NVIDIA GeForce RTX 4060 Ti 8GB
8 GB · 288 GB/s · 160 W
In short
The NVIDIA GeForce RTX 4060 Ti 8GB has 8 GB of GDDR6 memory with 288 GB/s of bandwidth, which sets how fast it generates text. At 32k context it runs 36 of the 129 open models we track fully in memory; the most capable is gpt-oss 20B at MXFP4, at about 33 tok/s (estimated). The best new price for the NVIDIA GeForce RTX 4060 Ti 8GB in the US is $440 at eBay; used, it starts at $430 (asking price).
Updated . Speeds are estimates; how they are worked out.
Prices
Every retailer we track in the US, cheapest first. Buy links may earn us a commission.
| Retailer | Condition | Price | Stock | Checked | Buy |
|---|---|---|---|---|---|
| eBayMSI GeForce RTX 4060 Ti Ventus 3X 8GB GDRR6 OC 2580MHz Gaming Graphics Card | used, asking price | $430 | In stock | 5 h ago | Buy |
| eBayMSI GeForce RTX 4060 Ti VENTUS 2X BLACK 8G OC PCIe4 Video Graphics Card WARRANTY | refurbished | $439 | In stock | 5 h ago | Buy |
| eBayASUS Dual GeForce RTX 4060 Ti EVO OC 8GB GDDR6 DLSS 3 | New | $440 | In stock | 5 h ago | Buy |
| eBayUsed GeForce RTX 4060 Ti 8GB on eBay | used, asking price | $450 | In stock | 5 h ago | Buy |
| eBayPNY RTX 4060 Ti 8GB XLR8 Gaming VERTO EPIC-X RGB Triple Fan Graphics Card | refurbished | $450 | In stock | 5 h ago | Buy |
| eBayASUS Dual OC GeForce RTX 4060 Ti 8GB RAM | refurbished | $510 | In stock | 5 h ago | Buy |
| eBayMSI GeForce RTX 4060 Ti 8GB Gaming X GPU | refurbished | $550 | In stock | 5 h ago | Buy |
| eBayZotac GeForce RTX 4060 Ti 8GB Twin Edge OC D40610H | refurbished | $550 | In stock | 5 h ago | Buy |
| eBayAsus GeForce RTX 4060 Ti 8GB Dual OC GPU | refurbished | $550 | In stock | 5 h ago | Buy |
| eBayNew Gigabyte GeForce RTX 4060 Ti WINDFORCE OC 8GB GDDR6 Graphics Card | New | $586 | In stock | 5 h ago | Buy |
| eBayASUS TUF GeForce RTX 4060 Ti V2 OC Edition 8GB GDDR6 Graphics Card... | New | $610+ $6 delivery | In stock | 5 h ago | Buy |
| eBayMSI NVIDIA GeForce RTX 4060 Ti VENTUS 3X OC 8GB GDDR6 Triple Fan PCIe 4.0 Sealed | New | $700+ $20 delivery | In stock | 5 h ago | Buy |
| eBayGIGABYTE RTX 4060 Ti 8GB GDDR6 PCIe 4.0 GV-N406TWF2OCV2-8GD Unused | New | $1,100 | In stock | 5 h ago | Buy |
| eBayGigabyte GeForce RTX 4060 Ti WINDFORCE OC 8GB Graphics Card | New | $1,206 | In stock | 5 h ago | Buy |
Used on eBay: median asking price $450, most between $420 and $500 (21 listings, 2026-11-03). Used price history
Price history
Lowest price shown each day over the last 90 days.
Not enough price history yet.
What two of them run
Every model in the catalogue, with the best quant for this hardware. Estimates, not measurements.
