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 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 25 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 @8k | Prefill | Good for |
|---|---|---|---|---|---|---|---|
| Qwen 3.5 9B Alibaba Qwen | Q5_K_M | fits | 7.2 GB | 14k23k with q8 cache | 25tok/sest. | 343 tok/s | chat |
| DeepSeek R1 0528 Qwen3 8B DeepSeek | Q5_K_M | fits | 7.3 GB | 8k15k with q8 cache | 24tok/sest. | 362 tok/s | chat |
| Qwen3 8B Alibaba Qwen | Q5_K_M | fits | 7.3 GB | 8k15k with q8 cache | 24tok/sest. | 362 tok/s | chat |
| Qwen 3.5 4B Alibaba Qwen | Q8_0 | fits | 5.2 GB | 66k110k with q8 cache | 35tok/sest. | 690 tok/s | chat, coding |
| Ministral 3 8B Mistral AI | Q4_K_M | fits | 6.7 GB | 13k24k with q8 cache | 27tok/sest. | 338 tok/s | chat |
| Granite 4.2 8B IBM | Q4_K_M | fits | 7.2 GB | 9k17k with q8 cache | 25tok/sest. | 336 tok/s | chat |
| Gemma 4 E4B | Q5_K_M | fits | 6.1 GB | 46k77k with q8 cache | 29tok/sest. | 415 tok/s | chat, coding |
| LFM2.5 8B-A1B Liquid AI | UD-Q5_K_M | fits | 7.0 GB | 14k25k with q8 cache | 88tok/sest. | 2424 tok/s | chat, coding |
| Llama 3.1 8B Meta | Q5_K_M | fits | 7.1 GB | 10k18k with q8 cache | 25tok/sest. | 374 tok/s | chat |
| Qwen2.5 0.5B Alibaba Qwen | Q8_0 | fits | 1.3 GB | 32k | 267tok/sest. | 5074 tok/s | chat, coding, long documents |
| Qwen3.5 0.8B Alibaba Qwen | Q8_0 | fits | 1.6 GB | 256k | 181tok/sest. | 3491 tok/s | chat, coding, agents, long documents |
| Qwen3.5 2B Alibaba Qwen | Q5_K_M | fits | 2.2 GB | 256k | 109tok/sest. | 1432 tok/s | chat, coding, agents, long documents |
| LFM3 1.7B Liquid AI | Q8_0 | fits | 2.6 GB | 256k | 86tok/sest. | 1840 tok/s | chat, coding, agents, long documents |
| DeepSeek R1 Distill Qwen 1.5B DeepSeek | Q8_0 | fits | 2.7 GB | 128k | 79tok/sest. | 1597 tok/s | chat, coding, long documents |
| MiniCPM5 2B openbmb | Q8_0 | fits | 3.6 GB | 89k128k with q8 cache | 56tok/sest. | 1056 tok/s | chat, coding, agents |
| LFM2.5 2.6B Liquid AI | Q8_0 | fits | 3.9 GB | 62k108k with q8 cache | 50tok/sest. | 1063 tok/s | chat, coding, agents |
| LFM2.5 VL 3B Liquid AI | Q8_0 | fits | 3.9 GB | 32k | 50tok/sest. | 938 tok/s | chat, coding, agents |
| Spark X2.5 4B XHToken | mlx-4bit | fits | 3.9 GB | 93k156k with q8 cache | 50tok/sest. | 751 tok/s | chat, coding, agents, long documents |
| granite vision 4.1 4b IBM | Q8_0 | fits | 4.7 GB | 39k70k with q8 cache | 39tok/sest. | 703 tok/s | chat, coding |
| Ministral 3 3B Instruct 2512 Mistral AI | Q8_0 | fits | 5.0 GB | 30k55k with q8 cache | 37tok/sest. | 720 tok/s | chat, coding |
| granite 4.2 3b IBM | Q8_0 | fits | 5.0 GB | 36k64k with q8 cache | 37tok/sest. | 756 tok/s | chat, coding |
| FrogNano 4B 2609 Microsoft | Q8_0 | fits | 5.3 GB | 63k104k with q8 cache | 34tok/sest. | 690 tok/s | chat, coding |
| Ministral 3 8B Reasoning 2512 Mistral AI | Q4_K_M | fits | 6.7 GB | 13k24k with q8 cache | 27tok/sest. | 338 tok/s | chat |
| GLM 4.6V Flash Z.ai | Q4_K_M | fits | 6.8 GB | 21k35k with q8 cache | 26tok/sest. | 292 tok/s | chat |
| DeepSeek R1 Distill Qwen 7B DeepSeek | Q6_K | fits | 7.0 GB | 14k25k with q8 cache | 25tok/sest. | 403 tok/s | chat |
| DeepSeek R1 Distill Llama 8B DeepSeek | Q5_K_M | fits | 7.1 GB | 10k18k with q8 cache | 25tok/sest. | 374 tok/s | chat |
| Qwen 3.8 27B Alibaba Qwen | UD-Q4_K_M | offload | 16.7 GB | – | 4tok/sest. | 77 tok/s | – |
| Qwen 3.6 27B Alibaba Qwen | Q4_K_M | offload | 17.0 GB | – | 4tok/sest. | 77 tok/s | – |
