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 8k context it runs 26 of the 129 open models we track fully in memory; the most capable is Qwen 3.5 9B at Q5_K_M, at about 40 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 | 5 h ago | Buy |
| eBayASUS DUAL GeForce RTX 5060 8G GDDR7 OC Edition Graphics Card | used, asking price | $450 | In stock | 5 h ago | Buy |
| eBayNEW MSI GeForce RTX 5060 Inspire 2X OC Graphics Card 8GB GDDR7 NVIDIA Blackwell | New | $475 | In stock | 5 h ago | Buy |
| eBayUsed GeForce RTX 5060 on eBay | used, asking price | $480 | In stock | 5 h ago | Buy |
| eBayGIGABYTE GeForce RTX 5060 WINDFORCE MAX OC V2 8G PCI Express 5.0 ATX Graphic Car | New | $482 | In stock | 5 h ago | Buy |
| eBayGIGABYTE GeForce RTX 5060 GAMING OC V2 8G Graphics Card | New | $482 | In stock | 5 h ago | Buy |
| eBayASUS Dual GeForce RTX 5060 OC 8GB GDDR7 PCIe 5.0 DLSS4 HDMI DP Graphics Card NEW | New | $489 | In stock | 5 h ago | Buy |
| eBayGIGABYTE Eagle GeForce RTX 5060 8GB GDDR7 PCI Express 5.0 Graphics Card GV-N5060 | New | $489 | In stock | 5 h ago | Buy |
| eBayGIGABYTE Eagle GeForce RTX 5060 8GB GDDR7 PCI Express 5.0 ATX Graphics Card GV-N | New | $492 | In stock | 5 h 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.
Not enough price history yet.
What it runs
Every model in the catalogue, with the best quant for this hardware. Estimates, not measurements.
| Model | Quant | Fits | Memory | Max context | Decode @8k | Prefill | Good for |
|---|---|---|---|---|---|---|---|
| Qwen 3.5 9B Alibaba Qwen | Q5_K_M | fits | 7.2 GB | 14k23k with q8 cache | 40tok/sest. | 435 tok/s | chat, coding |
| DeepSeek R1 0528 Qwen3 8B DeepSeek | Q5_K_M | fits | 7.3 GB | 8k15k with q8 cache | 39tok/sest. | 459 tok/s | chat |
| Qwen3 8B Alibaba Qwen | Q5_K_M | fits | 7.3 GB | 8k15k with q8 cache | 39tok/sest. | 459 tok/s | chat |
| Qwen 3.5 4B Alibaba Qwen | Q8_0 | fits | 5.2 GB | 66k110k with q8 cache | 58tok/sest. | 876 tok/s | chat, coding |
| Ministral 3 8B Mistral AI | Q4_K_M | fits | 6.7 GB | 13k24k with q8 cache | 44tok/sest. | 429 tok/s | chat |
| Granite 4.2 8B IBM | Q4_K_M | fits | 7.2 GB | 9k17k with q8 cache | 40tok/sest. | 426 tok/s | chat |
| Gemma 4 E4B | Q5_K_M | fits | 6.1 GB | 46k77k with q8 cache | 48tok/sest. | 527 tok/s | chat, coding |
| LFM2.5 8B-A1B Liquid AI | UD-Q5_K_M | fits | 7.0 GB | 14k25k with q8 cache | 144tok/sest. | 3076 tok/s | chat, coding |
| Llama 3.1 8B Meta | Q5_K_M | fits | 7.1 GB | 10k18k with q8 cache | 41tok/sest. | 474 tok/s | chat |
| Qwen2.5 0.5B Alibaba Qwen | Q8_0 | fits | 1.3 GB | 32k | 440tok/sest. | 6438 tok/s | chat, coding, long documents |
| Qwen3.5 0.8B Alibaba Qwen | Q8_0 | fits | 1.6 GB | 256k | 298tok/sest. | 4430 tok/s | chat, coding, agents, long documents |
| Qwen3.5 2B Alibaba Qwen | Q5_K_M | fits | 2.2 GB | 256k | 179tok/sest. | 1817 tok/s | chat, coding, agents, long documents |
