Best GPUs for Local LLM Inference (2026)

TL;DR

• Best overall: NVIDIA GeForce RTX 5090 — $8,215.00 (as of 11/06/2026 14:11 UTC - Details) · Check price on Amazon
• Best value: NVIDIA GeForce RTX 3090 (used) — $1,894.99 renewed (as of 11/06/2026 14:10 UTC - Details) · Check price on Amazon
• Best budget: NVIDIA GeForce RTX 5060 Ti 16GB — Check price on Amazon

What decides a local-LLM GPU is how much memory it has and how fast that memory is. The model's weights and its context must fit in VRAM, and token generation speed follows memory bandwidth: in Hardware Corner's llama.cpp tests a used RTX 3090 (936 GB/s) generated 87 tokens/s on Qwen3 8B, nearly the same as a new RTX 5070 Ti (896 GB/s, 88 tokens/s) and well ahead of the RTX 5060 Ti (448 GB/s, 51 tokens/s). [src1, src6, src10] The software matters as well: Tom's Hardware's September 2026 tests of a 27B model found llama.cpp slowing badly at long context on an RTX 5090 and RTX 4090, while SGLang ran almost three times faster than vLLM's single-card recipe on the same 5090. [src3]

The NVIDIA GeForce RTX 5090 (32GB GDDR7, 1,792 GB/s, $1,999 launch price) is the fastest consumer card for this. In Hardware Corner's tests it led every consumer GPU at every model size it ran, and processed prompts 2.7 times as fast as an RTX 3090; Hardware Corner calls it "the most balanced choice" for models up to 34B "before stepping into the inflated pricing of the prosumer segment". [src1, src4] Its 32GB does not open up new model sizes, though: Hardware Corner found it "won't unlock entirely new models compared to 24GB GPUs", only longer context, and StorageReview notes that the RTX 5090 "cannot load or execute a 120B model and often fail[s] to handle even 70B models". [src1, src8] It is also very hard to buy (see below).

For most people the better buy is a used NVIDIA GeForce RTX 3090. Its 24GB runs 30B-class models that no 16GB card can, Hardware Corner calls it "the smartest and most balanced single-card solution available", and XDA, writing on 2026-10-07 when used 3090s sold for around $1,000 and up on eBay, still calls it the GPU to buy for local AI. [src1, src13] The NVIDIA GeForce RTX 5060 Ti 16GB is the budget pick for 7B–14B models and gpt-oss 20B. [src1, src10] For 70B and 120B models on one card, the 96GB NVIDIA RTX PRO 6000 Blackwell is the only option the testers found workable. [src1, src8]

Best Overall: NVIDIA GeForce RTX 5090 (32GB) — Check price

In Hardware Corner's llama.cpp ranking the RTX 5090 was the fastest consumer card on every model it ran at 16K context: 145 tokens/s on Qwen3 8B, 103 on Qwen3 14B, 142 on the Qwen3 30B mixture-of-experts model, 51 on the dense Qwen3 32B and 249 on gpt-oss 20B, and it processed prompts at about 7,000 tokens/s on Qwen3 8B, 2.7 times the RTX 3090's rate. Hardware Corner calls it "the new speed leader for consumer GPUs". [src1] In a separate test at 4K context it processed prompts at 10,406 tokens/s on Qwen3 8B. [src2] StorageReview's Llama 3 text-generation test gave it 214 tokens/s against the RTX 4090's 150. [src5] Its specifications: 32GB of GDDR7 at 1,792 GB/s, a 575W board power and a $1,999 launch price. [src4]

Two limits. First, 32GB adds context rather than new model sizes: Hardware Corner found it loads the same models as a 24GB card, with longer context, and a 70B model at 4-bit does not fit. [src1, src8] Second, the software: in Tom's Hardware's tests of the 27B Qwen 3.8 model, llama.cpp took roughly 30 minutes to start answering at long context on one 5090, vLLM's single-card recipe allowed only 32K of context at about 20 tokens/s, and SGLang was almost three times faster than vLLM; Tom's Hardware's conclusion is that a single 5090 gives usable performance "if you … don't need long-context inference", but that you have to "choose your model runner carefully". [src3]

Availability: Tom's Hardware reported on 2026-09-14 that RTX 5090 stock had disappeared from US online retailers, leaving third-party sellers asking $6,500 to $9,500, and said it does not recommend buying from them. [src17] On 2026-10-10 it reported a leak that NVIDIA has stopped supplying the RTX 5090's chip to card makers; NVIDIA had not confirmed it. [src18] The listing we link had no purchasable offer in our daily checks through 2026-10-09.

