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 32k context it runs 36 of the 129 open models we track fully in memory; the most capable is gpt-oss 20B at MXFP4, at about 31 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 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 @32k | Prefill | Good for |
|---|---|---|---|---|---|---|---|
| gpt-oss 20B OpenAI | MXFP4 | fits | 13.3 GB | 82k128k with q8 cache | 31tok/sest. | 585 tok/s | chat, coding, agents |
| Gemma 4 12B | Q8_0 | fits | 13.9 GB | 73k122k with q8 cache | 13tok/sest. | 254 tok/s | – |
| Qwen 3.5 9B Alibaba Qwen | Q8_0 | fits | 11.2 GB | 128k212k with q8 cache | 16tok/sest. | 317 tok/s | chat |
| Ministral 3 14B Mistral AI | Q4_K_M | fits | 14.0 GB | 37k67k with q8 cache | 12tok/sest. | 176 tok/s | chat |
| Qwen3 14B Alibaba Qwen | Q4_K_M | fits | 14.7 GB | 32k40k with q8 cache | 12tok/sest. | 158 tok/s | chat |
| DeepSeek R1 0528 Qwen3 8B DeepSeek | Q8_0 | fits | 13.9 GB | 38k69k with q8 cache | 13tok/sest. | 261 tok/s | chat |
| Qwen3 8B Alibaba Qwen | Q8_0 | fits | 13.9 GB | 38k40k with q8 cache | 13tok/sest. | 261 tok/s | chat |
| Qwen 3.5 4B Alibaba Qwen | Q8_0 | fits | 6.5 GB | 255k256k with q8 cache | 30tok/sest. | 593 tok/s | chat, coding, long documents |
| Granite 4.2 8B IBM | Q6_K | fits | 13.3 GB | 41k75k with q8 cache | 13tok/sest. | 240 tok/s | chat |
| Ministral 3 8B Mistral AI | Q8_0 | fits | 13.9 GB | 38k69k with q8 cache | 12tok/sest. | 252 tok/s | chat |
| Gemma 4 E4B | Q8_0 | fits | 9.8 GB | 128k | 18tok/sest. | 398 tok/s | chat |
| LFM2.5 8B-A1B Liquid AI | Q8_0 | fits | 11.2 GB | 100k125k with q8 cache | 38tok/sest. | 1303 tok/s | chat, coding |
| Llama 3.1 8B Meta | Q6_K | fits | 11.4 GB | 57k104k with q8 cache | 16tok/sest. | 276 tok/s | chat |
| Qwen2.5 0.5B Alibaba Qwen | Q8_0 | fits | 2.2 GB | 32k | 181tok/sest. | 2837 tok/s | chat, coding, long documents |
| Qwen3.5 0.8B Alibaba Qwen | Q8_0 | fits | 2.4 GB | 256k | 137tok/sest. | 2665 tok/s | chat, coding, agents, long documents |
| Qwen3.5 2B Alibaba Qwen | Q5_K_M | fits | 3.0 GB | 256k | 91tok/sest. | 1271 tok/s | chat, coding, agents, long documents |
| LFM3 1.7B Liquid AI | Q8_0 | fits | 3.4 GB | 256k | 75tok/sest. | 1582 tok/s | chat, coding, agents, long documents |
| DeepSeek R1 Distill Qwen 1.5B DeepSeek | Q8_0 | fits | 3.9 GB | 128k | 60tok/sest. | 1067 tok/s | chat, coding, long documents |
| MiniCPM5 2B openbmb | Q8_0 | fits | 5.1 GB | 128k | 41tok/sest. | 637 tok/s | chat, coding, agents |
| Spark X2.5 4B XHToken | mlx-4bit | fits | 5.3 GB | 263k441k with q8 cache | 39tok/sest. | 626 tok/s | chat, coding, agents, long documents |
| LFM2.5 2.6B Liquid AI | Q8_0 | fits | 5.8 GB | 128k | 35tok/sest. | 722 tok/s | chat, coding, agents, long documents |