| LFM3 1.7B Liquid AI | Q8_0 | fits | 2.6 GB | 256k | 142tok/sest. | 2334 tok/s | chat, coding, agents, long documents |
| DeepSeek R1 Distill Qwen 1.5B DeepSeek | Q8_0 | fits | 2.7 GB | 128k | 130tok/sest. | 2026 tok/s | chat, coding, long documents |
| MiniCPM5 2B openbmb | Q8_0 | fits | 3.6 GB | 89k128k with q8 cache | 92tok/sest. | 1339 tok/s | chat, coding, agents |
| LFM2.5 2.6B Liquid AI | Q8_0 | fits | 3.9 GB | 62k108k with q8 cache | 82tok/sest. | 1348 tok/s | chat, coding, agents |
| LFM2.5 VL 3B Liquid AI | Q8_0 | fits | 3.9 GB | 32k | 82tok/sest. | 1190 tok/s | chat, coding, agents |
| Spark X2.5 4B XHToken | mlx-4bit | fits | 3.9 GB | 93k156k with q8 cache | 82tok/sest. | 953 tok/s | chat, coding, agents, long documents |
| granite vision 4.1 4b IBM | Q8_0 | fits | 4.7 GB | 39k70k with q8 cache | 65tok/sest. | 892 tok/s | chat, coding |
| Ministral 3 3B Instruct 2512 Mistral AI | Q8_0 | fits | 5.0 GB | 30k55k with q8 cache | 61tok/sest. | 914 tok/s | chat, coding, agents |
| granite 4.2 3b IBM | Q8_0 | fits | 5.0 GB | 36k64k with q8 cache | 61tok/sest. | 959 tok/s | chat, coding, agents |
| FrogNano 4B 2609 Microsoft | Q8_0 | fits | 5.3 GB | 63k104k with q8 cache | 56tok/sest. | 876 tok/s | chat, coding, agents |
| Ministral 3 8B Reasoning 2512 Mistral AI | Q4_K_M | fits | 6.7 GB | 13k24k with q8 cache | 44tok/sest. | 429 tok/s | chat |
| GLM 4.6V Flash Z.ai | Q4_K_M | fits | 6.8 GB | 21k35k with q8 cache | 43tok/sest. | 371 tok/s | chat, coding |
| DeepSeek R1 Distill Qwen 7B DeepSeek | Q6_K | fits | 7.0 GB | 14k25k with q8 cache | 41tok/sest. | 511 tok/s | chat, coding |
| DeepSeek R1 Distill Llama 8B DeepSeek | Q5_K_M | fits | 7.1 GB | 10k18k with q8 cache | 41tok/sest. | 474 tok/s | chat |
| Qwen 3.8 27B Alibaba Qwen | UD-Q4_K_M | offload | 16.7 GB | – | 5tok/sest. | 98 tok/s | – |
| Qwen 3.6 27B Alibaba Qwen | Q4_K_M | offload | 17.0 GB | – | 5tok/sest. | 97 tok/s | – |
| Gemma 4 31B | Q4_K_M | offload | 19.2 GB | – | 4tok/sest. | 81 tok/s | – |
| Qwen 3.6 35B-A3B Alibaba Qwen | MXFP4 | offload | 21.2 GB | – | 21tok/sest. | 770 tok/s | chat |
| Qwen 3.5 27B Alibaba Qwen | Q4_K_M | offload | 17.0 GB | – | 5tok/sest. | 98 tok/s | – |
| Gemma 4 26B-A4B | MXFP4 | offload | 16.5 GB | – | 17tok/sest. | 644 tok/s | chat |
| Qwen 3.5 35B-A3B Alibaba Qwen | Q4_K_M | offload | 21.5 GB | – | 21tok/sest. | 767 tok/s | chat |
| GLM 4.7 Flash Z.ai | Q4_K_M | offload | 18.2 GB | – | 13tok/sest. | 455 tok/s | – |
| Devstral Small 2 24B Mistral AI | Q4_K_M | offload | 15.3 GB | – | 5tok/sest. | 115 tok/s | – |
| Nemotron 3.5 Lightning 30B-A3B NVIDIA | MXFP4 | offload | 22.5 GB | – | 19tok/sest. | 787 tok/s | chat |
| gpt-oss 20B OpenAI | Q4_K_M | offload | 11.8 GB | – | 25tok/sest. | 714 tok/s | chat |
| Kimi Linear 48B-A3B Moonshot AI | Q4_K_M | offload | 28.8 GB | – | 21tok/sest. | 666 tok/s | chat |
| Qwen3 Coder 30B-A3B Alibaba Qwen | Q4_K_M | offload | 18.8 GB | – | 18tok/sest. | 582 tok/s | chat |