Best you can buy today: the used RTX 3090 below, the testers' value pick (the renewed one we link was in stock from a marketplace seller in our 2026-10-09 check); or, new, the RTX 5080, in stock at Amazon.com in the same check, if 16GB is enough for your models. [src1, src7]

Best Value: NVIDIA GeForce RTX 3090 24GB (used) — Check price

Hardware Corner calls a used RTX 3090 "the top choice for a single-GPU setup, enabling larger 30B+ models at an unbeatable price-to-VRAM ratio". In its tests the 3090 ran 87 tokens/s on Qwen3 8B, "nearly identical to the newer RTX 5070 Ti", 114 on the Qwen3 30B mixture-of-experts model and 30 on the dense Qwen3 32B, and it still produced 62 tokens/s on gpt-oss 20B with 131K of context. Against the 5090 at 32K context it ran Qwen3 30B at 87 tokens/s to the 5090's 110, and Hardware Corner judges its speeds "entirely workable for most local inference tasks". [src1] Tom's Hardware ran the 27B Qwen 3.8 model on a 3090 in September 2026: it fits at 4-bit (Q4_K_M) with an 8-bit KV cache and up to about 112K of context, and the 3090, unlike the RTX 4090 and RTX 5090, did not suffer llama.cpp's long-context slowdown. Tom's Hardware also found that a 24GB card needs all of its memory for the model, so you want a second graphics card, or a headless server, to drive the display. [src3] Its specifications: 24GB of GDDR6X at 936 GB/s and 350W. [src4]

XDA, writing on 2026-10-07, puts recent eBay sold prices for used 3090s at around $1,000 and up, against $600 to $800 earlier in the year, and still concludes that "if you're really serious about running AI locally, the best choice continues to be the 3090, even with its price increases". [src13] Compute Market's advice: buy the used 3090 if you need 24GB, and the RTX 5060 Ti if you mainly run 7B–13B models and want a warranty, lower power and less noise. [src11] A used card has no manufacturer warranty. The renewed listing we link was in stock from a marketplace seller in our 2026-10-09 check.

Best Budget: NVIDIA GeForce RTX 5060 Ti 16GB — Check price

Hardware Corner calls the 16GB RTX 5060 Ti "the top value pick for budget builders": 51 tokens/s on Qwen3 8B against the RTX 4060 Ti 16GB's 34, and 82 tokens/s on gpt-oss 20B. Its 16GB handles 8B and 14B models and gpt-oss 20B, which Hardware Corner ran at up to 131K of context on it (44 tokens/s), but none of its 30B or 32B tests. [src1] Tom's Hardware measured 40% more tokens per second than the RTX 4060 Ti 16GB in the MLPerf Client text-generation test, and notes that the extra VRAM "definitely benefits AI workloads that use large language models". Its specifications: 16GB of GDDR7 at 448 GB/s, 180W, and a $429 launch price for the 16GB version ($379 for the 8GB). [src10] Compute Market recommends it for casual use and first setups, and the RTX 5070 Ti for people who use LLMs daily. [src12] The listing we link was in stock at Amazon.com in our 2026-10-09 check; the picks table has today's price.

Best for 70B–120B Models: NVIDIA RTX PRO 6000 Blackwell (96GB) — Check price

The only card here that holds 70B and 120B models. StorageReview measured 31.8 tokens/s on Llama 3.1 70B, 163 tokens/s on gpt-oss 120B and 197 tokens/s on Llama 3.1 8B with the Workstation Edition (96GB of GDDR7 with ECC at 1.79 TB/s, up to 600W, $8,500), and concludes that for long-context LLMs "the RTX PRO 6000 justifies its premium with capacity and consistency that other cards cannot match". [src8] Hardware Corner calls it "the only viable option for running the largest 70B-120B models": 28 tokens/s on Llama 70B and 182 on gpt-oss 120B at 16K context, and 1% to 10% slower than the RTX 5090 on the smaller models both ran. [src1] At gpt-oss 120B's full 131K context it still generated 83 tokens/s with Flash Attention on. [src9] Tom's Hardware's 2026-10-10 report says it "can easily sell for $15,000". [src18] The listing we link was in stock only from a marketplace seller in our 2026-10-09 check; the picks table has its price.

