks. For fine-tuning, where memory pressure is far higher than inference, the headroom matters even more.

It is priced as a professional tool, several times an RTX 5090. The Max-Q variant, configurable down to 300 W, is worth knowing about if you are building into a constrained power or thermal envelope.

Best memory per dollar

24 GB and 960 GB/s, typically for meaningfully less than the NVIDIA cards that match it on memory.

For text generation specifically, this is a strong proposition. llama.cpp and Ollama both have solid ROCm and Vulkan back-ends, and a 32B model at 4-bit runs well.

The honest caveat is everything else. Image generation pipelines, fine-tuning frameworks and most research code assume CUDA. Some of it works on ROCm, some works after effort, and some does not work at all. If your workload is “run language models” the hardware is excellent value. If it is “try whatever appears on GitHub this month”, the friction is real and recurring.

Still excellent

The RTX 4090 remains a genuinely good local AI card. 24 GB runs 32B models at 4-bit, and 1,008 GB/s is within striking distance of current hardware.

It is out of production, so buying means the second-hand market. When prices are sensible it is frequently the best capability-per-dollar available. Verify the card’s history and inspect the power connector.

What actually determines performance

Capacity is a cliff, not a slope

The most common expensive mistake is buying a card that is almost big enough.

When a model does not fit, the runtime splits it — some layers on the GPU, the rest on the CPU — and the CPU portion runs across the PCIe bus at a fraction of the speed. Performance does not degrade gracefully. It falls off a cliff, typically by five to fifteen times.

This is why we recommend sizing for the model you actually want to run, with headroom, rather than buying the fastest card in budget and hoping.

Bandwidth sets the ceiling

Generating each token requires reading the model’s active weights out of memory. That makes generation memory-bandwidth-bound, and gives a ceiling you can calculate before buying:

theoretical tokens/second ≈ memory bandwidth ÷ model size in memory

A 19 GB model on an RTX 5090’s 1,792 GB/s ceilings out around 94 tokens/second; expect 60–75 in practice. The same model on an RTX 5060 Ti’s 448 GB/s ceilings around 23. Same model, same capacity, four times the speed difference — entirely explained by the bus width.

Compute matters for images, not text

Image generation inverts the hierarchy. Diffusion models are small enough that capacity is rarely the constraint, and the work is compute-bound rather than bandwidth-bound.

If Stable Diffusion or ComfyUI is your main workload, weight shader count and tensor throughput far more heavily, and 16 GB is generally sufficient.

What to look for

Buy the memory tier your workload needs, then optimise within it. Decide whether you are a 16, 24, 32 or 48 GB user first. Comparing a 16 GB card against a 24 GB card on price is comparing two different capabilities.

Check the bus width, not just the capacity. 16 GB on 256-bit and 16 GB on 128-bit are the same capacity and roughly a factor of two apart in speed.

Budget the whole system. A 575 W card needs a 1,000 W supply, adequate case airflow, and physical clearance. These are not optional extras.

Leave the PCIe slot argument alone. For single-GPU inference, PCIe generation and lane count barely matter — weights are transferred once. It becomes significant only when splitting a model across cards.

Consider the previous generation seriously. In AI work, memory capacity ages far more slowly than compute. A 24 GB card from two generations back can beat a current 16 GB card at the thing you actually want to do.

If none of these fit

If your requirement is 70B-class models and a workstation GPU is out of budget, a graphics card is probably the wrong purchase entirely.

A unified-memory machine — the Framework Desktop with 128 GB, a Mac Studio, or NVIDIA’s DGX Spark — will run models no consumer card can load, at a fraction of the power. They are much slower per token, but slow is infinitely faster than does-not-run. We cover the trade in Best AI Workstations.

Continue your research

• How Much VRAM Do You Need for Local AI? — size your requirement before you spend
• RTX 5090 vs RTX 5080 for AI — the two cards most people cross-shop
• NVIDIA vs AMD for Local AI — what the ecosystem gap actually costs
• Best GPUs for Ollama — the same hardware, assessed for one specific runtime