| Qwen3 32B Alibaba Qwen | Q4_K_M | offload | 21.2 GB | – | 3tok/sest. | 69 tok/s | – |
| Gemma 4 12B | Q4_K_M | offload | 7.8 GB | – | 29tok/sest. | 331 tok/s | chat, coding |
| Mistral Small 3.2 24B Mistral AI | Q4_K_M | offload | 15.3 GB | – | 5tok/sest. | 115 tok/s | – |
| Gemma 3 27B | Q4_K_M | offload | 17.2 GB | – | 5tok/sest. | 98 tok/s | – |
| DeepSeek R1 Distill Qwen 32B DeepSeek | Q4_K_M | offload | 21.2 GB | – | 3tok/sest. | 72 tok/s | – |
| Ministral 3 14B Mistral AI | Q4_K_M | offload | 9.7 GB | – | 14tok/sest. | 239 tok/s | – |
| Qwen3 14B Alibaba Qwen | Q4_K_M | offload | 10.4 GB | – | 12tok/sest. | 214 tok/s | – |
| Phi-4 14B Microsoft | Q4_K_M | offload | 10.6 GB | – | 11tok/sest. | 213 tok/s | – |
| Mellum2.1 12B A2.5B Thinking JetBrains | Q4_K_M | offload | 9.1 GB | – | 46tok/sest. | 1300 tok/s | chat, coding, agents, long documents |
| GLM 4 9B 0414 Z.ai | mlx-4bit | offload | 7.6 GB | 2k4k with q8 cache | 33tok/sest. | 393 tok/s | chat |
| Kimi VL A3B Thinking 2506 Moonshot AI | Q4_K_M | offload | 10.8 GB | – | 32tok/sest. | 996 tok/s | chat, coding |
| LFM2.5 24B A2B Liquid AI | Q4_K_M | offload | 15.7 GB | – | 26tok/sest. | 1078 tok/s | chat |
| Kimi VL A3B Moonshot AI | mlx-5bit | offload | 12.0 GB | – | 26tok/sest. | 885 tok/s | chat |
| Ministral 3 14B Reasoning 2512 Mistral AI | Q4_K_M | offload | 9.7 GB | – | 14tok/sest. | 239 tok/s | – |
| DeepSeek R1 Distill Qwen 14B DeepSeek | Q4_K_M | offload | 10.6 GB | – | 11tok/sest. | 207 tok/s | – |
| GLM 4.1V 9B Thinking Z.ai | mlx-8bit | offload | 12.0 GB | – | 9tok/sest. | 280 tok/s | – |
| Devstral Small 2507 Mistral AI | Q4_K_M | offload | 15.3 GB | – | 5tok/sest. | 117 tok/s | – |
| Magistral Small 2506 Mistral AI | Q4_K_M | offload | 15.3 GB | – | 5tok/sest. | 117 tok/s | – |
| Magistral Small 2507 Mistral AI | Q4_K_M | offload | 15.3 GB | – | 5tok/sest. | 117 tok/s | – |
| Magistral Small 2509 Mistral AI | Q4_K_M | offload | 15.3 GB | – | 5tok/sest. | 115 tok/s | – |
| Muse Glimmer 30B meta-models | Q4_K_M | offload | 17.0 GB | – | 5tok/sest. | 90 tok/s | – |
| granite 4.2 30b IBM | Q4_K_M | offload | 19.5 GB | – | 4tok/sest. | 84 tok/s | – |
| Qwen3.8 27B OBLITERATED OBLITERATUS | mlx-4bit | offload | 20.6 GB | – | 3tok/sest. | 91 tok/s | – |
| DeepSeek V4 Flash DeepSeek | UD-Q2_K_XL | won't run | 91.6 GB | – | – | – | – |
| GLM 5.3 Flash Z.ai | UD-Q2_K_XL | won't run | 102.2 GB | – | – | – | – |
| Qwen 3.8 Flash Next Alibaba Qwen | UD-Q2_K_XL | won't run | 74.5 GB | – | – | – | – |
| MiniMax M2.7 MiniMax | UD-Q2_K_XL | won't run | 72.8 GB | – | – | – | – |
| Qwen 3.5 122B-A10B Alibaba Qwen | UD-Q2_K_XL | won't run | 40.0 GB | – | – | – | – |
| Nemotron 3 Super 120B-A12B NVIDIA | UD-Q2_K_XL | won't run | 51.9 GB | – | – | – | – |
| Qwen3 Coder Next Alibaba Qwen | Q4_K_M | won't run | 46.2 GB | – | – | – | – |
| gpt-oss 120B OpenAI | Q4_K_M | won't run | 59.5 GB | – | – | – | – |
