NVIDIA GeForce RTX 5060
8 GB · 448 GB/s · 145 W
In short
The NVIDIA GeForce RTX 5060 has 8 GB of GDDR7 memory with 448 GB/s of bandwidth, which sets how fast it generates text. At 128k context it runs 21 of the 129 open models we track fully in memory; the most capable is Gemma 4 12B at Q6_K, at about 23 tok/s (estimated). The best new price for the NVIDIA GeForce RTX 5060 in the US is $475 at eBay; used, it starts at $448 (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 |
|---|---|---|---|---|---|
| eBayGIGABYTE GeForce RTX 5060 WINDFORCE OC 8G 8GB GDDR7 Graphics Card GPU Dual Fan | used, asking price | $448 | In stock | 7 min ago | Buy |
| eBayASUS DUAL GeForce RTX 5060 8G GDDR7 OC Edition Graphics Card | used, asking price | $450 | In stock | 7 min ago | Buy |
| eBayNEW MSI GeForce RTX 5060 Inspire 2X OC Graphics Card 8GB GDDR7 NVIDIA Blackwell | New | $475 | In stock | 7 min ago | Buy |
| eBayUsed GeForce RTX 5060 on eBay | used, asking price | $480 | In stock | 6 h ago | Buy |
| eBayGIGABYTE GeForce RTX 5060 WINDFORCE MAX OC V2 8G PCI Express 5.0 ATX Graphic Car | New | $482 | In stock | 7 min ago | Buy |
| eBayGIGABYTE GeForce RTX 5060 GAMING OC V2 8G Graphics Card | New | $482 | In stock | 7 min ago | Buy |
| eBayASUS Dual GeForce RTX 5060 OC 8GB GDDR7 PCIe 5.0 DLSS4 HDMI DP Graphics Card NEW | New | $489 | In stock | 7 min ago | Buy |
| eBayGIGABYTE Eagle GeForce RTX 5060 8GB GDDR7 PCI Express 5.0 Graphics Card GV-N5060 | New | $489 | In stock | 7 min ago | Buy |
| eBayGIGABYTE Eagle GeForce RTX 5060 8GB GDDR7 PCI Express 5.0 ATX Graphics Card GV-N | New | $492 | In stock | 7 min ago | Buy |
Used on eBay: median asking price $480, most between $450 and $500 (21 listings, 2026-11-03). Used price history
Price history
Lowest price shown each day over the last 90 days.
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 @128k | Prefill | Good for |
|---|---|---|---|---|---|---|---|
| Gemma 4 12B | Q6_K | fits | 13.3 GB | 198k256k with q8 cache | 23tok/sest. | 260 tok/s | chat, coding |
| Qwen 3.5 9B Alibaba Qwen | Q8_0 | fits | 14.8 GB | 128k212k with q8 cache | 20tok/sest. | 309 tok/s | chat, coding, long documents |
| Qwen 3.5 4B Alibaba Qwen | Q8_0 | fits | 10.1 GB | 255k256k with q8 cache | 32tok/sest. | 482 tok/s | chat, coding, long documents |
| Gemma 4 E4B | Q8_0 | fits | 13.0 GB | 128k | 23tok/sest. | 434 tok/s | chat, coding, long documents |
| LFM2.5 8B-A1B Liquid AI | UD-Q6_K | fits | 14.5 GB | 125k | 23tok/sest. | 580 tok/s | chat, coding, long documents |
| Qwen2.5 0.5B Alibaba Qwen | Q8_0 | fits | 3.8 GB | 32k | 130tok/sest. | 1303 tok/s | chat, coding, long documents |
