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 128k context it runs 5 of the 129 open models we track fully in memory; the most capable is Qwen2.5 0.5B at Q8_0, at about 79 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 | 2 h ago | Buy |
| eBayUsed GeForce RTX 4060 on eBay | used, asking price | $650 | In stock | 5 h ago | Buy |
| eBayASUS ROG Strix GeForce RTX 4060 OC Edition Gaming Graphics Card 8GB | used, asking price | $800 | In stock | 2 h ago | Buy |
| eBayUsed NVIDIA GeForce RTX 4060 8GB GDDR6 Graphics Card | used, asking price | $799+ $26 delivery | In stock | 2 h ago | Buy |
| AmazonASUS DUAL-RTX4060-O8G | New | $839+ $6 delivery | In stock | 4 h ago | Buy |
| AmazonASUS Dual GeForce RTX 4060 OC White Edition 8GB GDDR6 | New | $1,003+ $6 delivery | In stock | 4 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 it runs
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 |
|---|---|---|---|---|---|---|---|
| Qwen2.5 0.5B Alibaba Qwen | Q8_0 | fits | 3.4 GB | 32k | 79tok/sest. | 1027 tok/s | chat, coding, long documents |
| Qwen3.5 0.8B Alibaba Qwen | Q8_0 | fits | 3.7 GB | 256k | 69tok/sest. | 1369 tok/s | chat, coding, agents, long documents |
| Qwen3.5 2B Alibaba Qwen | Q5_K_M | fits | 4.3 GB | 256k | 55tok/sest. | 875 tok/s | chat, coding, agents, long documents |
| LFM3 1.7B Liquid AI | Q8_0 | fits | 4.7 GB | 256k | 49tok/sest. | 1013 tok/s | chat, coding, agents, long documents |
| DeepSeek R1 Distill Qwen 1.5B DeepSeek | Q8_0 | fits | 6.7 GB | 128k | 30tok/sest. | 459 tok/s | chat, coding, long documents |
| Qwen 3.8 27B Alibaba Qwen | UD-Q4_K_M | offload | 24.9 GB | – | 3tok/sest. | 46 tok/s | – |
| Qwen 3.6 27B Alibaba Qwen | Q4_K_M | offload | 25.3 GB | – | 3tok/sest. | 46 tok/s | – |
| Gemma 4 31B | Q4_K_M | offload | 29.3 GB | – | 2tok/sest. | 42 tok/s | – |
| Qwen 3.6 35B-A3B Alibaba Qwen | MXFP4 | offload | 24.2 GB | – | 9tok/sest. | 226 tok/s | chat |
| Qwen 3.5 27B Alibaba Qwen | Q4_K_M | offload | 25.2 GB | – | 3tok/sest. | 46 tok/s | – |
| Gemma 4 26B-A4B | MXFP4 | offload | 19.6 GB | – | 8tok/sest. | 295 tok/s | chat |
| Qwen 3.5 35B-A3B Alibaba Qwen | Q4_K_M | offload | 24.5 GB | – | 9tok/sest. | 225 tok/s | chat |
| GLM 4.7 Flash Z.ai | Q4_K_M | offload | 25.1 GB | – | 4tok/sest. | 106 tok/s | – |
| Nemotron 3.5 Lightning 30B-A3B NVIDIA | MXFP4 | offload | 23.9 GB | – | 13tok/sest. | 306 tok/s | chat |
| Kimi Linear 48B-A3B Moonshot AI | Q4_K_M | offload | 30.5 GB | – | 13tok/sest. | 160 tok/s | chat |
| gpt-oss 20B OpenAI | Q4_K_M | offload | 15.3 GB | – | 8tok/sest. | 210 tok/s | chat |
| Qwen3 Coder 30B-A3B Alibaba Qwen | Q4_K_M | offload | 30.7 GB | – | 3tok/sest. | 62 tok/s | chat |
| Gemma 4 12B | Q4_K_M | offload | 10.4 GB | – | 10tok/sest. | 164 tok/s | chat |
