NVIDIA GeForce RTX 4070
12 GB · 504.2 GB/s · 200 W
In short
The NVIDIA GeForce RTX 4070 has 12 GB of GDDR6X memory with 504.2 GB/s of bandwidth, which sets how fast it generates text. At 32k context it runs 56 of the 125 open models we track fully in memory; the most capable is Qwen 3.8 27B at UD-Q5_K_M, at about 14 tok/s (estimated). The best new price for the NVIDIA GeForce RTX 4070 in Canada is $1,230 at Amazon.
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 |
|---|---|---|---|---|---|
| AmazonASUS Dual GeForce RTX 4070 OC Edition 12GB GDDR6X | New | $1,230 | In stock | 2 h ago | Buy |
| AmazonASUS Dual GeForce RTX 4070 EVO OC Edition 12GB GDDR6X is Designed for Broad… | New | $1,730+ $6 delivery | In stock | 2 h ago | Buy |
| AmazonASUS ROG Strix GeForce RTX 4070 OC Edition Gaming Graphics Card | New | $1,889+ $6 delivery | In stock | 2 h ago | Buy |
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 |
|---|---|---|---|---|---|---|---|
| Qwen 3.8 27B Alibaba Qwen | UD-Q5_K_M | fits | 21.8 GB | 46k80k with q8 cache | 14tok/sest. | 212 tok/s | chat |
| Qwen 3.6 27B Alibaba Qwen | Q5_K_M | fits | 21.6 GB | 49k86k with q8 cache | 14tok/sest. | 212 tok/s | chat |
| Gemma 4 31B | Q4_K_M | fits | 21.6 GB | 45k89k with q8 cache | 14tok/sest. | 189 tok/s | chat |
| Qwen 3.6 35B-A3B Alibaba Qwen | Q4_K_M | fits | 22.7 GB | 34k54k with q8 cache | 72tok/sest. | 1510 tok/s | chat, coding, agents, long documents |
| Qwen 3.5 27B Alibaba Qwen | Q5_K_M | fits | 21.7 GB | 48k84k with q8 cache | 14tok/sest. | 212 tok/s | chat |
| Gemma 4 26B-A4B | UD-Q5_K_M | fits | 21.8 GB | 71k116k with q8 cache | 45tok/sest. | 1344 tok/s | chat, coding, agents, long documents |
| Qwen 3.5 35B-A3B Alibaba Qwen | Q4_K_M | fits | 22.5 GB | 44k70k with q8 cache | 73tok/sest. | 1510 tok/s | chat, coding, agents, long documents |
| GLM 4.7 Flash Z.ai | Q4_K_M | fits | 20.0 GB | 80k139k with q8 cache | 39tok/sest. | 789 tok/s | chat, coding, agents |
| Devstral Small 2 24B Mistral AI | Q5_K_M | fits | 21.9 GB | 37k68k with q8 cache | 14tok/sest. | 223 tok/s | chat |
| gpt-oss 20B OpenAI | MXFP4 | fits | 13.3 GB | 128k | 58tok/sest. | 1128 tok/s | chat, coding, agents, long documents |
| Qwen3 Coder 30B-A3B Alibaba Qwen | Q4_K_M | fits | 21.6 GB | 44k79k with q8 cache | 37tok/sest. | 695 tok/s | chat, coding, agents |
| Gemma 4 12B | Q8_0 | fits | 13.9 GB | 256k | 23tok/sest. | 491 tok/s | chat, coding, long documents |
| Qwen 3.5 9B Alibaba Qwen | Q8_0 | fits | 11.2 GB | 256k | 29tok/sest. | 611 tok/s | chat, coding, long documents |
| Mistral Small 3.2 24B Mistral AI | Q5_K_M | fits | 21.9 GB | 37k68k with q8 cache | 14tok/sest. | 223 tok/s | chat |
| Gemma 3 27B | Q5_K_M | fits | 22.1 GB | 39k74k with q8 cache | 14tok/sest. | 225 tok/s | chat |
| Ministral 3 14B Mistral AI | Q8_0 | fits | 19.7 GB | 51k93k with q8 cache | 16tok/sest. | 340 tok/s | chat |
| Qwen3 14B Alibaba Qwen | Q8_0 | fits | 20.9 GB | 40k | 15tok/sest. | 305 tok/s | chat |
| DeepSeek R1 0528 Qwen3 8B DeepSeek | Q8_0 | fits | 13.9 GB | 92k128k with q8 cache | 23tok/sest. | 504 tok/s | chat, coding |
| Qwen3 8B Alibaba Qwen | Q8_0 | fits | 13.9 GB | 40k | 23tok/sest. | 504 tok/s | chat, coding |
| Phi-4 14B Microsoft | Q8_0 | fits | 22.0 GB | 16k | 14tok/sest. | 307 tok/s | chat |