| Qwen3.5 0.8B Alibaba Qwen | Q8_0 | fits | 4.1 GB | 256k | 114tok/sest. | 1737 tok/s | chat, coding, agents, long documents |
| Qwen3.5 2B Alibaba Qwen | Q5_K_M | fits | 4.7 GB | 256k | 91tok/sest. | 1111 tok/s | chat, coding, agents, long documents |
| LFM3 1.7B Liquid AI | Q8_0 | fits | 5.1 GB | 256k | 80tok/sest. | 1285 tok/s | chat, coding, agents, long documents |
| DeepSeek R1 Distill Qwen 1.5B DeepSeek | Q8_0 | fits | 7.1 GB | 128k | 49tok/sest. | 582 tok/s | chat, coding, long documents |
| Mellum2.1 12B A2.5B Thinking JetBrains | Q6_K | fits | 14.3 GB | 128k | 39tok/sest. | 671 tok/s | chat, coding, agents, long documents |
| Spark X2.5 4B XHToken | mlx-4bit | fits | 9.2 GB | 263k441k with q8 cache | 35tok/sest. | 476 tok/s | chat, coding, agents, long documents |
| MiniCPM5 2B openbmb | Q8_0 | fits | 9.6 GB | 128k | 33tok/sest. | 313 tok/s | chat, coding, agents, long documents |
| FrogNano 4B 2609 Microsoft | Q8_0 | fits | 10.2 GB | 251k256k with q8 cache | 31tok/sest. | 482 tok/s | chat, coding, agents, long documents |
| LFM2.5 2.6B Liquid AI | Q8_0 | fits | 12.0 GB | 128k | 25tok/sest. | 401 tok/s | chat, coding, agents, long documents |
| LFM2.5 VL 3B Liquid AI | Q8_0 | fits | 12.0 GB | 32k | 25tok/sest. | 386 tok/s | chat, coding, agents, long documents |
| GLM 4.6V Flash Z.ai | Q4_K_M | fits | 12.6 GB | 128k | 24tok/sest. | 136 tok/s | chat, coding |
| granite vision 4.1 4b IBM | Q5_K_M | fits | 14.1 GB | 128k | 21tok/sest. | 248 tok/s | chat, coding |
| GLM 4 9B 0414 Z.ai | mlx-5bit | fits | 14.5 GB | 32k | 20tok/sest. | 140 tok/s | chat, coding |
| DeepSeek R1 Distill Qwen 7B DeepSeek | Q6_K | fits | 14.7 GB | 128k | 20tok/sest. | 208 tok/s | chat, coding |
| granite 4.2 3b IBM | Q6_K | fits | 14.8 GB | 128k | 20tok/sest. | 253 tok/s | chat, coding, agents |
| Qwen 3.8 27B Alibaba Qwen | UD-Q4_K_M | offload | 25.3 GB | – | 4tok/sest. | 78 tok/s | – |
| Qwen 3.6 27B Alibaba Qwen | Q4_K_M | offload | 25.7 GB | – | 4tok/sest. | 77 tok/s | – |
| Gemma 4 31B | Q4_K_M | offload | 29.7 GB | – | 3tok/sest. | 68 tok/s | – |
| Qwen 3.6 35B-A3B Alibaba Qwen | MXFP4 | offload | 24.6 GB | – | 13tok/sest. | 382 tok/s | chat, coding |
| Qwen 3.5 27B Alibaba Qwen | Q4_K_M | offload | 25.6 GB | – | 4tok/sest. | 77 tok/s | – |
| Gemma 4 26B-A4B | MXFP4 | offload | 20.0 GB | – | 14tok/sest. | 519 tok/s | chat, coding |
| Qwen 3.5 35B-A3B Alibaba Qwen | Q4_K_M | offload | 24.9 GB | – | 13tok/sest. | 380 tok/s | chat, coding |
| GLM 4.7 Flash Z.ai | Q4_K_M | offload | 25.5 GB | – | 6tok/sest. | 177 tok/s | chat |
| Devstral Small 2 24B Mistral AI | Q4_K_M | offload | 35.2 GB | 2k3k with q8 cache | 2tok/sest. | 60 tok/s | – |
| Nemotron 3.5 Lightning 30B-A3B NVIDIA | MXFP4 | offload | 24.3 GB | – | 20tok/sest. | 518 tok/s | chat, coding, long documents |
