NVIDIA GeForce RTX 4060
8 GB · 272 GB/s · 115 W
In short
The NVIDIA GeForce RTX 4060 has 8 GB of GDDR6 memory with 272 GB/s of bandwidth, which sets how fast it generates text. At 8k context it runs 38 of the 129 open models we track fully in memory; the most capable is gpt-oss 20B at MXFP4, at about 38 tok/s (estimated). The best new price for the NVIDIA GeForce RTX 4060 in Canada is $839 at Amazon; used, it starts at $650 (asking price).
Updated . Speeds are estimates; how they are worked out.
Prices
Every retailer we track in Canada, cheapest first. Buy links may earn us a commission.
| Retailer | Condition | Price | Stock | Checked | Buy |
|---|---|---|---|---|---|
| eBayMSI Gaming GeForce RTX 4060 8GB GDRR6 GPU RTX 4060 Ventus 2X Black 8G OC | used, asking price | $650 | In stock | 3 h ago | Buy |
| eBayUsed GeForce RTX 4060 on eBay | used, asking price | $650 | In stock | 6 h ago | Buy |
| eBayASUS ROG Strix GeForce RTX 4060 OC Edition Gaming Graphics Card 8GB | used, asking price | $800 | In stock | 3 h ago | Buy |
| eBayUsed NVIDIA GeForce RTX 4060 8GB GDDR6 Graphics Card | used, asking price | $799+ $26 delivery | In stock | 3 h ago | Buy |
| AmazonASUS DUAL-RTX4060-O8G | New | $839+ $6 delivery | In stock | 5 h ago | Buy |
| AmazonASUS Dual GeForce RTX 4060 OC White Edition 8GB GDDR6 | New | $1,003+ $6 delivery | In stock | 5 h ago | Buy |
Used on eBay: median asking price $650, most between $650 and $799 (5 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 @8k | Prefill | Good for |
|---|---|---|---|---|---|---|---|
| gpt-oss 20B OpenAI | MXFP4 | fits | 12.6 GB | 82k128k with q8 cache | 38tok/sest. | 738 tok/s | chat, coding, agents |
| Gemma 4 12B | Q8_0 | fits | 13.4 GB | 73k122k with q8 cache | 13tok/sest. | 271 tok/s | – |
| Qwen 3.5 9B Alibaba Qwen | Q8_0 | fits | 10.3 GB | 128k212k with q8 cache | 17tok/sest. | 343 tok/s | chat |
| Ministral 3 14B Mistral AI | Q6_K | fits | 12.7 GB | 20k37k with q8 cache | 14tok/sest. | 223 tok/s | – |
| Qwen3 14B Alibaba Qwen | Q6_K | fits | 13.7 GB | 14k27k with q8 cache | 13tok/sest. | 207 tok/s | – |
| DeepSeek R1 0528 Qwen3 8B DeepSeek | Q8_0 | fits | 10.4 GB | 38k69k with q8 cache | 17tok/sest. | 362 tok/s | chat |
| Qwen3 8B Alibaba Qwen | Q8_0 | fits | 10.4 GB | 38k40k with q8 cache | 17tok/sest. | 362 tok/s | chat |
| Phi-4 14B Microsoft | Q6_K | fits | 13.9 GB | 12k16k with q8 cache | 12tok/sest. | 208 tok/s | – |
| Qwen 3.5 4B Alibaba Qwen | Q8_0 | fits | 5.6 GB | 255k256k with q8 cache | 35tok/sest. | 690 tok/s | chat, coding, long documents |
