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 64k context it runs 27 of the 129 open models we track fully in memory; the most capable is gpt-oss 20B at MXFP4, at about 27 tok/s (estimated). The NVIDIA GeForce RTX 4060 Ti 8GB is not on sale new in the US right now; used, it starts at $450 (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 |
|---|---|---|---|---|---|
| eBayUsed GeForce RTX 4060 Ti 8GB on eBay | used, asking price | $450 | In stock | 6 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 @64k | Prefill | Good for |
|---|---|---|---|---|---|---|---|
| gpt-oss 20B OpenAI | MXFP4 | fits | 14.3 GB | 82k128k with q8 cache | 27tok/sest. | 669 tok/s | chat, coding, agents, long documents |
| Gemma 4 12B | Q8_0 | fits | 14.6 GB | 73k122k with q8 cache | 13tok/sest. | 344 tok/s | – |
| Qwen 3.5 9B Alibaba Qwen | Q8_0 | fits | 12.4 GB | 128k212k with q8 cache | 15tok/sest. | 421 tok/s | chat |
| Qwen 3.5 4B Alibaba Qwen | Q8_0 | fits | 7.7 GB | 255k256k with q8 cache | 27tok/sest. | 729 tok/s | chat, coding, long documents |
| Gemma 4 E4B | Q8_0 | fits | 10.9 GB | 128k | 18tok/sest. | 551 tok/s | chat |
| LFM2.5 8B-A1B Liquid AI | Q8_0 | fits | 12.9 GB | 100k125k with q8 cache | 25tok/sest. | 1176 tok/s | chat, coding |
| Llama 3.1 8B Meta | Q4_K_M | fits | 14.1 GB | 69k126k with q8 cache | 13tok/sest. | 299 tok/s | chat |
| Qwen2.5 0.5B Alibaba Qwen | Q8_0 | fits | 2.7 GB | 32k | 134tok/sest. | 2608 tok/s | chat, coding, long documents |
| Qwen3.5 0.8B Alibaba Qwen | Q8_0 | fits | 3.0 GB | 256k | 109tok/sest. | 2957 tok/s | chat, coding, agents, long documents |
| Qwen3.5 2B Alibaba Qwen | Q5_K_M | fits | 3.6 GB | 256k | 79tok/sest. | 1612 tok/s | chat, coding, agents, long documents |
| LFM3 1.7B Liquid AI | Q8_0 | fits | 4.0 GB | 256k | 67tok/sest. | 1945 tok/s | chat, coding, agents, long documents |
| DeepSeek R1 Distill Qwen 1.5B DeepSeek | Q8_0 | fits | 5.0 GB | 128k | 47tok/sest. | 1080 tok/s | chat, coding, long documents |
| MiniCPM5 2B openbmb | Q8_0 | fits | 6.6 GB | 128k | 33tok/sest. | 609 tok/s | chat, coding, agents, long documents |
| Spark X2.5 4B XHToken | mlx-4bit | fits | 6.6 GB | 263k441k with q8 cache | 32tok/sest. | 748 tok/s | chat, coding, agents, long documents |
| Mellum2.1 12B A2.5B Thinking JetBrains | Q8_0 | fits | 14.4 GB | 83k125k with q8 cache | 29tok/sest. | 1090 tok/s | chat, coding, agents, long documents |
| Kimi VL A3B Thinking 2506 Moonshot AI | Q4_K_M | fits | 13.2 GB | 109k128k with q8 cache | 27tok/sest. | 750 tok/s | chat, coding, long documents |
| FrogNano 4B 2609 Microsoft | Q8_0 | fits | 7.8 GB | 251k256k with q8 cache | 26tok/sest. | 729 tok/s | chat, coding, agents, long documents |
| LFM2.5 2.6B Liquid AI | Q8_0 | fits | 7.9 GB | 128k | 26tok/sest. | 738 tok/s | chat, coding, agents, long documents |