| LFM2.5 VL 3B Liquid AI | Q8_0 | fits | 5.8 GB | 32k | 35tok/sest. | 662 tok/s | chat, coding, agents, long documents |
| Kimi VL A3B Thinking 2506 Moonshot AI | Q4_K_M | fits | 12.1 GB | 109k128k with q8 cache | 34tok/sest. | 707 tok/s | chat, coding, long documents |
| Mellum2.1 12B A2.5B Thinking JetBrains | Q8_0 | fits | 13.8 GB | 83k125k with q8 cache | 32tok/sest. | 941 tok/s | chat, coding, agents, long documents |
| Kimi VL A3B Moonshot AI | mlx-5bit | fits | 13.3 GB | 75k123k with q8 cache | 32tok/sest. | 595 tok/s | chat, coding, agents |
| FrogNano 4B 2609 Microsoft | Q8_0 | fits | 6.6 GB | 251k256k with q8 cache | 29tok/sest. | 593 tok/s | chat, coding, long documents |
| granite vision 4.1 4b IBM | Q8_0 | fits | 7.2 GB | 122k128k with q8 cache | 27tok/sest. | 463 tok/s | chat, coding |
| granite 4.2 3b IBM | Q8_0 | fits | 7.4 GB | 119k128k with q8 cache | 26tok/sest. | 485 tok/s | chat, coding |
| Ministral 3 3B Instruct 2512 Mistral AI | Q8_0 | fits | 7.9 GB | 95k172k with q8 cache | 24tok/sest. | 463 tok/s | chat, coding |
| GLM 4 9B 0414 Z.ai | mlx-5bit | fits | 10.2 GB | 32k | 18tok/sest. | 230 tok/s | chat |
| DeepSeek R1 Distill Qwen 7B DeepSeek | Q8_0 | fits | 10.6 GB | 101k128k with q8 cache | 17tok/sest. | 312 tok/s | chat |
| GLM 4.6V Flash Z.ai | Q8_0 | fits | 11.9 GB | 97k128k with q8 cache | 15tok/sest. | 217 tok/s | chat |
| DeepSeek R1 Distill Llama 8B DeepSeek | Q8_0 | fits | 13.2 GB | 43k79k with q8 cache | 13tok/sest. | 276 tok/s | chat |
| GLM 4.1V 9B Thinking Z.ai | mlx-8bit | fits | 13.5 GB | 60k64k with q8 cache | 13tok/sest. | 217 tok/s | – |
| Ministral 3 14B Reasoning 2512 Mistral AI | Q4_K_M | fits | 14.0 GB | 37k67k with q8 cache | 12tok/sest. | 176 tok/s | chat |
| Ministral 3 8B Reasoning 2512 Mistral AI | Q8_0 | fits | 13.9 GB | 38k69k with q8 cache | 12tok/sest. | 252 tok/s | chat |
| Qwen 3.8 27B Alibaba Qwen | UD-Q4_K_M | offload | 18.8 GB | – | 6tok/sest. | 95 tok/s | – |
| Qwen 3.6 27B Alibaba Qwen | Q4_K_M | offload | 19.1 GB | – | 6tok/sest. | 94 tok/s | – |
| Gemma 4 31B | Q4_K_M | offload | 21.6 GB | – | 5tok/sest. | 78 tok/s | – |
| Mistral Small 4 119B Mistral AI | UD-Q2_K_XL | offload | 39.4 GB | – | 14tok/sest. | 175 tok/s | chat |
| Qwen 3.6 35B-A3B Alibaba Qwen | MXFP4 | offload | 22.2 GB | – | 23tok/sest. | 617 tok/s | chat, coding |
| Qwen 3.5 27B Alibaba Qwen | Q4_K_M | offload | 19.0 GB | – | 6tok/sest. | 94 tok/s | – |
| Gemma 4 26B-A4B | MXFP4 | offload | 17.5 GB | – | 22tok/sest. | 627 tok/s | chat, coding |
| Qwen 3.5 35B-A3B Alibaba Qwen | Q4_K_M | offload | 22.5 GB | – | 22tok/sest. | 613 tok/s | chat, coding |
