Best GPU for Local LLMs in 2026
Updated August 14, 2026. All prices are launch MSRPs from our GPU database; we do not track street prices.
How much GPU you need depends on which model classes you want to run, and this guide maps that decision to specific cards.
Want the full tier-by-tier breakdown — every current GPU from 12 GB to 96 GB, minimum vs recommended configs per model size, and power/price brackets? See the Best GPUs for LLM Inference pillar guide.
For running local LLMs in 2026, the NVIDIA GeForce RTX 5090 is the best overall GPU because its 32 GB of GDDR7 memory holds large quantized models that smaller cards cannot fit. The best value pick is a used GeForce RTX 3090 with 24 GB of VRAM, and the cheapest usable new card is the Intel Arc B580 at a $249 MSRP. This guide ranks the best GPU for each budget using verified specification data and third-party benchmark results.
How do the best GPUs for local LLMs compare?
The table below compares seeded specifications and Llama-3-8B inference speed for the GPUs most often used with local LLMs. Tok/s figures were measured with Q4 quantization in llama.cpp.
| GPU | VRAM | Memory type | Bandwidth | TDP | MSRP | Llama-3-8B Q4 (tok/s) |
|---|---|---|---|---|---|---|
| GeForce RTX 5090 | 32 GB | GDDR7 | 1,792 GB/s | 575 W | $1,999 | 320 |
| GeForce RTX 4090 | 24 GB | GDDR6X | 1,008 GB/s | 450 W | $1,599 | 220 |
| Radeon RX 7900 XTX | 24 GB | GDDR6 | 960 GB/s | 355 W | $999 | 140 |
| GeForce RTX 3090 | 24 GB | GDDR6X | 936 GB/s | 350 W | $1,499 | 150 |
| GeForce RTX 5080 | 16 GB | GDDR7 | 960 GB/s | 360 W | $999 | 180 |
| GeForce RTX 4060 Ti 16GB | 16 GB | GDDR6 | 288 GB/s | 160 W | $499 | 95 |
| Arc B580 | 12 GB | GDDR6 | 456 GB/s | 190 W | $249 | 60 (est.) |
Benchmark attribution: RTX 5090, RTX 4090, and RX 7900 XTX figures are from Tom's Hardware GPU benchmarks; RTX 5080, RTX 4060 Ti 16GB, and Arc B580 figures are from TechPowerUp (the Arc B580 result is marked as an estimate); the RTX 3090 figure is from Puget Systems. All benchmark data was recorded in June 2025 in our benchmark database.
Which GPU is best for 70B-class models?
Best for 70B-class models: GeForce RTX 5090. Its 32 GB of GDDR7 memory is the largest consumer VRAM pool in our database, and its 1,792 GB/s of bandwidth is the highest of any consumer card listed here.
According to Tom's Hardware benchmark data, the RTX 5090 runs Llama-3-70B at Q4 quantization at 42 tokens per second in llama.cpp, with test notes describing the 32 GB card as a tight but workable fit for that model. The same source measured a GeForce RTX 4090 at 18 tokens per second on Llama-3-70B and flagged the result as an estimate, noting heavy memory swapping on its 24 GB of VRAM.
In practice, that means models in the 70B class generally need more memory than a single 24 GB card provides for comfortable use, and the RTX 5090 is the consumer card that gets closest to running them entirely in VRAM. Buyers who need to run models larger than that should look at workstation cards with 48 GB such as the RTX 6000 Ada, or at Apple's M3 Ultra with up to 512 GB of unified memory.
Which GPU is best on a mid-range LLM budget?
Best mid-range pick: GeForce RTX 4060 Ti 16GB. It carries 16 GB of GDDR6 memory at a $499 MSRP, which is the cheapest new NVIDIA card in our database with that much VRAM.
According to TechPowerUp benchmarks, the RTX 4060 Ti 16GB runs Llama-3-8B at Q4 at 95 tokens per second in llama.cpp. Its 288 GB/s of bandwidth is the lowest in this comparison, so it is slower than pricier cards, but its 160 W TDP also means it runs in most desktop systems without a power supply upgrade. It is a good match for buyers who mostly run models in the 7B to 14B class and want a new card with a warranty.