| LFM2.5 VL 3B Liquid AI | Q8_0 | fits | 7.9 GB | 32k | 26tok/sest. | 694 tok/s | chat, coding, agents, long documents |
| Kimi VL A3B Moonshot AI | mlx-5bit | fits | 14.4 GB | 75k123k with q8 cache | 26tok/sest. | 589 tok/s | chat, coding, agents |
| granite vision 4.1 4b IBM | Q8_0 | fits | 9.8 GB | 122k128k with q8 cache | 20tok/sest. | 464 tok/s | chat, coding |
| granite 4.2 3b IBM | Q8_0 | fits | 10.1 GB | 119k128k with q8 cache | 19tok/sest. | 479 tok/s | chat, coding |
| Ministral 3 3B Instruct 2512 Mistral AI | Q8_0 | fits | 11.4 GB | 95k172k with q8 cache | 17tok/sest. | 459 tok/s | chat, coding |
| GLM 4 9B 0414 Z.ai | mlx-5bit | fits | 11.7 GB | 32k | 16tok/sest. | 247 tok/s | chat |
| DeepSeek R1 Distill Qwen 7B DeepSeek | Q8_0 | fits | 12.5 GB | 101k128k with q8 cache | 15tok/sest. | 350 tok/s | chat |
| GLM 4.6V Flash Z.ai | Q8_0 | fits | 13.3 GB | 97k128k with q8 cache | 14tok/sest. | 236 tok/s | chat |
| DeepSeek R1 Distill Llama 8B DeepSeek | Q4_K_M | fits | 14.1 GB | 69k126k with q8 cache | 13tok/sest. | 299 tok/s | chat |
| Qwen 3.8 27B Alibaba Qwen | UD-Q4_K_M | offload | 20.9 GB | – | 5tok/sest. | 118 tok/s | – |
| Qwen 3.6 27B Alibaba Qwen | Q4_K_M | offload | 21.3 GB | – | 5tok/sest. | 117 tok/s | – |
| Gemma 4 31B | Q4_K_M | offload | 24.3 GB | – | 4tok/sest. | 99 tok/s | – |
| Mistral Small 4 119B Mistral AI | UD-Q2_K_XL | offload | 40.3 GB | – | 11tok/sest. | 180 tok/s | chat |
| Qwen 3.6 35B-A3B Alibaba Qwen | MXFP4 | offload | 23.0 GB | – | 18tok/sest. | 674 tok/s | chat, coding |
| Qwen 3.5 27B Alibaba Qwen | Q4_K_M | offload | 21.2 GB | – | 5tok/sest. | 117 tok/s | – |
| Gemma 4 26B-A4B | MXFP4 | offload | 18.3 GB | – | 18tok/sest. | 782 tok/s | chat, coding |
| Qwen 3.5 35B-A3B Alibaba Qwen | Q4_K_M | offload | 23.3 GB | – | 17tok/sest. | 670 tok/s | chat, coding |
| GLM 4.7 Flash Z.ai | Q4_K_M | offload | 21.8 GB | – | 9tok/sest. | 344 tok/s | chat |
| Devstral Small 2 24B Mistral AI | Q4_K_M | offload | 24.8 GB | 2k3k with q8 cache | 4tok/sest. | 106 tok/s | – |
| Nemotron 3.5 Lightning 30B-A3B NVIDIA | MXFP4 | offload | 23.5 GB | – | 21tok/sest. | 821 tok/s | chat, coding, long documents |
| Kimi Linear 48B-A3B Moonshot AI | Q4_K_M | offload | 30.0 GB | – | 20tok/sest. | 477 tok/s | chat, coding |
| Qwen3 Coder 30B-A3B Alibaba Qwen | Q4_K_M | offload | 24.8 GB | – | 7tok/sest. | 232 tok/s | chat |
| Qwen3 32B Alibaba Qwen | Q4_K_M | offload | 35.9 GB | – | 2tok/sest. | 47 tok/s | – |
| Gemma 3 27B | Q4_K_M | offload | 22.3 GB | – | 4tok/sest. | 127 tok/s | – |
| Mistral Small 3.2 24B Mistral AI | Q4_K_M | offload | 24.8 GB | 2k3k with q8 cache | 4tok/sest. | 106 tok/s | – |
| DeepSeek R1 Distill Qwen 32B DeepSeek | Q4_K_M | offload | 36.0 GB | – | 2tok/sest. | 58 tok/s | – |
| Ministral 3 14B Mistral AI | Q4_K_M | offload | 19.1 GB | 37k67k with q8 cache | 6tok/sest. | 171 tok/s | chat |