| gpt-oss 20B OpenAI | Q4_K_M | offload | 15.7 GB | 97k128k with q8 cache | 24tok/sest. | 390 tok/s | chat, coding, agents, long documents |
| Kimi Linear 48B-A3B Moonshot AI | Q4_K_M | offload | 30.9 GB | – | 17tok/sest. | 258 tok/s | chat, coding |
| Qwen3 Coder 30B-A3B Alibaba Qwen | Q4_K_M | offload | 31.1 GB | – | 3tok/sest. | 100 tok/s | chat |
| Gemma 3 27B | Q4_K_M | offload | 27.7 GB | – | 3tok/sest. | 91 tok/s | – |
| Mistral Small 3.2 24B Mistral AI | Q4_K_M | offload | 35.2 GB | 2k3k with q8 cache | 2tok/sest. | 60 tok/s | – |
| Ministral 3 14B Mistral AI | Q4_K_M | offload | 29.5 GB | 37k67k with q8 cache | 3tok/sest. | 83 tok/s | chat, coding |
| Qwen3 14B Alibaba Qwen | Q4_K_M | offload | 30.2 GB | 32k40k with q8 cache | 3tok/sest. | 70 tok/s | chat |
| DeepSeek R1 0528 Qwen3 8B DeepSeek | Q4_K_M | offload | 24.5 GB | 61k111k with q8 cache | 4tok/sest. | 116 tok/s | chat, coding |
| Qwen3 8B Alibaba Qwen | Q4_K_M | offload | 24.5 GB | 40k | 4tok/sest. | 116 tok/s | chat, coding |
| Phi-4 14B Microsoft | Q4_K_M | offload | 35.1 GB | 16k | 2tok/sest. | 66 tok/s | chat |
| Ministral 3 8B Mistral AI | Q4_K_M | offload | 23.7 GB | 63k115k with q8 cache | 4tok/sest. | 120 tok/s | chat, coding |
| Granite 4.2 8B IBM | Q4_K_M | offload | 27.0 GB | 52k96k with q8 cache | 3tok/sest. | 101 tok/s | chat, coding |
| Llama 3.1 8B Meta | Q4_K_M | offload | 22.4 GB | 69k126k with q8 cache | 5tok/sest. | 133 tok/s | chat, coding |
| Kimi VL A3B Thinking 2506 Moonshot AI | Q4_K_M | offload | 15.5 GB | 109k128k with q8 cache | 23tok/sest. | 409 tok/s | chat, coding, long documents |
| Kimi VL A3B Moonshot AI | mlx-5bit | offload | 16.7 GB | 75k123k with q8 cache | 18tok/sest. | 288 tok/s | chat, coding, agents |
| Ministral 3 3B Instruct 2512 Mistral AI | Q4_K_M | offload | 16.8 GB | 108k195k with q8 cache | 11tok/sest. | 222 tok/s | chat, coding, agents |
| GLM 4.1V 9B Thinking Z.ai | mlx-8bit | offload | 17.8 GB | 60k64k with q8 cache | 9tok/sest. | 120 tok/s | chat, coding |
| Muse Glimmer 30B meta-models | Q4_K_M | offload | 19.7 GB | – | 7tok/sest. | 96 tok/s | – |
| DeepSeek R1 Distill Llama 8B DeepSeek | Q4_K_M | offload | 22.4 GB | 69k126k with q8 cache | 5tok/sest. | 133 tok/s | chat, coding |
| LFM2.5 24B A2B Liquid AI | Q4_K_M | offload | 26.1 GB | – | 5tok/sest. | 244 tok/s | chat, coding |
| Ministral 3 8B Reasoning 2512 Mistral AI | Q4_K_M | offload | 23.7 GB | 63k115k with q8 cache | 4tok/sest. | 120 tok/s | chat, coding |
| Qwen3.8 27B OBLITERATED OBLITERATUS | mlx-4bit | offload | 29.2 GB | – | 3tok/sest. | 73 tok/s | – |
| Ministral 3 14B Reasoning 2512 Mistral AI | Q4_K_M | offload | 29.5 GB | 37k67k with q8 cache | 3tok/sest. | 83 tok/s | chat, coding |