| Model | Quant | Fits | Memory | Max context | Decode @32k | Prefill | Good for |
|---|---|---|---|---|---|---|---|
| gpt-oss 20B OpenAI | MXFP4 | fits | 13.3 GB | 82k128k with q8 cache | 33tok/sest. | 854 tok/s | chat, coding, agents, long documents |
| Gemma 4 12B | Q8_0 | fits | 13.9 GB | 73k122k with q8 cache | 13tok/sest. | 371 tok/s | – |
| Qwen 3.5 9B Alibaba Qwen | Q8_0 | fits | 11.2 GB | 128k212k with q8 cache | 17tok/sest. | 463 tok/s | chat |
| Ministral 3 14B Mistral AI | Q4_K_M | fits | 14.0 GB | 37k67k with q8 cache | 13tok/sest. | 257 tok/s | chat |
| Qwen3 14B Alibaba Qwen | Q4_K_M | fits | 14.7 GB | 32k40k with q8 cache | 12tok/sest. | 231 tok/s | chat |
| DeepSeek R1 0528 Qwen3 8B DeepSeek | Q8_0 | fits | 13.9 GB | 38k69k with q8 cache | 13tok/sest. | 381 tok/s | chat |
| Qwen3 8B Alibaba Qwen | Q8_0 | fits | 13.9 GB | 38k40k with q8 cache | 13tok/sest. | 381 tok/s | chat |
| Qwen 3.5 4B Alibaba Qwen | Q8_0 | fits | 6.5 GB | 255k256k with q8 cache | 32tok/sest. | 866 tok/s | chat, coding, long documents |
| Granite 4.2 8B IBM | Q6_K | fits | 13.3 GB | 41k75k with q8 cache | 14tok/sest. | 351 tok/s | chat |
| Ministral 3 8B Mistral AI | Q8_0 | fits | 13.9 GB | 38k69k with q8 cache | 13tok/sest. | 368 tok/s | chat |
| Gemma 4 E4B | Q8_0 | fits | 9.8 GB | 128k | 20tok/sest. | 581 tok/s | chat |
| LFM2.5 8B-A1B Liquid AI | Q8_0 | fits | 11.2 GB | 100k125k with q8 cache | 40tok/sest. | 1902 tok/s | chat, coding |
| Llama 3.1 8B Meta | Q6_K | fits | 11.4 GB | 57k104k with q8 cache | 16tok/sest. | 403 tok/s | chat |
| Qwen2.5 0.5B Alibaba Qwen | Q8_0 | fits | 2.2 GB | 32k | 191tok/sest. | 4142 tok/s | chat, coding, long documents |
| Qwen3.5 0.8B Alibaba Qwen | Q8_0 | fits | 2.4 GB | 256k | 145tok/sest. | 3891 tok/s | chat, coding, agents, long documents |
| Qwen3.5 2B Alibaba Qwen | Q5_K_M | fits | 3.0 GB | 256k | 96tok/sest. | 1855 tok/s | chat, coding, agents, long documents |
| LFM3 1.7B Liquid AI | Q8_0 | fits | 3.4 GB | 256k | 79tok/sest. | 2309 tok/s | chat, coding, agents, long documents |
| DeepSeek R1 Distill Qwen 1.5B DeepSeek | Q8_0 | fits | 3.9 GB | 128k | 63tok/sest. | 1558 tok/s | chat, coding, long documents |
| MiniCPM5 2B openbmb | Q8_0 | fits | 5.1 GB | 128k | 44tok/sest. | 931 tok/s | chat, coding, agents, long documents |
| Spark X2.5 4B XHToken | mlx-4bit | fits | 5.3 GB | 263k441k with q8 cache | 42tok/sest. | 914 tok/s | chat, coding, agents, long documents |
| LFM2.5 2.6B Liquid AI | Q8_0 | fits | 5.8 GB | 128k | 37tok/sest. | 1054 tok/s | chat, coding, agents, long documents |
| LFM2.5 VL 3B Liquid AI | Q8_0 | fits | 5.8 GB | 32k | 37tok/sest. | 967 tok/s | chat, coding, agents, long documents |
| Kimi VL A3B Thinking 2506 Moonshot AI | Q4_K_M | fits | 12.1 GB | 109k128k with q8 cache | 36tok/sest. | 1032 tok/s | chat, coding, long documents |
| Mellum2.1 12B A2.5B Thinking JetBrains | Q8_0 | fits | 13.8 GB | 83k125k with q8 cache | 34tok/sest. | 1373 tok/s | chat, coding, agents, long documents |