| Qwen 3.5 9B Alibaba Qwen | Q4_K_M | offload | 10.8 GB | 36k60k with q8 cache | 10tok/sest. | 191 tok/s | chat, coding |
| Gemma 3 27B | Q4_K_M | offload | 27.3 GB | – | 2tok/sest. | 55 tok/s | – |
| Ministral 3 14B Mistral AI | Q4_K_M | offload | 29.1 GB | – | 2tok/sest. | 51 tok/s | – |
| Qwen3 14B Alibaba Qwen | Q4_K_M | offload | 29.8 GB | – | 2tok/sest. | 43 tok/s | – |
| DeepSeek R1 0528 Qwen3 8B DeepSeek | Q4_K_M | offload | 24.1 GB | 13k25k with q8 cache | 3tok/sest. | 69 tok/s | chat |
| Qwen3 8B Alibaba Qwen | Q4_K_M | offload | 24.1 GB | 13k25k with q8 cache | 3tok/sest. | 69 tok/s | chat |
| Qwen 3.5 4B Alibaba Qwen | Q4_K_M | offload | 8.0 GB | 110k182k with q8 cache | 20tok/sest. | 357 tok/s | chat, coding, long documents |
| Ministral 3 8B Mistral AI | Q4_K_M | offload | 23.3 GB | 13k24k with q8 cache | 3tok/sest. | 71 tok/s | chat |
| Granite 4.2 8B IBM | Q4_K_M | offload | 26.6 GB | 9k17k with q8 cache | 2tok/sest. | 61 tok/s | chat |
| Gemma 4 E4B | Q4_K_M | offload | 9.6 GB | 61k100k with q8 cache | 12tok/sest. | 286 tok/s | chat, coding |
| LFM2.5 8B-A1B Liquid AI | UD-Q4_K_M | offload | 12.4 GB | 33k56k with q8 cache | 7tok/sest. | 332 tok/s | chat, coding |
| Llama 3.1 8B Meta | Q4_K_M | offload | 22.0 GB | 16k29k with q8 cache | 3tok/sest. | 77 tok/s | chat |
| FrogNano 4B 2609 Microsoft | Q4_K_M | offload | 8.1 GB | 109k180k with q8 cache | 19tok/sest. | 356 tok/s | chat, coding, agents, long documents |
| MiniCPM5 2B openbmb | Q4_K_M | offload | 8.2 GB | 110k128k with q8 cache | 19tok/sest. | 229 tok/s | chat, coding, agents |
| Spark X2.5 4B XHToken | mlx-4bit | offload | 8.8 GB | 93k156k with q8 cache | 15tok/sest. | 332 tok/s | chat, coding, agents, long documents |
| Mellum2.1 12B A2.5B Thinking JetBrains | Q4_K_M | offload | 11.5 GB | – | 15tok/sest. | 400 tok/s | chat, coding |
| LFM2.5 2.6B Liquid AI | Q4_K_M | offload | 10.5 GB | 79k128k with q8 cache | 10tok/sest. | 251 tok/s | chat, coding, agents |
| LFM2.5 VL 3B Liquid AI | Q4_K_M | offload | 10.5 GB | 32k | 10tok/sest. | 242 tok/s | chat, coding, agents |
| Kimi VL A3B Thinking 2506 Moonshot AI | Q4_K_M | offload | 15.1 GB | – | 8tok/sest. | 220 tok/s | chat |
| GLM 4.6V Flash Z.ai | Q4_K_M | offload | 12.2 GB | 21k35k with q8 cache | 8tok/sest. | 78 tok/s | chat |
| Kimi VL A3B Moonshot AI | mlx-5bit | offload | 16.3 GB | – | 7tok/sest. | 157 tok/s | chat |
| DeepSeek R1 Distill Qwen 7B DeepSeek | Q4_K_M | offload | 12.8 GB | 38k66k with q8 cache | 7tok/sest. | 117 tok/s | chat, coding |
| GLM 4 9B 0414 Z.ai | mlx-4bit | offload | 13.0 GB | 2k4k with q8 cache | 7tok/sest. | 78 tok/s | chat |
| granite vision 4.1 4b IBM | Q4_K_M | offload | 13.4 GB | 56k100k with q8 cache | 6tok/sest. | 136 tok/s | chat, coding |