| DeepSeek R1 Distill Qwen 14B DeepSeek | Q4_K_M | offload | 34.2 GB | 27k50k with q8 cache | 2tok/sest. | 59 tok/s | chat |
| Devstral Small 2507 Mistral AI | Q4_K_M | offload | 35.2 GB | 2k3k with q8 cache | 2tok/sest. | 61 tok/s | – |
| Magistral Small 2506 Mistral AI | Q4_K_M | offload | 35.2 GB | 2k3k with q8 cache | 2tok/sest. | 61 tok/s | – |
| Magistral Small 2507 Mistral AI | Q4_K_M | offload | 35.2 GB | 2k3k with q8 cache | 2tok/sest. | 61 tok/s | – |
| Magistral Small 2509 Mistral AI | Q4_K_M | offload | 35.2 GB | 2k3k with q8 cache | 2tok/sest. | 60 tok/s | – |
| DeepSeek V4 Flash DeepSeek | UD-Q2_K_XL | won't run | 102.8 GB | – | – | – | – |
| GLM 5.3 Flash Z.ai | UD-Q2_K_XL | won't run | 104.6 GB | – | – | – | – |
| Qwen 3.8 Flash Next Alibaba Qwen | UD-Q2_K_XL | won't run | 78.4 GB | – | – | – | – |
| MiniMax M2.7 MiniMax | UD-Q2_K_XL | won't run | 103.0 GB | – | – | – | – |
| Qwen 3.5 122B-A10B Alibaba Qwen | UD-Q2_K_XL | won't run | 44.0 GB | – | – | – | – |
| Nemotron 3 Super 120B-A12B NVIDIA | UD-Q2_K_XL | won't run | 53.9 GB | – | – | – | – |
| Qwen3 Coder Next Alibaba Qwen | Q4_K_M | won't run | 50.1 GB | – | – | – | – |
| gpt-oss 120B OpenAI | Q4_K_M | won't run | 64.8 GB | – | – | – | – |
| Mistral Small 4 119B Mistral AI | UD-Q2_K_XL | won't run | 42.1 GB | – | – | – | – |
| GLM 4.5 Air Z.ai | Q2_K | won't run | 67.1 GB | – | – | – | – |
| Qwen3 Next 80B-A3B Alibaba Qwen | Q4_K_M | won't run | 50.1 GB | – | – | – | – |
| Qwen3 32B Alibaba Qwen | Q4_K_M | won't run | 52.3 GB | – | – | – | – |
| DeepSeek R1 Distill Qwen 32B DeepSeek | Q4_K_M | won't run | 52.3 GB | – | – | – | – |
| Llama 3.3 70B Meta | Q4_K_M | won't run | 81.5 GB | – | – | – | – |
| DeepSeek R1 DeepSeek | Q4_K_M | won't run | 387.1 GB | – | – | – | – |
| DeepSeek R1 0528 DeepSeek | Q4_K_M | won't run | 387.6 GB | – | – | – | – |
| DeepSeek R1 Distill Llama 70B DeepSeek | Q4_K_M | won't run | 81.5 GB | – | – | – | – |
| DeepSeek R1 Zero DeepSeek | Q4_K_M | won't run | 387.1 GB | – | – | – | – |
| DeepSeek V3 DeepSeek | Q4_K_M | won't run | 387.1 GB | – | – | – | – |
| DeepSeek V3 0324 DeepSeek | Q4_K_M | won't run | 387.1 GB | – | – | – | – |
| DeepSeek V3.1 DeepSeek | Q4_K_M | won't run | 388.0 GB | – | – | – | – |
| DeepSeek V3.1 Terminus DeepSeek | Q4_K_M | won't run | 388.0 GB | – | – | – | – |
| DeepSeek V3.2 DeepSeek | Q4_K_M | won't run | 388.0 GB | – | – | – | – |
| DeepSeek V4 Flash 0731 DeepSeek | MXFP4 | won't run | 158.2 GB | – | – | – | – |
| DeepSeek V4 Flash Vision Exp DeepSeek | MXFP4 | won't run | 167.0 GB | – | – | – | – |
| Devstral 2 123B Instruct 2512 Mistral AI | Q4_K_M | won't run | 115.6 GB | – | – | – | – |