| Kimi VL A3B Moonshot AI | mlx-5bit | fits | 13.3 GB | 75k123k with q8 cache | 34tok/sest. | 868 tok/s | chat, coding, agents |
| FrogNano 4B 2609 Microsoft | Q8_0 | fits | 6.6 GB | 251k256k with q8 cache | 31tok/sest. | 866 tok/s | chat, coding, agents, long documents |
| granite vision 4.1 4b IBM | Q8_0 | fits | 7.2 GB | 122k128k with q8 cache | 28tok/sest. | 675 tok/s | chat, coding |
| granite 4.2 3b IBM | Q8_0 | fits | 7.4 GB | 119k128k with q8 cache | 27tok/sest. | 708 tok/s | chat, coding |
| Ministral 3 3B Instruct 2512 Mistral AI | Q8_0 | fits | 7.9 GB | 95k172k with q8 cache | 25tok/sest. | 677 tok/s | chat, coding |
| GLM 4 9B 0414 Z.ai | mlx-5bit | fits | 10.2 GB | 32k | 19tok/sest. | 336 tok/s | chat |
| DeepSeek R1 Distill Qwen 7B DeepSeek | Q8_0 | fits | 10.6 GB | 101k128k with q8 cache | 18tok/sest. | 455 tok/s | chat |
| GLM 4.6V Flash Z.ai | Q8_0 | fits | 11.9 GB | 97k128k with q8 cache | 16tok/sest. | 317 tok/s | chat |
| DeepSeek R1 Distill Llama 8B DeepSeek | Q8_0 | fits | 13.2 GB | 43k79k with q8 cache | 14tok/sest. | 403 tok/s | chat |
| GLM 4.1V 9B Thinking Z.ai | mlx-8bit | fits | 13.5 GB | 60k64k with q8 cache | 14tok/sest. | 317 tok/s | – |
| Ministral 3 14B Reasoning 2512 Mistral AI | Q4_K_M | fits | 14.0 GB | 37k67k with q8 cache | 13tok/sest. | 257 tok/s | chat |
| Ministral 3 8B Reasoning 2512 Mistral AI | Q8_0 | fits | 13.9 GB | 38k69k with q8 cache | 13tok/sest. | 368 tok/s | chat |
| Qwen 3.8 27B Alibaba Qwen | UD-Q4_K_M | offload | 18.8 GB | – | 6tok/sest. | 138 tok/s | – |
| Qwen 3.6 27B Alibaba Qwen | Q4_K_M | offload | 19.1 GB | – | 6tok/sest. | 137 tok/s | – |
| Gemma 4 31B | Q4_K_M | offload | 21.6 GB | – | 5tok/sest. | 114 tok/s | – |
| Mistral Small 4 119B Mistral AI | UD-Q2_K_XL | offload | 39.4 GB | – | 14tok/sest. | 256 tok/s | chat |
| Qwen 3.6 35B-A3B Alibaba Qwen | MXFP4 | offload | 22.2 GB | – | 23tok/sest. | 901 tok/s | chat, coding |
| Qwen 3.5 27B Alibaba Qwen | Q4_K_M | offload | 19.0 GB | – | 6tok/sest. | 137 tok/s | – |
| Gemma 4 26B-A4B | MXFP4 | offload | 17.5 GB | – | 22tok/sest. | 915 tok/s | chat, coding |
| Qwen 3.5 35B-A3B Alibaba Qwen | Q4_K_M | offload | 22.5 GB | – | 23tok/sest. | 895 tok/s | chat, coding |
| GLM 4.7 Flash Z.ai | Q4_K_M | offload | 20.0 GB | – | 14tok/sest. | 498 tok/s | chat |
| Devstral Small 2 24B Mistral AI | Q4_K_M | offload | 19.6 GB | 2k3k with q8 cache | 6tok/sest. | 142 tok/s | – |
| Nemotron 3.5 Lightning 30B-A3B NVIDIA | MXFP4 | offload | 23.1 GB | – | 23tok/sest. | 1006 tok/s | chat, coding, long documents |
| Kimi Linear 48B-A3B Moonshot AI | Q4_K_M | offload | 29.6 GB | – | 23tok/sest. | 677 tok/s | chat, coding |
| Qwen3 Coder 30B-A3B Alibaba Qwen | Q4_K_M | offload | 21.6 GB | – | 12tok/sest. | 421 tok/s | chat |
| Qwen3 32B Alibaba Qwen | Q4_K_M | offload | 27.7 GB | – | 3tok/sest. | 70 tok/s | – |