| Ministral 3 8B Mistral AI | Q8_0 | fits | 10.6 GB | 38k69k with q8 cache | 17tok/sest. | 338 tok/s | chat |
| Granite 4.2 8B IBM | Q8_0 | fits | 11.1 GB | 30k56k with q8 cache | 16tok/sest. | 336 tok/s | chat |
| Gemma 4 E4B | Q8_0 | fits | 9.0 GB | 128k | 20tok/sest. | 415 tok/s | chat |
| LFM2.5 8B-A1B Liquid AI | Q8_0 | fits | 9.9 GB | 100k125k with q8 cache | 69tok/sest. | 2424 tok/s | chat, coding |
| Llama 3.1 8B Meta | Q6_K | fits | 8.3 GB | 57k104k with q8 cache | 22tok/sest. | 374 tok/s | chat |
| Qwen2.5 0.5B Alibaba Qwen | Q8_0 | fits | 1.7 GB | 32k | 267tok/sest. | 5074 tok/s | chat, coding, long documents |
| Qwen3.5 0.8B Alibaba Qwen | Q8_0 | fits | 2.0 GB | 256k | 181tok/sest. | 3491 tok/s | chat, coding, agents, long documents |
| Qwen3.5 2B Alibaba Qwen | Q5_K_M | fits | 2.6 GB | 256k | 109tok/sest. | 1432 tok/s | chat, coding, agents, long documents |
| LFM3 1.7B Liquid AI | Q8_0 | fits | 3.0 GB | 256k | 86tok/sest. | 1840 tok/s | chat, coding, agents, long documents |
| DeepSeek R1 Distill Qwen 1.5B DeepSeek | Q8_0 | fits | 3.1 GB | 128k | 79tok/sest. | 1597 tok/s | chat, coding, long documents |
| MiniCPM5 2B openbmb | Q8_0 | fits | 4.0 GB | 128k | 56tok/sest. | 1056 tok/s | chat, coding, agents |
| LFM2.5 2.6B Liquid AI | Q8_0 | fits | 4.3 GB | 128k | 50tok/sest. | 1063 tok/s | chat, coding, agents, long documents |
| LFM2.5 VL 3B Liquid AI | Q8_0 | fits | 4.3 GB | 32k | 50tok/sest. | 938 tok/s | chat, coding, agents, long documents |
| Spark X2.5 4B XHToken | mlx-4bit | fits | 4.3 GB | 263k441k with q8 cache | 50tok/sest. | 751 tok/s | chat, coding, agents, long documents |
| Kimi VL A3B Thinking 2506 Moonshot AI | Q4_K_M | fits | 11.2 GB | 109k128k with q8 cache | 46tok/sest. | 985 tok/s | chat, coding, long documents |
| granite vision 4.1 4b IBM | Q8_0 | fits | 5.1 GB | 122k128k with q8 cache | 39tok/sest. | 703 tok/s | chat, coding |
| Ministral 3 3B Instruct 2512 Mistral AI | Q8_0 | fits | 5.4 GB | 95k172k with q8 cache | 37tok/sest. | 720 tok/s | chat, coding |
| granite 4.2 3b IBM | Q8_0 | fits | 5.4 GB | 119k128k with q8 cache | 37tok/sest. | 756 tok/s | chat, coding |
| Kimi VL A3B Moonshot AI | mlx-6bit | fits | 14.3 GB | 23k37k with q8 cache | 36tok/sest. | 924 tok/s | chat, coding |
| Mellum2.1 12B A2.5B Thinking JetBrains | Q8_0 | fits | 13.3 GB | 83k125k with q8 cache | 36tok/sest. | 1168 tok/s | chat, coding, agents, long documents |
| FrogNano 4B 2609 Microsoft | Q8_0 | fits | 5.7 GB | 251k256k with q8 cache | 34tok/sest. | 690 tok/s | chat, coding, long documents |
| DeepSeek R1 Distill Qwen 7B DeepSeek | Q8_0 | fits | 9.1 GB | 101k128k with q8 cache | 20tok/sest. | 403 tok/s | chat |