| GLM 4.7 Flash Z.ai | Q4_K_M | offload | 20.0 GB | – | 13tok/sest. | 341 tok/s | chat |
| Devstral Small 2 24B Mistral AI | Q4_K_M | offload | 19.6 GB | 2k3k with q8 cache | 6tok/sest. | 97 tok/s | – |
| Nemotron 3.5 Lightning 30B-A3B NVIDIA | MXFP4 | offload | 23.1 GB | – | 23tok/sest. | 689 tok/s | chat, coding, long documents |
| Kimi Linear 48B-A3B Moonshot AI | Q4_K_M | offload | 29.6 GB | – | 22tok/sest. | 464 tok/s | chat, coding |
| Qwen3 Coder 30B-A3B Alibaba Qwen | Q4_K_M | offload | 21.6 GB | – | 12tok/sest. | 288 tok/s | chat |
| Qwen3 32B Alibaba Qwen | Q4_K_M | offload | 27.7 GB | – | 3tok/sest. | 48 tok/s | – |
| Gemma 3 27B | Q4_K_M | offload | 19.6 GB | – | 6tok/sest. | 98 tok/s | – |
| Mistral Small 3.2 24B Mistral AI | Q4_K_M | offload | 19.6 GB | 2k3k with q8 cache | 6tok/sest. | 97 tok/s | – |
| DeepSeek R1 Distill Qwen 32B DeepSeek | Q4_K_M | offload | 27.8 GB | – | 3tok/sest. | 55 tok/s | – |
| Phi-4 14B Microsoft | Q4_K_M | offload | 15.8 GB | 16k | 9tok/sest. | 152 tok/s | chat |
| LFM2.5 24B A2B Liquid AI | Q4_K_M | offload | 18.1 GB | – | 18tok/sest. | 641 tok/s | chat |
| DeepSeek R1 Distill Qwen 14B DeepSeek | Q4_K_M | offload | 15.7 GB | 27k50k with q8 cache | 10tok/sest. | 144 tok/s | chat |
| Muse Glimmer 30B meta-models | Q4_K_M | offload | 17.9 GB | – | 7tok/sest. | 94 tok/s | – |
| Devstral Small 2507 Mistral AI | Q4_K_M | offload | 19.6 GB | 2k3k with q8 cache | 6tok/sest. | 99 tok/s | – |
| Magistral Small 2506 Mistral AI | Q4_K_M | offload | 19.6 GB | 2k3k with q8 cache | 6tok/sest. | 99 tok/s | – |
| Magistral Small 2507 Mistral AI | Q4_K_M | offload | 19.6 GB | 2k3k with q8 cache | 6tok/sest. | 99 tok/s | – |
| Magistral Small 2509 Mistral AI | Q4_K_M | offload | 19.6 GB | 2k3k with q8 cache | 6tok/sest. | 97 tok/s | – |
| Qwen3.8 27B OBLITERATED OBLITERATUS | mlx-4bit | offload | 22.7 GB | – | 4tok/sest. | 86 tok/s | – |
| granite 4.2 30b IBM | Q4_K_M | offload | 26.1 GB | – | 3tok/sest. | 65 tok/s | – |
| DeepSeek V4 Flash DeepSeek | UD-Q2_K_XL | won't run | 94.2 GB | – | – | – | – |
| GLM 5.3 Flash Z.ai | UD-Q2_K_XL | won't run | 103.0 GB | – | – | – | – |
| Qwen 3.8 Flash Next Alibaba Qwen | UD-Q2_K_XL | won't run | 75.6 GB | – | – | – | – |
| MiniMax M2.7 MiniMax | UD-Q2_K_XL | won't run | 79.2 GB | – | – | – | – |
| Qwen 3.5 122B-A10B Alibaba Qwen | UD-Q2_K_XL | won't run | 41.1 GB | – | – | – | – |
| Nemotron 3 Super 120B-A12B NVIDIA | UD-Q2_K_XL | won't run | 52.6 GB | – | – | – | – |
| Qwen3 Coder Next Alibaba Qwen | Q4_K_M | won't run | 47.3 GB | – | – | – | – |
| gpt-oss 120B OpenAI | Q4_K_M | won't run | 60.9 GB | – | – | – | – |
| GLM 4.5 Air Z.ai | Q2_K | won't run | 49.2 GB | – | – | – | – |
| Qwen3 Next 80B-A3B Alibaba Qwen | Q4_K_M | won't run | 47.3 GB | – | – | – | – |