| granite 4.2 3b IBM | Q4_K_M | offload | 13.6 GB | 54k95k with q8 cache | 6tok/sest. | 138 tok/s | chat, coding |
| Ministral 3 3B Instruct 2512 Mistral AI | Q4_K_M | offload | 16.4 GB | 43k78k with q8 cache | 5tok/sest. | 122 tok/s | chat, coding |
| GLM 4.1V 9B Thinking Z.ai | mlx-8bit | offload | 17.4 GB | – | 4tok/sest. | 67 tok/s | – |
| Muse Glimmer 30B meta-models | Q4_K_M | offload | 19.3 GB | – | 4tok/sest. | 55 tok/s | – |
| LFM2.5 24B A2B Liquid AI | Q4_K_M | offload | 25.7 GB | – | 3tok/sest. | 146 tok/s | chat |
| DeepSeek R1 Distill Llama 8B DeepSeek | Q4_K_M | offload | 22.0 GB | 16k29k with q8 cache | 3tok/sest. | 77 tok/s | chat |
| Ministral 3 8B Reasoning 2512 Mistral AI | Q4_K_M | offload | 23.3 GB | 13k24k with q8 cache | 3tok/sest. | 71 tok/s | chat |
| Ministral 3 14B Reasoning 2512 Mistral AI | Q4_K_M | offload | 29.1 GB | – | 2tok/sest. | 51 tok/s | – |
| Qwen3.8 27B OBLITERATED OBLITERATUS | mlx-4bit | offload | 28.8 GB | – | 2tok/sest. | 44 tok/s | – |
| DeepSeek V4 Flash DeepSeek | UD-Q2_K_XL | won't run | 102.4 GB | – | – | – | – |
| GLM 5.3 Flash Z.ai | UD-Q2_K_XL | won't run | 104.2 GB | – | – | – | – |
| Qwen 3.8 Flash Next Alibaba Qwen | UD-Q2_K_XL | won't run | 78.0 GB | – | – | – | – |
| MiniMax M2.7 MiniMax | UD-Q2_K_XL | won't run | 102.6 GB | – | – | – | – |
| Qwen 3.5 122B-A10B Alibaba Qwen | UD-Q2_K_XL | won't run | 43.6 GB | – | – | – | – |
| Nemotron 3 Super 120B-A12B NVIDIA | UD-Q2_K_XL | won't run | 53.5 GB | – | – | – | – |
| Qwen3 Coder Next Alibaba Qwen | Q4_K_M | won't run | 49.7 GB | – | – | – | – |
| gpt-oss 120B OpenAI | Q4_K_M | won't run | 64.4 GB | – | – | – | – |
| Mistral Small 4 119B Mistral AI | UD-Q2_K_XL | won't run | 41.7 GB | – | – | – | – |
| GLM 4.5 Air Z.ai | Q2_K | won't run | 66.7 GB | – | – | – | – |
| Devstral Small 2 24B Mistral AI | Q4_K_M | won't run | 34.8 GB | – | – | – | – |
| Qwen3 Next 80B-A3B Alibaba Qwen | Q4_K_M | won't run | 49.7 GB | – | – | – | – |
| Qwen3 32B Alibaba Qwen | Q4_K_M | won't run | 51.9 GB | – | – | – | – |
| DeepSeek R1 Distill Qwen 32B DeepSeek | Q4_K_M | won't run | 51.9 GB | – | – | – | – |
| Llama 3.3 70B Meta | Q4_K_M | won't run | 81.1 GB | – | – | – | – |
| Mistral Small 3.2 24B Mistral AI | Q4_K_M | won't run | 34.8 GB | – | – | – | – |
| Phi-4 14B Microsoft | Q4_K_M | won't run | 34.7 GB | – | – | – | – |
| DeepSeek R1 DeepSeek | Q4_K_M | won't run | 386.7 GB | – | – | – | – |
| DeepSeek R1 0528 DeepSeek | Q4_K_M | won't run | 387.2 GB | – | – | – | – |
| DeepSeek R1 Distill Llama 70B DeepSeek | Q4_K_M | won't run | 81.1 GB | – | – | – | – |
| DeepSeek R1 Distill Qwen 14B DeepSeek | Q4_K_M | won't run | 33.8 GB | – | – | – | – |
| DeepSeek R1 Zero DeepSeek | Q4_K_M | won't run | 386.7 GB | – | – | – | – |