| Gemma 4 31B | Q4_K_M | offload | 19.2 GB | – | 4tok/sest. | 64 tok/s | – |
| Qwen 3.6 35B-A3B Alibaba Qwen | MXFP4 | offload | 21.2 GB | – | 20tok/sest. | 607 tok/s | chat |
| Qwen 3.5 27B Alibaba Qwen | Q4_K_M | offload | 17.0 GB | – | 4tok/sest. | 77 tok/s | – |
| Gemma 4 26B-A4B | MXFP4 | offload | 16.5 GB | – | 15tok/sest. | 507 tok/s | chat |
| Qwen 3.5 35B-A3B Alibaba Qwen | Q4_K_M | offload | 21.5 GB | – | 20tok/sest. | 604 tok/s | chat |
| GLM 4.7 Flash Z.ai | Q4_K_M | offload | 18.2 GB | – | 13tok/sest. | 359 tok/s | – |
| Devstral Small 2 24B Mistral AI | Q4_K_M | offload | 15.3 GB | – | 5tok/sest. | 90 tok/s | – |
| Nemotron 3.5 Lightning 30B-A3B NVIDIA | MXFP4 | offload | 22.5 GB | – | 18tok/sest. | 620 tok/s | chat |
| gpt-oss 20B OpenAI | Q4_K_M | offload | 11.8 GB | – | 22tok/sest. | 563 tok/s | chat |
| Kimi Linear 48B-A3B Moonshot AI | Q4_K_M | offload | 28.8 GB | – | 20tok/sest. | 525 tok/s | chat |
| Qwen3 Coder 30B-A3B Alibaba Qwen | Q4_K_M | offload | 18.8 GB | – | 16tok/sest. | 458 tok/s | chat |
| Qwen3 32B Alibaba Qwen | Q4_K_M | offload | 21.2 GB | – | 3tok/sest. | 55 tok/s | – |
| Gemma 4 12B | Q4_K_M | offload | 7.8 GB | – | 20tok/sest. | 261 tok/s | chat |
| Mistral Small 3.2 24B Mistral AI | Q4_K_M | offload | 15.3 GB | – | 5tok/sest. | 90 tok/s | – |
| Gemma 3 27B | Q4_K_M | offload | 17.2 GB | – | 4tok/sest. | 78 tok/s | – |
| DeepSeek R1 Distill Qwen 32B DeepSeek | Q4_K_M | offload | 21.2 GB | – | 3tok/sest. | 57 tok/s | – |
| Ministral 3 14B Mistral AI | Q4_K_M | offload | 9.7 GB | – | 11tok/sest. | 189 tok/s | – |
| Qwen3 14B Alibaba Qwen | Q4_K_M | offload | 10.4 GB | – | 10tok/sest. | 168 tok/s | – |
| Phi-4 14B Microsoft | Q4_K_M | offload | 10.6 GB | – | 9tok/sest. | 168 tok/s | – |
| Mellum2.1 12B A2.5B Thinking JetBrains | Q4_K_M | offload | 9.1 GB | – | 36tok/sest. | 1025 tok/s | chat, coding |
| Kimi VL A3B Thinking 2506 Moonshot AI | Q4_K_M | offload | 10.8 GB | – | 27tok/sest. | 785 tok/s | chat |
| LFM2.5 24B A2B Liquid AI | Q4_K_M | offload | 15.7 GB | – | 24tok/sest. | 850 tok/s | chat |
| Kimi VL A3B Moonshot AI | mlx-5bit | offload | 12.0 GB | – | 22tok/sest. | 697 tok/s | chat |
| GLM 4 9B 0414 Z.ai | mlx-4bit | offload | 7.6 GB | 2k4k with q8 cache | 21tok/sest. | 310 tok/s | chat |
| Ministral 3 14B Reasoning 2512 Mistral AI | Q4_K_M | offload | 9.7 GB | – | 11tok/sest. | 189 tok/s | – |
| DeepSeek R1 Distill Qwen 14B DeepSeek | Q4_K_M | offload | 10.6 GB | – | 9tok/sest. | 163 tok/s | – |
| GLM 4.1V 9B Thinking Z.ai | mlx-8bit | offload | 12.0 GB | – | 7tok/sest. | 220 tok/s | – |
| Devstral Small 2507 Mistral AI | Q4_K_M | offload | 15.3 GB | – | 5tok/sest. | 92 tok/s | – |
| Magistral Small 2506 Mistral AI | Q4_K_M | offload | 15.3 GB | – | 5tok/sest. | 92 tok/s | – |
| Magistral Small 2507 Mistral AI | Q4_K_M | offload | 15.3 GB | – | 5tok/sest. | 92 tok/s | – |
| Magistral Small 2509 Mistral AI | Q4_K_M | offload | 15.3 GB | – | 5tok/sest. | 90 tok/s | – |
| Muse Glimmer 30B meta-models | Q4_K_M | offload | 17.0 GB | – | 4tok/sest. | 71 tok/s | – |
| granite 4.2 30b IBM | Q4_K_M | offload | 19.5 GB | – | 4tok/sest. | 66 tok/s | – |
| Qwen3.8 27B OBLITERATED OBLITERATUS | mlx-4bit | offload | 20.6 GB | – | 3tok/sest. | 72 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 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?
- Qwen 3.5 9B (Q5_K_M) fits fully in its memory at 8k context and generates at about 25 tok/s, an estimate from its memory bandwidth. 26 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.