| GLM 4 9B 0414 Z.ai | mlx-5bit | fits | 9.1 GB | 32k | 20tok/sest. | 317 tok/s | chat |
| DeepSeek R1 Distill Llama 8B DeepSeek | Q8_0 | fits | 10.1 GB | 43k79k with q8 cache | 18tok/sest. | 374 tok/s | chat |
| Ministral 3 8B Reasoning 2512 Mistral AI | Q8_0 | fits | 10.6 GB | 38k69k with q8 cache | 17tok/sest. | 338 tok/s | chat |
| GLM 4.6V Flash Z.ai | Q8_0 | fits | 10.8 GB | 97k128k with q8 cache | 16tok/sest. | 292 tok/s | chat |
| GLM 4.1V 9B Thinking Z.ai | mlx-8bit | fits | 12.4 GB | 60k64k with q8 cache | 14tok/sest. | 292 tok/s | – |
| Ministral 3 14B Reasoning 2512 Mistral AI | Q6_K | fits | 12.7 GB | 20k37k with q8 cache | 14tok/sest. | 223 tok/s | – |
| DeepSeek R1 Distill Qwen 14B DeepSeek | Q6_K | fits | 13.9 GB | 12k22k with q8 cache | 12tok/sest. | 203 tok/s | – |
| Qwen 3.8 27B Alibaba Qwen | UD-Q4_K_M | offload | 17.1 GB | – | 8tok/sest. | 109 tok/s | – |
| Qwen 3.5 122B-A10B Alibaba Qwen | UD-Q2_K_XL | offload | 40.4 GB | – | 11tok/sest. | 192 tok/s | – |
| Qwen 3.6 27B Alibaba Qwen | Q4_K_M | offload | 17.4 GB | – | 7tok/sest. | 107 tok/s | – |
| Gemma 4 31B | Q4_K_M | offload | 19.6 GB | – | 6tok/sest. | 88 tok/s | – |
| Mistral Small 4 119B Mistral AI | UD-Q2_K_XL | offload | 38.8 GB | – | 17tok/sest. | 254 tok/s | chat |
| Qwen 3.6 35B-A3B Alibaba Qwen | MXFP4 | offload | 21.6 GB | – | 29tok/sest. | 817 tok/s | chat, coding |
| Qwen 3.5 27B Alibaba Qwen | Q4_K_M | offload | 17.4 GB | – | 7tok/sest. | 108 tok/s | – |
| Gemma 4 26B-A4B | MXFP4 | offload | 16.9 GB | – | 27tok/sest. | 714 tok/s | chat, coding |
| Qwen 3.5 35B-A3B Alibaba Qwen | Q4_K_M | offload | 21.9 GB | – | 28tok/sest. | 811 tok/s | chat, coding |
| GLM 4.7 Flash Z.ai | Q4_K_M | offload | 18.6 GB | – | 20tok/sest. | 496 tok/s | chat |
| Devstral Small 2 24B Mistral AI | Q4_K_M | offload | 15.7 GB | 2k3k with q8 cache | 9tok/sest. | 129 tok/s | – |
| Nemotron 3.5 Lightning 30B-A3B NVIDIA | MXFP4 | offload | 22.9 GB | – | 25tok/sest. | 826 tok/s | chat, coding, long documents |
| Qwen3 Coder 30B-A3B Alibaba Qwen | Q4_K_M | offload | 19.2 GB | – | 25tok/sest. | 630 tok/s | chat |
| Kimi Linear 48B-A3B Moonshot AI | Q4_K_M | offload | 29.2 GB | – | 25tok/sest. | 670 tok/s | chat, coding |
| Qwen3 32B Alibaba Qwen | Q4_K_M | offload | 21.6 GB | – | 5tok/sest. | 74 tok/s | – |
| Mistral Small 3.2 24B Mistral AI | Q4_K_M | offload | 15.7 GB | 2k3k with q8 cache | 9tok/sest. | 129 tok/s | – |
| Gemma 3 27B | Q4_K_M | offload | 17.6 GB | – | 7tok/sest. | 108 tok/s | – |