| Gemma 3 27B | Q4_K_M | offload | 19.6 GB | – | 6tok/sest. | 143 tok/s | – |
| Mistral Small 3.2 24B Mistral AI | Q4_K_M | offload | 19.6 GB | 2k3k with q8 cache | 6tok/sest. | 142 tok/s | – |
| DeepSeek R1 Distill Qwen 32B DeepSeek | Q4_K_M | offload | 27.8 GB | – | 3tok/sest. | 81 tok/s | – |
| Phi-4 14B Microsoft | Q4_K_M | offload | 15.8 GB | 16k | 10tok/sest. | 223 tok/s | chat |
| LFM2.5 24B A2B Liquid AI | Q4_K_M | offload | 18.1 GB | – | 19tok/sest. | 936 tok/s | chat |
| DeepSeek R1 Distill Qwen 14B DeepSeek | Q4_K_M | offload | 15.7 GB | 27k50k with q8 cache | 10tok/sest. | 210 tok/s | chat |
| Muse Glimmer 30B meta-models | Q4_K_M | offload | 17.9 GB | – | 7tok/sest. | 137 tok/s | – |
| Devstral Small 2507 Mistral AI | Q4_K_M | offload | 19.6 GB | 2k3k with q8 cache | 6tok/sest. | 144 tok/s | – |
| Magistral Small 2506 Mistral AI | Q4_K_M | offload | 19.6 GB | 2k3k with q8 cache | 6tok/sest. | 144 tok/s | – |
| Magistral Small 2507 Mistral AI | Q4_K_M | offload | 19.6 GB | 2k3k with q8 cache | 6tok/sest. | 144 tok/s | – |
| Magistral Small 2509 Mistral AI | Q4_K_M | offload | 19.6 GB | 2k3k with q8 cache | 6tok/sest. | 142 tok/s | – |
| Qwen3.8 27B OBLITERATED OBLITERATUS | mlx-4bit | offload | 22.7 GB | – | 4tok/sest. | 125 tok/s | – |
| granite 4.2 30b IBM | Q4_K_M | offload | 26.1 GB | – | 3tok/sest. | 95 tok/s | – |
| DeepSeek V4 Flash DeepSeek | UD-Q2_K_XL | won't run | 94.2 GB | – | – | – | – |
| GLM 5.3 Flash Z.ai | UD-Q2_K_XL | won't run | 103.0 GB | – | – | – | – |
| Qwen 3.8 Flash Next Alibaba Qwen | UD-Q2_K_XL | won't run | 75.6 GB | – | – | – | – |
| MiniMax M2.7 MiniMax | UD-Q2_K_XL | won't run | 79.2 GB | – | – | – | – |
| Qwen 3.5 122B-A10B Alibaba Qwen | UD-Q2_K_XL | won't run | 41.1 GB | – | – | – | – |
| Nemotron 3 Super 120B-A12B NVIDIA | UD-Q2_K_XL | won't run | 52.6 GB | – | – | – | – |
| Qwen3 Coder Next Alibaba Qwen | Q4_K_M | won't run | 47.3 GB | – | – | – | – |
| gpt-oss 120B OpenAI | Q4_K_M | won't run | 60.9 GB | – | – | – | – |
| GLM 4.5 Air Z.ai | Q2_K | won't run | 49.2 GB | – | – | – | – |
| Qwen3 Next 80B-A3B Alibaba Qwen | Q4_K_M | won't run | 47.3 GB | – | – | – | – |
| Llama 3.3 70B Meta | Q4_K_M | won't run | 50.9 GB | – | – | – | – |
| DeepSeek R1 DeepSeek | Q4_K_M | won't run | 380.1 GB | – | – | – | – |
| DeepSeek R1 0528 DeepSeek | Q4_K_M | won't run | 380.6 GB | – | – | – | – |
| DeepSeek R1 Distill Llama 70B DeepSeek | Q4_K_M | won't run | 50.9 GB | – | – | – | – |
| DeepSeek R1 Zero DeepSeek | Q4_K_M | won't run | 380.1 GB | – | – | – | – |
| DeepSeek V3 DeepSeek | Q4_K_M | won't run | 380.1 GB | – | – | – | – |
| DeepSeek V3 0324 DeepSeek | Q4_K_M | won't run | 380.1 GB | – | – | – | – |
| DeepSeek V3.1 DeepSeek | Q4_K_M | won't run | 381.0 GB | – | – | – | – |
| DeepSeek V3.1 Terminus DeepSeek | Q4_K_M | won't run | 381.0 GB | – | – | – | – |
| DeepSeek V3.2 DeepSeek | Q4_K_M | won't run | 381.0 GB | – | – | – | – |
