Which consumer GPU is best for LLM, AI and deep learning?

The NVIDIA RTX 5090 is currently the best for LLM and AI.

How does VRAM affect LLM performance?

More VRAM lets you run larger models fully on GPU without CPU offloading, which greatly improves tokens per second. Our LLM Performance hub covers VRAM limits and benchmarks.

Where can I find LLM benchmarks and performance guides?

Our LLM Performance hub covers throughput vs latency, VRAM limits, parallel requests, and benchmarks across runtimes and hardware.

Is 16GB VRAM enough for running LLMs locally?

Yes for many 7B–14B models. Larger models may use CPU offloading and run slower. The LLM Performance section has benchmarks for 16GB GPUs.

Comparing NVidia GPU suitability for AI

AI requires a lot of power...

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In the midst of the modern world’s turmoil here I’m comparing tech specs of different cards suitable for AI tasks (Deep Learning, Object Detection and LLMs). They are all incredibly expensive though.

For more on how GPU choice affects LLM throughput, VRAM limits, and benchmarks across runtimes, see LLM Performance: Benchmarks, Bottlenecks & Optimization. For a broader 2026 buying guide that includes AMD and Intel options, see GPUs for AI in 2026: NVIDIA, AMD, Intel Compared.

Graphics cards image generated by AI running on GPU

This Is AI-generated image. Don’t take it seriously…

Let’s have a look at other options, just to have a look around

Card	VRAM	Bus Width	Memory Bandwidth	CUDA Cores	Tensor Cores	Power (W)
RTX 4060 Ti 16GB	16 GB	128-bit	288 GB/s	4,352	136	165
RTX 4070 Ti 16GB	16 GB	256-bit	672 GB/s	7,680	240	285
RTX 4080 16GB	16 GB	256-bit	716.8 GB/s	9,728	304	320
RTX 4080 Super 16GB	16 GB	256-bit	736 GB/s	10,240	320	320
RTX 4090 24GB	24 GB	384-bit	1008 GB/s	16,384	512	450
RTX 5060 Ti 16GB	16 GB	128-bit	448 GB/s	4,608	144	180
RTX 5070 Ti 16GB	16 GB	256-bit	896 GB/s	8,960	280	300
RTX 5080 16GB	16 GB	256-bit	896 GB/s	10,752	336	~320
RTX 5090 32GB	32 GB	512-bit	1792 GB/s	21,760	680	~450
RTX 2000 Ada	16 GB	128-bit	224 GB/s	2,816	88	70
RTX 4000 Ada	20 GB	160-bit	280 GB/s	6,144	192	70
RTX 4500 Ada	24 GB	192-bit	432 GB/s	7,680	240	210
RTX 5000 Ada	32 GB	256-bit	576 GB/s	12,800	400	250
RTX 6000 Ada	48 GB	384-bit	960 GB/s	18,176	568	300

Estimated LLM speed by GPU (Qwen 3.6 27B baseline)

The table below estimates generation and prompt throughput from a single measured baseline (RTX 4080 16GB), then scales results using CUDA core count and memory bandwidth. This gives a practical planning reference when comparing NVIDIA cards for local inference.

GPU	CUDA Cores	Bandwidth	Est. Gen (t/s)	Est. Prompt (t/s)	Est. MTP max 2 Gen	Est. MTP max 2 Prompt
RTX 4080 16GB	8,192	912 GB/s	45 (measured)	200 (measured)	75 (measured)	151 (measured)
RTX 4090 24GB	12,000	1,008 GB/s	~50	~260	~82	~220
RTX 5060 Ti 16GB	4,608	448 GB/s	~22	~100	~38	~76
RTX 5070 12GB	6,144	672 GB/s	~34	~140	~58	~114
RTX 5070 Ti 16GB	8,960	896 GB/s	~44	~190	~74	~145
RTX 5080 16GB	10,752	960 GB/s	~45	~250	~76	~195
RTX 5090 32GB	21,760	1,792 GB/s	~85	~390	~140	~310
RTX PRO 4000 24GB	6,144	672 GB/s	~34	~140	~58	~114
RTX PRO 4500 32GB	7,680	896 GB/s	~44	~175	~74	~138
RTX PRO 5000 48GB	12,800	1,344 GB/s	~60	~300	~100	~240
RTX PRO 6000 96GB	21,760	1,792 GB/s	~85	~390	~140	~310

These numbers are rough estimates, not benchmark results for every card. Actual speed depends on model size, quantisation, context length, and software stack. Performance can drop sharply if a model does not fit fully in VRAM and starts offloading to CPU memory.

For measured cross-platform results, see NVIDIA DGX Spark vs Mac Studio vs RTX-4080. For VRAM-fit model choices, see Best LLM on 16 GB VRAM GPU.

Memory Bandwidth:

RTX 5090 (1792 GB/s), then RTX 4090(1008 GB/s), then RTX 6000 Ada (960 GB/s)

Tensor Cores:

RTX 5090 (680), then RTX 6000 Ada (568), then RTX 4090 (512)

CUDA Cores

RTX 5090 (21,760), then RTX 6000 Ada (18,176, then RTX 4090 (16,384)

RAM

RTX 6000 Ada (48 GB), then RTX 5090 and RTX 5000 Ada (32 GB), then RTX 4090 (24GB)

Pricing in Australia

RTX 6000 Ada: 12,000 AUD
RTX 5090: 6,000 AUD
RTX 5000 Ada: 7,000 AUD
RTX 4090: sold out

LLM best counsumer GPU

Still I think RTX 5090 would best choice for machine learning, deep learning, AI and even LLM :)…

Real Prices

A bit pricey…

NVidia RTX 5090 page

And real RTX 5090 prices are 50% more then expected. Look at this!

That’s on 15/05/2025

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To explore LLM benchmarks, VRAM requirements, and performance tuning on different GPUs and runtimes, check our LLM Performance: Benchmarks, Bottlenecks & Optimization hub. For multi-GPU and platform planning, see AI Infrastructure on Consumer Hardware and LLM Performance and PCIe Lanes.

Let’s have a look at other options, just to have a look around

Estimated LLM speed by GPU (Qwen 3.6 27B baseline)

Pricing in Australia

LLM best counsumer GPU

Real Prices

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