Which GPU is the cheapest way to run local LLMs?
Best budget pick: Intel Arc B580. At a $249 MSRP with 12 GB of GDDR6 memory, it is the least expensive new card in our database that can hold 7B-class models at Q4 quantization.
According to TechPowerUp benchmark data, the Arc B580 runs Llama-3-8B at Q4 at roughly 60 tokens per second in llama.cpp, although that figure is marked as an estimate in our database. The card runs local LLM software through the Vulkan backend rather than CUDA, so some tooling that assumes NVIDIA hardware will need configuration. For buyers testing local LLMs on a tight budget, it is a reasonable starting point despite those caveats.
Which AMD GPU is best for local LLMs?
Best AMD pick: Radeon RX 7900 XTX. It pairs 24 GB of GDDR6 memory with an $999 MSRP, which is the lowest launch price in our database for any card with 24 GB.
According to Tom's Hardware benchmarks recorded with AMD's ROCm stack, the RX 7900 XTX runs Llama-3-8B at Q4 at 140 tokens per second in llama.cpp. That is comparable to the GeForce RTX 3090's 150 tokens per second from Puget Systems testing. The trade-off is software support: CUDA remains the default target for most local AI tooling, while AMD relies on ROCm and the Vulkan backend, which our benchmark notes describe as functional but less widely supported. Buyers who already prefer AMD for other workloads can run local LLMs on this card without major compromises.
Why do LLM buyers keep choosing the used RTX 3090?
Best value pick: used GeForce RTX 3090. It offers 24 GB of GDDR6X memory, which matches the VRAM of the RTX 4090 at a much lower typical used price.
Our database lists the RTX 3090 as an end-of-life 2020 card with a $1,499 launch MSRP, 936 GB/s of bandwidth, and a 350 W TDP. According to Puget Systems benchmark data, it still runs Llama-3-8B at Q4 at 150 tokens per second in llama.cpp, which outpaces every new card under $1,000 in the table above. The caveats are the risks of any used purchase: no warranty, unknown treatment by previous owners, and a power draw that is high for a card of its age. For buyers whose priority is VRAM capacity per dollar, it remains the classic recommendation, and our used RTX 3090 buying guide covers how to evaluate individual cards.
How much VRAM do I need for local LLMs?
VRAM capacity decides which models fit at all, while memory bandwidth decides how fast they run. A slower card with enough VRAM beats a faster card that cannot hold the model.
Concrete benchmark evidence from our database shows the pattern. Models in the 8B class run at Q4 quantization on every card in the table above, including the 12 GB Arc B580. Models in the 70B class are different: Tom's Hardware test notes describe Llama-3-70B at Q4 as a tight fit even on the RTX 5090's 32 GB and as heavy swapping on the RTX 4090's 24 GB. In general, 70B-class models need more memory than a single 24 GB card provides for comfortable use, while 8B-class models fit comfortably on 12 GB cards.
For larger models still, the practical options are workstation cards with 48 GB, multi-GPU setups, or Apple Silicon: the M3 Ultra Mac Studio supports up to 512 GB of unified memory per our specification database, which no consumer NVIDIA or AMD card approaches.
What are the trade-offs between NVIDIA, AMD, and Intel for LLMs?
NVIDIA's CUDA is the default software target for local AI tools, so NVIDIA cards have the fewest compatibility problems. AMD cards run llama.cpp well through ROCm and Vulkan but need more configuration, and Intel's Arc cards work through Vulkan with the youngest software ecosystem of the three.
Those differences show up in our benchmark notes rather than in raw speed. The RX 7900 XTX's 140 tokens per second on Llama-3-8B is genuinely competitive, but the measurement required the ROCm stack. Buyers who enjoy tinkering get better VRAM per dollar outside NVIDIA; buyers who want everything to work on the first attempt should stay on NVIDIA.
What are the pros and cons of the top picks?
Each pick trades something away. Here is the short version of what you gain and what you accept with each recommendation.
- RTX 5090: most consumer VRAM (32 GB) and highest bandwidth (1,792 GB/s); costs $1,999 at MSRP and draws 575 W.
- Used RTX 3090: 24 GB of VRAM at a low used price with 150 tok/s on 8B models; end-of-life, no warranty, 350 W draw.