| DeepSeek V4 Flash 0731 DeepSeek | MXFP4 | won't run | 149.6 GB | – | – | – | – |
| DeepSeek V4 Flash Vision Exp DeepSeek | MXFP4 | won't run | 158.4 GB | – | – | – | – |
| Devstral 2 123B Instruct 2512 Mistral AI | Q4_K_M | won't run | 82.0 GB | – | – | – | – |
| GLM 4.5 Z.ai | Q4_K_M | won't run | 214.4 GB | – | – | – | – |
| GLM 4.5V Z.ai | Q4_K_M | won't run | 66.3 GB | – | – | – | – |
| GLM 4.6 Z.ai | Q4_K_M | won't run | 213.6 GB | – | – | – | – |
| GLM 4.6V Z.ai | Q4_K_M | won't run | 72.6 GB | – | – | – | – |
| GLM 4.7 Z.ai | Q4_K_M | won't run | 214.4 GB | – | – | – | – |
| GLM 5 Z.ai | MXFP4 | won't run | 386.4 GB | – | – | – | – |
| GLM 5.1 Z.ai | MXFP4 | won't run | 424.0 GB | – | – | – | – |
| GLM 5.2 Z.ai | UD-Q4_K_M | won't run | 437.9 GB | – | – | – | – |
| GLM 5.3 Z.ai | Q8_0 | won't run | 750.4 GB | – | – | – | – |
| Kimi Dev 72B Moonshot AI | Q6_K | won't run | 71.2 GB | – | – | – | – |
| Kimi K2 Moonshot AI | Q4_K_M | won't run | 581.6 GB | – | – | – | – |
| Kimi K2 Instruct 0905 Moonshot AI | Q4_K_M | won't run | 582.0 GB | – | – | – | – |
| Kimi K2 Thinking Moonshot AI | Q4_K_M | won't run | 582.0 GB | – | – | – | – |
| Kimi K2.5 Moonshot AI | Q4_K_M | won't run | 582.0 GB | – | – | – | – |
| MiniMax M2 MiniMax | Q4_K_M | won't run | 137.9 GB | – | – | – | – |
| MiniMax M2.1 MiniMax | Q4_K_M | won't run | 137.9 GB | – | – | – | – |
| MiniMax M2.5 MiniMax | MXFP4 | won't run | 124.3 GB | – | – | – | – |
| MiniMax M3 MiniMax | MXFP4 | won't run | 244.7 GB | – | – | – | – |
| Mistral Medium 3.5 128B Mistral AI | Q4_K_M | won't run | 82.0 GB | – | – | – | – |
Specifications
From the manufacturer's published specifications; sources are linked where we have them.
- GPU
- GeForce RTX 4060 Ti 8GB
- Memory
- 8 GB GDDR6
- Memory bus
- 128-bit
- Memory bandwidth
- 288 GB/s
- FP16 compute
- 22.065 TFLOPS
- Software
- CUDA
- Board power
- 160 W
- PCIe generation
- Gen 4
- PCIe lanes
- x8
Compare with
Questions
- How much memory does the NVIDIA GeForce RTX 4060 Ti 8GB have for AI models?
- The NVIDIA GeForce RTX 4060 Ti 8GB has 8 GB of GDDR6 memory with 288 GB/s of bandwidth, which sets how fast it generates text.
- What is the best model the NVIDIA GeForce RTX 4060 Ti 8GB can run locally?
- gpt-oss 20B (MXFP4) fits fully in its memory at 32k context and generates at about 33 tok/s, an estimate from its memory bandwidth. 36 of the 129 models we track fit at that context.
- Can the NVIDIA GeForce RTX 4060 Ti 8GB run DeepSeek V4 Flash?
- No: even at UD-Q2_K_XL, DeepSeek V4 Flash needs about 94 GB at 32k context, more than the NVIDIA GeForce RTX 4060 Ti 8GB and system RAM can hold.
- How much does the NVIDIA GeForce RTX 4060 Ti 8GB cost in the US?
- The best new price for the NVIDIA GeForce RTX 4060 Ti 8GB in the US is $440 at eBay; used, it starts at $430 (asking price).
Speeds on this page are for 32k context. See the best hardware for local AI at every budget.