| Mistral Small 4 119B Mistral AI | UD-Q2_K_XL | won't run | 38.4 GB | – | – | – | – |
| GLM 4.5 Air Z.ai | Q2_K | won't run | 44.4 GB | – | – | – | – |
| Qwen3 Next 80B-A3B Alibaba Qwen | Q4_K_M | won't run | 46.2 GB | – | – | – | – |
| Llama 3.3 70B Meta | Q4_K_M | won't run | 42.8 GB | – | – | – | – |
| DeepSeek R1 DeepSeek | Q4_K_M | won't run | 377.9 GB | – | – | – | – |
| DeepSeek R1 0528 DeepSeek | Q4_K_M | won't run | 378.4 GB | – | – | – | – |
| DeepSeek R1 Distill Llama 70B DeepSeek | Q4_K_M | won't run | 42.8 GB | – | – | – | – |
| DeepSeek R1 Zero DeepSeek | Q4_K_M | won't run | 377.9 GB | – | – | – | – |
| DeepSeek V3 DeepSeek | Q4_K_M | won't run | 377.9 GB | – | – | – | – |
| DeepSeek V3 0324 DeepSeek | Q4_K_M | won't run | 377.9 GB | – | – | – | – |
| DeepSeek V3.1 DeepSeek | Q4_K_M | won't run | 378.8 GB | – | – | – | – |
| DeepSeek V3.1 Terminus DeepSeek | Q4_K_M | won't run | 378.8 GB | – | – | – | – |
| DeepSeek V3.2 DeepSeek | Q4_K_M | won't run | 378.8 GB | – | – | – | – |
| DeepSeek V4 Flash 0731 DeepSeek | MXFP4 | won't run | 147.1 GB | – | – | – | – |
| DeepSeek V4 Flash Vision Exp DeepSeek | MXFP4 | won't run | 155.8 GB | – | – | – | – |
| Devstral 2 123B Instruct 2512 Mistral AI | Q4_K_M | won't run | 73.3 GB | – | – | – | – |
| GLM 4.5 Z.ai | Q4_K_M | won't run | 205.2 GB | – | – | – | – |
| GLM 4.5V Z.ai | Q4_K_M | won't run | 61.4 GB | – | – | – | – |
| GLM 4.6 Z.ai | Q4_K_M | won't run | 204.4 GB | – | – | – | – |
| GLM 4.6V Z.ai | Q4_K_M | won't run | 67.8 GB | – | – | – | – |
| GLM 4.7 Z.ai | Q4_K_M | won't run | 205.2 GB | – | – | – | – |
| GLM 5 Z.ai | MXFP4 | won't run | 383.8 GB | – | – | – | – |
| GLM 5.1 Z.ai | MXFP4 | won't run | 421.4 GB | – | – | – | – |
| GLM 5.2 Z.ai | UD-Q4_K_M | won't run | 435.3 GB | – | – | – | – |
| GLM 5.3 Z.ai | Q8_0 | won't run | 747.8 GB | – | – | – | – |
| Kimi Dev 72B Moonshot AI | Q6_K | won't run | 63.2 GB | – | – | – | – |
| Kimi K2 Moonshot AI | Q4_K_M | won't run | 579.4 GB | – | – | – | – |
| Kimi K2 Instruct 0905 Moonshot AI | Q4_K_M | won't run | 579.9 GB | – | – | – | – |
| Kimi K2 Thinking Moonshot AI | Q4_K_M | won't run | 579.9 GB | – | – | – | – |
| Kimi K2.5 Moonshot AI | Q4_K_M | won't run | 579.9 GB | – | – | – | – |
| MiniMax M2 MiniMax | Q4_K_M | won't run | 131.5 GB | – | – | – | – |
| MiniMax M2.1 MiniMax | Q4_K_M | won't run | 131.5 GB | – | – | – | – |
| MiniMax M2.5 MiniMax | MXFP4 | won't run | 118.0 GB | – | – | – | – |
| MiniMax M3 MiniMax | MXFP4 | won't run | 241.4 GB | – | – | – | – |
| Mistral Medium 3.5 128B Mistral AI | Q4_K_M | won't run | 73.3 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?
- Qwen 3.5 9B (Q5_K_M) fits fully in its memory at 8k context and generates at about 40 tok/s, an estimate from its memory bandwidth. 26 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 92 GB at 8k 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 8k context. See the best hardware for local AI at every budget.