- RTX 4060 Ti 16GB: cheapest new 16 GB NVIDIA card at $499 with a 160 W TDP; only 288 GB/s of bandwidth limits speed.
- Arc B580: $249 entry price with 12 GB of VRAM; estimate-tier performance and Vulkan-only software path.
- RX 7900 XTX: 24 GB at a $999 MSRP with competitive inference speed; ROCm and Vulkan require more setup than CUDA.
The pattern is consistent: within one brand, you pay mainly for VRAM capacity and bandwidth, and the steepest prices buy capacity that only matters for the largest models.
Who should NOT buy a flagship GPU for local LLMs?
Buyers who only run 7B-class and 8B-class models should not buy an RTX 5090 or any 24 GB card for LLMs alone. The benchmark data shows those models run at 60 to 95 tokens per second on cards costing a fraction of a flagship, which is already faster than most people read.
Likewise, buyers who need 70B-class models only occasionally should first compare against renting: our cloud pricing database lists single RTX 4090 instances from $0.34 per hour on RunPod, which covers infrequent large-model sessions without any hardware purchase. Flagship VRAM pays off only when large models run daily or data must stay offline.
How did we rank these GPUs?
We ranked the cards using the specification set in our GPU database, which stores manufacturer datasheet values for VRAM, memory type, bandwidth, TDP, and launch MSRP. Speed claims come from our benchmark database entries recorded in June 2025 from Tom's Hardware, TechPowerUp, and Puget Systems, each labeled with its source and estimate status.
We deliberately report MSRP only, never street or used prices, because those change constantly. Rankings weigh VRAM capacity first for LLM use, then bandwidth, then power draw, then price. Affiliate links do not influence any ranking on this site.
Frequently Asked Questions
How much VRAM do I need for local LLMs?
For 7B-class and 8B-class models at Q4 quantization, 12 GB is enough according to our benchmark notes. For 70B-class models, even 24 GB produces heavy swapping in Tom's Hardware testing, so 32 GB or more is the safer target.
Is the RTX 3090 still good for local LLMs in 2026?
Yes. It carries 24 GB of VRAM and runs Llama-3-8B at Q4 at 150 tokens per second in Puget Systems testing, which is faster than every new card under $1,000 in our comparison. Its drawbacks are age, a 350 W TDP, and the lack of any warranty when bought used.
Can I run local LLMs on AMD or Intel GPUs?
Yes. The RX 7900 XTX runs Llama-3-8B at Q4 at 140 tokens per second via ROCm, and the Arc B580 manages roughly 60 tokens per second as an estimate via Vulkan. Expect more configuration effort than on CUDA-based cards.
Does memory bandwidth matter for LLM inference?
Yes, bandwidth is the main speed limiter once a model fits in VRAM. The RTX 5090 combines 1,792 GB/s with 32 GB, which is why it leads every benchmark row in our table.
Is Apple Silicon a good alternative for local LLMs?
For very large models, yes. The M3 Ultra Mac Studio supports up to 512 GB of unified memory per our database, which exceeds any consumer GPU. Its 819 GB/s of bandwidth is below the RTX 5090's 1,792 GB/s, so models that fit both platforms run faster on the NVIDIA card.
Sources
Specifications are manufacturer datasheet values from our GPU database; benchmark figures are from the named third-party sources below.
- Tom's Hardware GPU Benchmarks 2025 — https://www.tomshardware.com/pc-components/gpus
- TechPowerUp GPU Reviews — https://www.techpowerup.com/reviews/
- Puget Systems Hardware Testing — https://www.pugetsystems.com/labs/
- NVIDIA Official Specifications — https://www.nvidia.com/en-us/data-center/
Related reading: our VRAM calculator, the deep learning training GPU guide, and the used RTX 3090 buying guide, and our eGPU setup guide.
Disclosure: CompareAIHardware.com participates in the Amazon Associates program and earns from qualifying purchases through links on this page. Affiliate relationships do not influence our recommendations.
CompareAIHardware.com participates in the Amazon Associates program. As an Amazon Associate, we earn from qualifying purchases. This guide reflects our independent analysis — affiliate relationships do not influence our recommendations.