| Qwen3 14B Alibaba Qwen | Q4_K_M | offload | 19.9 GB | 32k40k with q8 cache | 6tok/sest. | 147 tok/s | chat |
| DeepSeek R1 0528 Qwen3 8B DeepSeek | Q4_K_M | offload | 15.2 GB | 61k111k with q8 cache | 12tok/sest. | 274 tok/s | chat |
| Qwen3 8B Alibaba Qwen | Q4_K_M | offload | 15.2 GB | 40k | 12tok/sest. | 274 tok/s | chat |
| Phi-4 14B Microsoft | Q4_K_M | offload | 22.3 GB | 16k | 4tok/sest. | 139 tok/s | chat |
| Ministral 3 8B Mistral AI | Q4_K_M | offload | 14.8 GB | 63k115k with q8 cache | 12tok/sest. | 275 tok/s | chat |
| Granite 4.2 8B IBM | Q4_K_M | offload | 16.6 GB | 52k96k with q8 cache | 9tok/sest. | 236 tok/s | chat |
| Ministral 3 8B Reasoning 2512 Mistral AI | Q4_K_M | offload | 14.8 GB | 63k115k with q8 cache | 12tok/sest. | 275 tok/s | chat |
| GLM 4.1V 9B Thinking Z.ai | mlx-8bit | offload | 14.9 GB | 60k64k with q8 cache | 12tok/sest. | 235 tok/s | – |
| LFM2.5 24B A2B Liquid AI | Q4_K_M | offload | 20.8 GB | – | 9tok/sest. | 549 tok/s | chat |
| Muse Glimmer 30B meta-models | Q4_K_M | offload | 18.5 GB | – | 7tok/sest. | 127 tok/s | – |
| Ministral 3 14B Reasoning 2512 Mistral AI | Q4_K_M | offload | 19.1 GB | 37k67k with q8 cache | 6tok/sest. | 171 tok/s | chat |
| DeepSeek R1 Distill Qwen 14B DeepSeek | Q4_K_M | offload | 21.8 GB | 27k50k with q8 cache | 5tok/sest. | 127 tok/s | chat |
| Devstral Small 2507 Mistral AI | Q4_K_M | offload | 24.8 GB | 2k3k with q8 cache | 4tok/sest. | 107 tok/s | – |
| Magistral Small 2506 Mistral AI | Q4_K_M | offload | 24.8 GB | 2k3k with q8 cache | 4tok/sest. | 107 tok/s | – |
| Magistral Small 2507 Mistral AI | Q4_K_M | offload | 24.8 GB | 2k3k with q8 cache | 4tok/sest. | 107 tok/s | – |
| Magistral Small 2509 Mistral AI | Q4_K_M | offload | 24.8 GB | 2k3k with q8 cache | 4tok/sest. | 106 tok/s | – |
| Qwen3.8 27B OBLITERATED OBLITERATUS | mlx-4bit | offload | 24.9 GB | – | 4tok/sest. | 108 tok/s | – |
| granite 4.2 30b IBM | Q4_K_M | offload | 34.3 GB | – | 2tok/sest. | 69 tok/s | – |
| DeepSeek V4 Flash DeepSeek | UD-Q2_K_XL | won't run | 97.0 GB | – | – | – | – |
| GLM 5.3 Flash Z.ai | UD-Q2_K_XL | won't run | 103.5 GB | – | – | – | – |
| Qwen 3.8 Flash Next Alibaba Qwen | UD-Q2_K_XL | won't run | 76.5 GB | – | – | – | – |
| MiniMax M2.7 MiniMax | UD-Q2_K_XL | won't run | 87.1 GB | – | – | – | – |
| Qwen 3.5 122B-A10B Alibaba Qwen | UD-Q2_K_XL | won't run | 42.1 GB | – | – | – | – |
| Nemotron 3 Super 120B-A12B NVIDIA | UD-Q2_K_XL | won't run | 53.0 GB | – | – | – | – |
| Qwen3 Coder Next Alibaba Qwen | Q4_K_M | won't run | 48.2 GB | – | – | – | – |
| gpt-oss 120B OpenAI | Q4_K_M | won't run | 62.2 GB | – | – | – | – |
| GLM 4.5 Air Z.ai | Q2_K | won't run | 55.2 GB | – | – | – | – |
| Qwen3 Next 80B-A3B Alibaba Qwen | Q4_K_M | won't run | 48.2 GB | – | – | – | – |