Previous-Generation 24GB: NVIDIA GeForce RTX 4090 — Check price

Faster than the RTX 3090 with the same 24GB: in Hardware Corner's tests it ran 104 tokens/s on Qwen3 8B and 164 on gpt-oss 20B, and processed prompts 2.6 times as fast as the 3090. [src1] StorageReview measured 150 tokens/s in its Llama 3 test, against the RTX 5090's 214. [src5] Tom's Hardware notes that the 4090 and 3090 are "evergreen favorites among local LLM fans" for their VRAM and used prices, but found the 4090 also hit llama.cpp's long-context slowdown with a 27B model. [src3] Its specifications: 24GB of GDDR6X at 1,008 GB/s, 450W, $1,599 at launch. [src4] NVIDIA reportedly stopped making the 4090's chip in late 2024. [src19] The listing we link had no purchasable offer in our daily checks through 2026-10-09.

Fastest 16GB: NVIDIA GeForce RTX 5080 — Check price

The fastest 16GB card here: 94 tokens/s on Qwen3 8B and 140 on gpt-oss 20B in Hardware Corner's tests, about 8% faster than the RTX 5070 Ti on Qwen3 8B. [src1] StorageReview calls it "the sweet spot in the 50-series lineup"; it measured 136 tokens/s in its Llama 3 test, against the RTX 5070 Ti's 132. [src7, src6] It has 16GB of GDDR7 at 960 GB/s and launched at $999. [src7] Like the other 16GB cards, it did not run Hardware Corner's 30B or 32B models. [src1] XDA put its price at around $1,600 on 2026-10-07. [src13] The listing we link was in stock at Amazon.com in our 2026-10-09 check.

16GB Step-Up: NVIDIA GeForce RTX 5070 Ti — Check price

70% faster than the RTX 5060 Ti on Qwen3 8B in Hardware Corner's tests (88 against 51 tokens/s), and 133 tokens/s on gpt-oss 20B, but with the same 16GB, which is why Hardware Corner judges its gains "faster, yes, but not transformative" for the extra cost. [src1] StorageReview found it "coming surprisingly close to the RTX 5080 in many benchmarks". [src6] It has 16GB of GDDR7 at 896 GB/s and launched at $749. [src6] Compute Market recommends it over the 5060 Ti if you use LLMs daily or run 13B–14B models regularly. [src12] XDA found the cheapest at $1,120 on 2026-10-07. [src13] The listing we link had low stock at Amazon.com in our 2026-10-09 check.

Best AMD Option: AMD Radeon RX 7900 XTX (24GB) — Check price

The 24GB AMD card. XDA's Adam Conway runs the 27B Qwen 3.6 model on one at 4-bit (about 18GB): with ROCm he measured about 285 tokens/s of prompt processing and 15 tokens/s of generation at around 90K of context, and about 25 tokens/s in a fresh chat; the Vulkan backend generated 37 tokens/s, roughly 30% faster than ROCm. [src14] Compute Market calls it "the best AMD GPU for local LLM inference under $1,000" (24GB of GDDR6 at 960 GB/s, 355W) and says ROCm 7.2 (March 2026) added native Ollama support for it; Linux is the main path, with Windows ROCm a version behind. [src15] XDA's view on 2026-10-07 is that it is "a credible choice for someone who only runs LLMs on Linux through ROCm", but that CUDA is still the way to get plug-and-play support. [src13] Earlier in 2026 XDA found used 7900 XTX cards selling for about 20% less than used RTX 3090s. [src16] We found no lab test of the 7900 XTX in the same runs as the NVIDIA cards above, so its speeds are not in the comparison table. The listing we link had no purchasable offer in our daily checks through 2026-10-09.