| Llama 3.3 70B Meta | Q4_K_M | won't run | 50.9 GB | – | – | – | – |
| DeepSeek R1 DeepSeek | Q4_K_M | won't run | 380.1 GB | – | – | – | – |
| DeepSeek R1 0528 DeepSeek | Q4_K_M | won't run | 380.6 GB | – | – | – | – |
| DeepSeek R1 Distill Llama 70B DeepSeek | Q4_K_M | won't run | 50.9 GB | – | – | – | – |
| DeepSeek R1 Zero DeepSeek | Q4_K_M | won't run | 380.1 GB | – | – | – | – |
| DeepSeek V3 DeepSeek | Q4_K_M | won't run | 380.1 GB | – | – | – | – |
| DeepSeek V3 0324 DeepSeek | Q4_K_M | won't run | 380.1 GB | – | – | – | – |
| DeepSeek V3.1 DeepSeek | Q4_K_M | won't run | 381.0 GB | – | – | – | – |
| DeepSeek V3.1 Terminus DeepSeek | Q4_K_M | won't run | 381.0 GB | – | – | – | – |
| DeepSeek V3.2 DeepSeek | Q4_K_M | won't run | 381.0 GB | – | – | – | – |
| DeepSeek V4 Flash 0731 DeepSeek | MXFP4 | won't run | 149.6 GB | – | – | – | – |
| DeepSeek V4 Flash Vision Exp DeepSeek | MXFP4 | won't run | 158.4 GB | – | – | – | – |
| Devstral 2 123B Instruct 2512 Mistral AI | Q4_K_M | won't run | 82.0 GB | – | – | – | – |
| GLM 4.5 Z.ai | Q4_K_M | won't run | 214.4 GB | – | – | – | – |
| GLM 4.5V Z.ai | Q4_K_M | won't run | 66.3 GB | – | – | – | – |
| GLM 4.6 Z.ai | Q4_K_M | won't run | 213.6 GB | – | – | – | – |
| GLM 4.6V Z.ai | Q4_K_M | won't run | 72.6 GB | – | – | – | – |
| GLM 4.7 Z.ai | Q4_K_M | won't run | 214.4 GB | – | – | – | – |
| GLM 5 Z.ai | MXFP4 | won't run | 386.4 GB | – | – | – | – |
| GLM 5.1 Z.ai | MXFP4 | won't run | 424.0 GB | – | – | – | – |
| GLM 5.2 Z.ai | UD-Q4_K_M | won't run | 437.9 GB | – | – | – | – |
| GLM 5.3 Z.ai | Q8_0 | won't run | 750.4 GB | – | – | – | – |
| Kimi Dev 72B Moonshot AI | Q6_K | won't run | 71.2 GB | – | – | – | – |
| Kimi K2 Moonshot AI | Q4_K_M | won't run | 581.6 GB | – | – | – | – |
| Kimi K2 Instruct 0905 Moonshot AI | Q4_K_M | won't run | 582.0 GB | – | – | – | – |
| Kimi K2 Thinking Moonshot AI | Q4_K_M | won't run | 582.0 GB | – | – | – | – |
| Kimi K2.5 Moonshot AI | Q4_K_M | won't run | 582.0 GB | – | – | – | – |
| MiniMax M2 MiniMax | Q4_K_M | won't run | 137.9 GB | – | – | – | – |
| MiniMax M2.1 MiniMax | Q4_K_M | won't run | 137.9 GB | – | – | – | – |
| MiniMax M2.5 MiniMax | MXFP4 | won't run | 124.3 GB | – | – | – | – |
| MiniMax M3 MiniMax | MXFP4 | won't run | 244.7 GB | – | – | – | – |
| Mistral Medium 3.5 128B Mistral AI | Q4_K_M | won't run | 82.0 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 32k context and generates at about 31 tok/s, an estimate from its memory bandwidth. 36 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 94 GB at 32k 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 32k context. See the best hardware for local AI at every budget.