| DeepSeek R1 Distill Qwen 32B DeepSeek | Q4_K_M | offload | 21.6 GB | – | 5tok/sest. | 77 tok/s | – |
| LFM2.5 24B A2B Liquid AI | Q4_K_M | offload | 16.1 GB | – | 44tok/sest. | 1208 tok/s | chat |
| Devstral Small 2507 Mistral AI | Q4_K_M | offload | 15.7 GB | 2k3k with q8 cache | 9tok/sest. | 131 tok/s | – |
| Magistral Small 2506 Mistral AI | Q4_K_M | offload | 15.7 GB | 2k3k with q8 cache | 9tok/sest. | 131 tok/s | – |
| Magistral Small 2507 Mistral AI | Q4_K_M | offload | 15.7 GB | 2k3k with q8 cache | 9tok/sest. | 131 tok/s | – |
| Magistral Small 2509 Mistral AI | Q4_K_M | offload | 15.7 GB | 2k3k with q8 cache | 9tok/sest. | 129 tok/s | – |
| Muse Glimmer 30B meta-models | Q4_K_M | offload | 17.4 GB | – | 7tok/sest. | 100 tok/s | – |
| granite 4.2 30b IBM | Q4_K_M | offload | 19.9 GB | – | 5tok/sest. | 91 tok/s | – |
| Qwen3.8 27B OBLITERATED OBLITERATUS | mlx-4bit | offload | 21.0 GB | – | 5tok/sest. | 97 tok/s | – |
| DeepSeek V4 Flash DeepSeek | UD-Q2_K_XL | won't run | 92.0 GB | – | – | – | – |
| GLM 5.3 Flash Z.ai | UD-Q2_K_XL | won't run | 102.6 GB | – | – | – | – |
| Qwen 3.8 Flash Next Alibaba Qwen | UD-Q2_K_XL | won't run | 74.9 GB | – | – | – | – |
| MiniMax M2.7 MiniMax | UD-Q2_K_XL | won't run | 73.2 GB | – | – | – | – |
| Nemotron 3 Super 120B-A12B NVIDIA | UD-Q2_K_XL | won't run | 52.3 GB | – | – | – | – |
| Qwen3 Coder Next Alibaba Qwen | Q4_K_M | won't run | 46.6 GB | – | – | – | – |
| gpt-oss 120B OpenAI | Q4_K_M | won't run | 59.9 GB | – | – | – | – |
| GLM 4.5 Air Z.ai | Q2_K | won't run | 44.8 GB | – | – | – | – |
| Qwen3 Next 80B-A3B Alibaba Qwen | Q4_K_M | won't run | 46.6 GB | – | – | – | – |
| Llama 3.3 70B Meta | Q4_K_M | won't run | 43.2 GB | – | – | – | – |
| DeepSeek R1 DeepSeek | Q4_K_M | won't run | 378.3 GB | – | – | – | – |
| DeepSeek R1 0528 DeepSeek | Q4_K_M | won't run | 378.8 GB | – | – | – | – |
| DeepSeek R1 Distill Llama 70B DeepSeek | Q4_K_M | won't run | 43.2 GB | – | – | – | – |
| DeepSeek R1 Zero DeepSeek | Q4_K_M | won't run | 378.3 GB | – | – | – | – |
| DeepSeek V3 DeepSeek | Q4_K_M | won't run | 378.3 GB | – | – | – | – |
| DeepSeek V3 0324 DeepSeek | Q4_K_M | won't run | 378.3 GB | – | – | – | – |
| DeepSeek V3.1 DeepSeek | Q4_K_M | won't run | 379.2 GB | – | – | – | – |
| DeepSeek V3.1 Terminus DeepSeek | Q4_K_M | won't run | 379.2 GB | – | – | – | – |
| DeepSeek V3.2 DeepSeek | Q4_K_M | won't run | 379.2 GB | – | – | – | – |
| DeepSeek V4 Flash 0731 DeepSeek | MXFP4 | won't run | 147.5 GB | – | – | – | – |
| DeepSeek V4 Flash Vision Exp DeepSeek | MXFP4 | won't run | 156.2 GB | – | – | – | – |