Head-to-Head Comparisons

RTX 5090 vs RTX 3090 (used)

The 5090 has 32GB against 24GB and almost twice the bandwidth (1,792 against 936 GB/s). [src4] In Hardware Corner's tests at 32K context it ran Qwen3 30B at 110 tokens/s to the 3090's 87 and gpt-oss 20B at 215 to 112, and it processed prompts 2.7 times as fast; Hardware Corner still finds the 3090's speeds "entirely workable", and both cards hold the same models, the 5090 with longer context. [src1] On price, XDA on 2026-10-07: the 5090 costs at least $5,000, a used 3090 around $1,000. [src13]

Pick the RTX 5090 if: you can find one from a real retailer and need the fastest prompt processing or the longest context.
Pick the RTX 3090 if: you want 24GB at a fifth of the price and accept a used card.

RTX 5090 vs RTX 4090

On the same models, the 5090 was 39% faster than the 4090 on Qwen3 8B and 52% faster on gpt-oss 20B in Hardware Corner's tests, and 43% faster in StorageReview's Llama 3 test; it also has 8GB more memory. [src1, src5] Both hit llama.cpp's long-context slowdown in Tom's Hardware's 27B tests. [src3] Neither is easy to buy: the 4090 reportedly went out of production in late 2024, and the listings we link for both had no purchasable offer in our daily checks through 2026-10-09. [src19]

Pick the RTX 5090 if: you want 32GB and the most speed.
Pick the RTX 4090 if: your models fit in 24GB and you find one used for well under a 5090.

RTX 5090 vs RTX PRO 6000 Blackwell

Both use NVIDIA's GB202 chip with 1.79 TB/s of bandwidth; the PRO 6000 has more of the chip enabled and 96GB of ECC memory against 32GB. [src8] On models both can hold, the PRO 6000 was 1% to 10% slower in Hardware Corner's tests (141 against 145 tokens/s on Qwen3 8B); it also runs Llama 70B and gpt-oss 120B, which the 5090 cannot hold. [src1, src8] The PRO 6000 launched at $8,500 against $1,999. [src8, src4]

Pick the RTX 5090 if: your models are 34B or smaller.
Pick the RTX PRO 6000 if: you need 70B or 120B models on one card.

RTX 5060 Ti vs RTX 5070 Ti

Both have 16GB of GDDR7, so they hold the same models; the 5070 Ti was 70% faster on Qwen3 8B (88 against 51 tokens/s) and 61% faster on gpt-oss 20B in Hardware Corner's tests. [src1] They launched at $429 and $749. [src10, src6] Hardware Corner judges the 5070 Ti's extra speed not worth the extra cost for users limited to 16GB; Compute Market recommends it for daily users. [src1, src12]

Pick the RTX 5060 Ti if: you want the cheapest capable 16GB card.
Pick the RTX 5070 Ti if: you use LLMs all day and want faster replies.

RTX 3090 vs RX 7900 XTX

Both have 24GB. The 3090 runs CUDA, which XDA calls the way to get plug-and-play support; the 7900 XTX runs best on Linux through ROCm or Vulkan, where XDA's own test got 37 tokens/s from a 27B model. [src13, src14] Earlier in 2026 XDA found used 7900 XTX cards about 20% cheaper than used 3090s. [src16] No outlet we read tested both in the same run.

Pick the RTX 3090 if: you want the widest software support, or use Windows.
Pick the RX 7900 XTX if: you run Linux and want 24GB for less.

Decision Logic

If you want the fastest card and can buy an RTX 5090 from a real retailer

→ The RTX 5090: the fastest consumer card in every test we read, with 32GB for long context. Avoid marketplace sellers asking several times its $1,999 launch price; Tom's Hardware advises against them. [src1, src4, src17]

If you want 24GB for 27B–32B models at the lowest cost

→ A used RTX 3090. It holds 30B-class models at 4-bit, generates about as fast as an RTX 5070 Ti on 8B models, and costs around $1,000 used (XDA, 2026-10-07). [src1, src3, src13]

If you mainly run 7B–14B models or gpt-oss 20B, on the tightest budget

→ The RTX 5060 Ti 16GB, $429 at launch; buy the 16GB version. If you use LLMs all day, the RTX 5070 Ti or RTX 5080 are 70% to 83% faster on Qwen3 8B with the same 16GB. [src1, src10]