| GLM 4.5 Z.ai | Q4_K_M | won't run | 249.4 GB | – | – | – | – |
| GLM 4.5V Z.ai | Q4_K_M | won't run | 84.1 GB | – | – | – | – |
| GLM 4.6 Z.ai | Q4_K_M | won't run | 248.6 GB | – | – | – | – |
| GLM 4.6V Z.ai | Q4_K_M | won't run | 90.5 GB | – | – | – | – |
| GLM 4.7 Z.ai | Q4_K_M | won't run | 249.4 GB | – | – | – | – |
| GLM 5 Z.ai | MXFP4 | won't run | 395.2 GB | – | – | – | – |
| GLM 5.1 Z.ai | MXFP4 | won't run | 432.8 GB | – | – | – | – |
| GLM 5.2 Z.ai | UD-Q4_K_M | won't run | 446.7 GB | – | – | – | – |
| GLM 5.3 Z.ai | Q8_0 | won't run | 759.1 GB | – | – | – | – |
| Kimi Dev 72B Moonshot AI | Q6_K | won't run | 101.8 GB | – | – | – | – |
| Kimi K2 Moonshot AI | Q4_K_M | won't run | 588.6 GB | – | – | – | – |
| Kimi K2 Instruct 0905 Moonshot AI | Q4_K_M | won't run | 589.0 GB | – | – | – | – |
| Kimi K2 Thinking Moonshot AI | Q4_K_M | won't run | 589.0 GB | – | – | – | – |
| Kimi K2.5 Moonshot AI | Q4_K_M | won't run | 589.0 GB | – | – | – | – |
| MiniMax M2 MiniMax | Q4_K_M | won't run | 161.7 GB | – | – | – | – |
| MiniMax M2.1 MiniMax | Q4_K_M | won't run | 161.7 GB | – | – | – | – |
| MiniMax M2.5 MiniMax | MXFP4 | won't run | 148.1 GB | – | – | – | – |
| MiniMax M3 MiniMax | MXFP4 | won't run | 256.5 GB | – | – | – | – |
| Mistral Medium 3.5 128B Mistral AI | Q4_K_M | won't run | 115.6 GB | – | – | – | – |
| granite 4.2 30b IBM | Q4_K_M | won't run | 50.6 GB | – | – | – | – |
Specifications
From the manufacturer's published specifications; sources are linked where we have them.
- GPU
- GeForce RTX 5060
- Memory
- 8 GB GDDR7
- Memory bus
- 128-bit
- Memory bandwidth
- 448 GB/s
- FP16 compute
- 19.177 TFLOPS
- Software
- CUDA
- Board power
- 145 W
- PCIe generation
- Gen 5
- PCIe lanes
- x8
Compare with
Questions
- How much memory does the NVIDIA GeForce RTX 5060 have for AI models?
- The NVIDIA GeForce RTX 5060 has 8 GB of GDDR7 memory with 448 GB/s of bandwidth, which sets how fast it generates text.
- What is the best model the NVIDIA GeForce RTX 5060 can run locally?
- Gemma 4 12B (Q6_K) fits fully in its memory at 128k context and generates at about 23 tok/s, an estimate from its memory bandwidth. 21 of the 129 models we track fit at that context.
- Can the NVIDIA GeForce RTX 5060 run DeepSeek V4 Flash?
- No: even at UD-Q2_K_XL, DeepSeek V4 Flash needs about 103 GB at 128k context, more than the NVIDIA GeForce RTX 5060 and system RAM can hold.
- How much does the NVIDIA GeForce RTX 5060 cost in the US?
- The best new price for the NVIDIA GeForce RTX 5060 in the US is $475 at eBay; used, it starts at $448 (asking price).
Speeds on this page are for 128k context. See the best hardware for local AI at every budget.