| Llama 3.3 70B Meta | Q4_K_M | won't run | 61.1 GB | – | – | – | – |
| DeepSeek R1 DeepSeek | Q4_K_M | won't run | 382.4 GB | – | – | – | – |
| DeepSeek R1 0528 DeepSeek | Q4_K_M | won't run | 382.9 GB | – | – | – | – |
| DeepSeek R1 Distill Llama 70B DeepSeek | Q4_K_M | won't run | 61.1 GB | – | – | – | – |
| DeepSeek R1 Zero DeepSeek | Q4_K_M | won't run | 382.4 GB | – | – | – | – |
| DeepSeek V3 DeepSeek | Q4_K_M | won't run | 382.4 GB | – | – | – | – |
| DeepSeek V3 0324 DeepSeek | Q4_K_M | won't run | 382.4 GB | – | – | – | – |
| DeepSeek V3.1 DeepSeek | Q4_K_M | won't run | 383.3 GB | – | – | – | – |
| DeepSeek V3.1 Terminus DeepSeek | Q4_K_M | won't run | 383.3 GB | – | – | – | – |
| DeepSeek V3.2 DeepSeek | Q4_K_M | won't run | 383.3 GB | – | – | – | – |
| DeepSeek V4 Flash 0731 DeepSeek | MXFP4 | won't run | 152.5 GB | – | – | – | – |
| DeepSeek V4 Flash Vision Exp DeepSeek | MXFP4 | won't run | 161.3 GB | – | – | – | – |
| Devstral 2 123B Instruct 2512 Mistral AI | Q4_K_M | won't run | 93.2 GB | – | – | – | – |
| GLM 4.5 Z.ai | Q4_K_M | won't run | 226.1 GB | – | – | – | – |
| GLM 4.5V Z.ai | Q4_K_M | won't run | 72.2 GB | – | – | – | – |
| GLM 4.6 Z.ai | Q4_K_M | won't run | 225.3 GB | – | – | – | – |
| GLM 4.6V Z.ai | Q4_K_M | won't run | 78.6 GB | – | – | – | – |
| GLM 4.7 Z.ai | Q4_K_M | won't run | 226.1 GB | – | – | – | – |
| GLM 5 Z.ai | MXFP4 | won't run | 389.3 GB | – | – | – | – |
| GLM 5.1 Z.ai | MXFP4 | won't run | 426.9 GB | – | – | – | – |
| GLM 5.2 Z.ai | UD-Q4_K_M | won't run | 440.8 GB | – | – | – | – |
| GLM 5.3 Z.ai | Q8_0 | won't run | 753.3 GB | – | – | – | – |
| Kimi Dev 72B Moonshot AI | Q6_K | won't run | 81.4 GB | – | – | – | – |
| Kimi K2 Moonshot AI | Q4_K_M | won't run | 583.9 GB | – | – | – | – |
| Kimi K2 Instruct 0905 Moonshot AI | Q4_K_M | won't run | 584.3 GB | – | – | – | – |
| Kimi K2 Thinking Moonshot AI | Q4_K_M | won't run | 584.3 GB | – | – | – | – |
| Kimi K2.5 Moonshot AI | Q4_K_M | won't run | 584.3 GB | – | – | – | – |
| MiniMax M2 MiniMax | Q4_K_M | won't run | 145.8 GB | – | – | – | – |
| MiniMax M2.1 MiniMax | Q4_K_M | won't run | 145.8 GB | – | – | – | – |
| MiniMax M2.5 MiniMax | MXFP4 | won't run | 132.2 GB | – | – | – | – |
| MiniMax M3 MiniMax | MXFP4 | won't run | 248.7 GB | – | – | – | – |
| Mistral Medium 3.5 128B Mistral AI | Q4_K_M | won't run | 93.2 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 64k context and generates at about 27 tok/s, an estimate from its memory bandwidth. 27 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 97 GB at 64k 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 NVIDIA GeForce RTX 4060 Ti 8GB is not on sale new in the US right now; used, it starts at $450 (asking price).
Speeds on this page are for 64k context. See the best hardware for local AI at every budget.