If you need 70B or 120B models on one card

→ The RTX PRO 6000 Blackwell (96GB): 28 to 32 tokens/s on Llama 70B and 163 to 182 on gpt-oss 120B in two labs' tests. No 32GB or smaller card here holds them. [src1, src8]

If you run Linux and want AMD

→ The RX 7900 XTX (24GB), on the Vulkan backend for the fastest generation in XDA's test. [src14, src15]

If you want 32GB for less than an RTX 5090

→ No pick here. Intel's 32GB Arc Pro B70 launched at $949, but StorageReview found its software "the biggest challenge", and Hardware Corner found it slower than a used RTX 3090 for a single user. AMD's 32GB Radeon AI PRO R9700 is aimed at teams: Puget Systems recommends a pair for serving models up to 27B. [src20, src21, src22]

Default recommendation

→ A used RTX 3090 for most people; the RTX 5060 Ti 16GB if you only run models up to 14B or want a new card with a warranty; the RTX 5090 if you can find one at a sane price and need the speed. [src1, src11, src13]

What the testers actually ran at 4-bit in each memory size:

Critical note: context costs memory on top of the weights. On 24GB cards Tom's Hardware had to quantize the KV cache to 8-bit and stop at about 112K of context to fit a 27B model, and a 24GB card used for the model cannot also drive your display. [src3]

• The RTX 5090 has all but left US retail: Tom's Hardware reported on 2026-09-14 that online stock had disappeared, with third-party sellers asking $6,500 to $9,500, and on 2026-10-10 relayed an unconfirmed report that NVIDIA has stopped supplying its chip for GeForce cards. [src17, src18]
• Prices are far above launch: XDA's prices on 2026-10-07 were at least $5,000 for an RTX 5090, about $1,600 for an RTX 5080 and $1,120 for the cheapest RTX 5070 Ti, against launch prices of $1,999, $999 and $749. [src13, src4, src7, src6]
• Used 24GB cards got dearer: XDA saw used RTX 3090s go from $600–$800 earlier in 2026 to around $1,000 and up by October. [src13]
• No 24GB mid-range refresh yet: Tom's Hardware reports that a 24GB RTX 5080 Super was postponed by the memory shortage, and that a leak names a 24GB RTX 5080 as the next GeForce flagship; neither is confirmed. [src18]
• 32GB workstation cards for less: Intel's Arc Pro B70 ($949 at launch, over $1,100 in July) and AMD's Radeon AI PRO R9700 bring 32GB below RTX 5090 prices. StorageReview found the B70's software its biggest challenge and Hardware Corner found it slower than a used RTX 3090 for a single user; Puget Systems recommends R9700s in pairs, for teams serving models up to 27B. [src13, src20, src21, src22]
• AMD software caught up on Linux: ROCm 7.2 (March 2026) added native Ollama support for the RX 7900 XTX, and the Vulkan backend can be faster still for generation. [src15, src14]
• The model runner matters as much as the card: Tom's Hardware found llama.cpp, vLLM and SGLang giving very different speed and context on the same RTX 5090. [src3]

• The listings we link are partner boards: an ASUS ROG Astral RTX 5090, a renewed Gigabyte RTX 3090 Turbo (a blower design), an ASUS Dual RTX 5060 Ti 16GB, PNY's RTX PRO 6000, a PNY Verto RTX 4090, ASUS TUF Gaming RTX 5080 and RTX 5070 Ti cards, and an ASRock Phantom Gaming RX 7900 XTX. The outlets mostly tested other boards with the same GPUs, such as NVIDIA's own RTX 5090 Founders Edition and an ASUS Prime RTX 5070 Ti. [src4, src5, src6]
• Tokens-per-second figures depend on the runner, model, quantization and context length. Hardware Corner's are llama.cpp on Ubuntu with 4-bit models; other runners and settings give different numbers. [src1, src3]
• A used card has no manufacturer warranty. [src11]
• For the most-sought cards, buy from the retailer itself: Tom's Hardware warned of scams and inflated third-party listings for the RTX 5090. [src17]
• AMD cards are best run on Linux; Windows ROCm support lags. [src15]