| DeepSeek V3 DeepSeek | Q4_K_M | won't run | 386.7 GB | – | – | – | – |
| DeepSeek V3 0324 DeepSeek | Q4_K_M | won't run | 386.7 GB | – | – | – | – |
| DeepSeek V3.1 DeepSeek | Q4_K_M | won't run | 387.6 GB | – | – | – | – |
| DeepSeek V3.1 Terminus DeepSeek | Q4_K_M | won't run | 387.6 GB | – | – | – | – |
| DeepSeek V3.2 DeepSeek | Q4_K_M | won't run | 387.6 GB | – | – | – | – |
| DeepSeek V4 Flash 0731 DeepSeek | MXFP4 | won't run | 157.8 GB | – | – | – | – |
| DeepSeek V4 Flash Vision Exp DeepSeek | MXFP4 | won't run | 166.6 GB | – | – | – | – |
| Devstral 2 123B Instruct 2512 Mistral AI | Q4_K_M | won't run | 115.2 GB | – | – | – | – |
| Devstral Small 2507 Mistral AI | Q4_K_M | won't run | 34.8 GB | – | – | – | – |
| GLM 4.5 Z.ai | Q4_K_M | won't run | 249.0 GB | – | – | – | – |
| GLM 4.5V Z.ai | Q4_K_M | won't run | 83.7 GB | – | – | – | – |
| GLM 4.6 Z.ai | Q4_K_M | won't run | 248.2 GB | – | – | – | – |
| GLM 4.6V Z.ai | Q4_K_M | won't run | 90.1 GB | – | – | – | – |
| GLM 4.7 Z.ai | Q4_K_M | won't run | 249.0 GB | – | – | – | – |
| GLM 5 Z.ai | MXFP4 | won't run | 394.8 GB | – | – | – | – |
| GLM 5.1 Z.ai | MXFP4 | won't run | 432.4 GB | – | – | – | – |
| GLM 5.2 Z.ai | UD-Q4_K_M | won't run | 446.3 GB | – | – | – | – |
| GLM 5.3 Z.ai | Q8_0 | won't run | 758.7 GB | – | – | – | – |
| Kimi Dev 72B Moonshot AI | Q6_K | won't run | 101.4 GB | – | – | – | – |
| Kimi K2 Moonshot AI | Q4_K_M | won't run | 588.2 GB | – | – | – | – |
| Kimi K2 Instruct 0905 Moonshot AI | Q4_K_M | won't run | 588.6 GB | – | – | – | – |
| Kimi K2 Thinking Moonshot AI | Q4_K_M | won't run | 588.6 GB | – | – | – | – |
| Kimi K2.5 Moonshot AI | Q4_K_M | won't run | 588.6 GB | – | – | – | – |
| Magistral Small 2506 Mistral AI | Q4_K_M | won't run | 34.8 GB | – | – | – | – |
| Magistral Small 2507 Mistral AI | Q4_K_M | won't run | 34.8 GB | – | – | – | – |
| Magistral Small 2509 Mistral AI | Q4_K_M | won't run | 34.8 GB | – | – | – | – |
| MiniMax M2 MiniMax | Q4_K_M | won't run | 161.3 GB | – | – | – | – |
| MiniMax M2.1 MiniMax | Q4_K_M | won't run | 161.3 GB | – | – | – | – |
| MiniMax M2.5 MiniMax | MXFP4 | won't run | 147.7 GB | – | – | – | – |
| MiniMax M3 MiniMax | MXFP4 | won't run | 256.1 GB | – | – | – | – |
| Mistral Medium 3.5 128B Mistral AI | Q4_K_M | won't run | 115.2 GB | – | – | – | – |
| granite 4.2 30b IBM | Q4_K_M | won't run | 50.2 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?
- Qwen2.5 0.5B (Q8_0) fits fully in its memory at 128k context and generates at about 79 tok/s, an estimate from its memory bandwidth. 5 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 102 GB at 128k 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 128k context. See the best hardware for local AI at every budget.