| Devstral 2 123B Instruct 2512 Mistral AI | Q4_K_M | won't run | 73.7 GB | – | – | – | – |
| GLM 4.5 Z.ai | Q4_K_M | won't run | 205.6 GB | – | – | – | – |
| GLM 4.5V Z.ai | Q4_K_M | won't run | 61.8 GB | – | – | – | – |
| GLM 4.6 Z.ai | Q4_K_M | won't run | 204.8 GB | – | – | – | – |
| GLM 4.6V Z.ai | Q4_K_M | won't run | 68.2 GB | – | – | – | – |
| GLM 4.7 Z.ai | Q4_K_M | won't run | 205.6 GB | – | – | – | – |
| GLM 5 Z.ai | MXFP4 | won't run | 384.2 GB | – | – | – | – |
| GLM 5.1 Z.ai | MXFP4 | won't run | 421.8 GB | – | – | – | – |
| GLM 5.2 Z.ai | UD-Q4_K_M | won't run | 435.7 GB | – | – | – | – |
| GLM 5.3 Z.ai | Q8_0 | won't run | 748.2 GB | – | – | – | – |
| Kimi Dev 72B Moonshot AI | Q6_K | won't run | 63.6 GB | – | – | – | – |
| Kimi K2 Moonshot AI | Q4_K_M | won't run | 579.8 GB | – | – | – | – |
| Kimi K2 Instruct 0905 Moonshot AI | Q4_K_M | won't run | 580.3 GB | – | – | – | – |
| Kimi K2 Thinking Moonshot AI | Q4_K_M | won't run | 580.3 GB | – | – | – | – |
| Kimi K2.5 Moonshot AI | Q4_K_M | won't run | 580.3 GB | – | – | – | – |
| MiniMax M2 MiniMax | Q4_K_M | won't run | 131.9 GB | – | – | – | – |
| MiniMax M2.1 MiniMax | Q4_K_M | won't run | 131.9 GB | – | – | – | – |
| MiniMax M2.5 MiniMax | MXFP4 | won't run | 118.4 GB | – | – | – | – |
| MiniMax M3 MiniMax | MXFP4 | won't run | 241.8 GB | – | – | – | – |
| Mistral Medium 3.5 128B Mistral AI | Q4_K_M | won't run | 73.7 GB | – | – | – | – |
Specifications
From the manufacturer's published specifications; sources are linked where we have them.
- GPU
- GeForce RTX 4060
- Memory
- 8 GB GDDR6
- Memory bus
- 128-bit
- Memory bandwidth
- 272 GB/s
- FP16 compute
- 15.114 TFLOPS
- Software
- CUDA
- Board power
- 115 W
- PCIe generation
- Gen 4
- PCIe lanes
- x8
Compare with
Questions
- How much memory does the NVIDIA GeForce RTX 4060 have for AI models?
- The NVIDIA GeForce RTX 4060 has 8 GB of GDDR6 memory with 272 GB/s of bandwidth, which sets how fast it generates text.
- What is the best model the NVIDIA GeForce RTX 4060 can run locally?
- gpt-oss 20B (MXFP4) fits fully in its memory at 8k context and generates at about 38 tok/s, an estimate from its memory bandwidth. 38 of the 129 models we track fit at that context.
- Can the NVIDIA GeForce RTX 4060 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 4060 and system RAM can hold.
- How much does the NVIDIA GeForce RTX 4060 cost in Canada?
- The best new price for the NVIDIA GeForce RTX 4060 in Canada is $839 at Amazon; used, it starts at $650 (asking price).
Speeds on this page are for 8k context. See the best hardware for local AI at every budget.