| Qwen 3.5 4B Alibaba Qwen | Q8_0 | fits | 6.5 GB | 256k | 56tok/sest. | 1144 tok/s | chat, coding, long documents |
| Ministral 3 8B Mistral AI | Q8_0 | fits | 13.9 GB | 95k173k with q8 cache | 23tok/sest. | 486 tok/s | chat, coding |
| Granite 4.2 8B IBM | Q8_0 | fits | 15.0 GB | 80k128k with q8 cache | 21tok/sest. | 463 tok/s | chat, coding |
| Gemma 4 E4B | Q8_0 | fits | 9.8 GB | 128k | 34tok/sest. | 768 tok/s | chat, coding, long documents |
| LFM2.5 8B-A1B Liquid AI | Q8_0 | fits | 11.2 GB | 125k | 70tok/sest. | 2512 tok/s | chat, coding, long documents |
| Llama 3.1 8B Meta | Q6_K | fits | 11.4 GB | 118k128k with q8 cache | 29tok/sest. | 532 tok/s | chat, coding |
| Qwen2.5 0.5B Alibaba Qwen | Q8_0 | fits | 2.2 GB | 32k | 335tok/sest. | 5471 tok/s | chat, coding, long documents |
| Qwen3.5 0.8B Alibaba Qwen | Q8_0 | fits | 2.4 GB | 256k | 254tok/sest. | 5139 tok/s | chat, coding, agents, long documents |
| Qwen3.5 2B Alibaba Qwen | Q5_K_M | fits | 3.0 GB | 256k | 168tok/sest. | 2450 tok/s | chat, coding, agents, long documents |
| DeepSeek R1 Distill Qwen 1.5B DeepSeek | Q8_0 | fits | 3.9 GB | 128k | 110tok/sest. | 2058 tok/s | chat, coding, long documents |
| MiniCPM5 2B openbmb | Q8_0 | fits | 5.1 GB | 128k | 77tok/sest. | 1229 tok/s | chat, coding, agents, long documents |
| Spark X2.5 4B XHToken | mlx-4bit | fits | 5.3 GB | 458k767k with q8 cache | 73tok/sest. | 1207 tok/s | chat, coding, agents, long documents |
| LFM2.5 2.6B Liquid AI | Q8_0 | fits | 5.8 GB | 128k | 64tok/sest. | 1392 tok/s | chat, coding, agents, long documents |
| LFM2.5 VL 3B Liquid AI | Q8_0 | fits | 5.8 GB | 32k | 64tok/sest. | 1277 tok/s | chat, coding, agents, long documents |
| Mellum2.1 12B A2.5B Thinking JetBrains | Q8_0 | fits | 13.8 GB | 128k | 59tok/sest. | 1814 tok/s | chat, coding, agents, long documents |
| FrogNano 4B 2609 Microsoft | Q8_0 | fits | 6.6 GB | 256k | 54tok/sest. | 1144 tok/s | chat, coding, agents, long documents |
| granite vision 4.1 4b IBM | Q8_0 | fits | 7.2 GB | 128k | 50tok/sest. | 892 tok/s | chat, coding, long documents |
| granite 4.2 3b IBM | Q8_0 | fits | 7.4 GB | 128k | 48tok/sest. | 935 tok/s | chat, coding, agents, long documents |
| Kimi VL A3B Thinking 2506 Moonshot AI | Q8_0 | fits | 18.0 GB | 128k | 46tok/sest. | 1363 tok/s | chat, coding, long documents |
| LFM2.5 24B A2B Liquid AI | Q5_K_M | fits | 21.0 GB | 53k94k with q8 cache | 44tok/sest. | 1400 tok/s | chat, coding, agents |
| Kimi VL A3B Moonshot AI | mlx-8bit | fits | 18.8 GB | 128k | 44tok/sest. | 1363 tok/s | chat, coding, agents, long documents |
| Ministral 3 3B Instruct 2512 Mistral AI | Q8_0 | fits | 7.9 GB | 170k256k with q8 cache | 44tok/sest. | 894 tok/s | chat, coding, agents, long documents |
| GLM 4 9B 0414 Z.ai | mlx-5bit | fits | 10.2 GB | 32k | 33tok/sest. | 444 tok/s | chat, coding |
| DeepSeek R1 Distill Qwen 7B DeepSeek | Q8_0 | fits | 10.6 GB | 128k | 31tok/sest. | 601 tok/s | chat, coding, long documents |
| GLM 4.6V Flash Z.ai | Q8_0 | fits | 11.9 GB | 128k | 28tok/sest. | 419 tok/s | chat, coding |
| DeepSeek R1 Distill Llama 8B DeepSeek | Q8_0 | fits | 13.2 GB | 105k128k with q8 cache | 24tok/sest. | 532 tok/s | chat, coding |
| GLM 4.1V 9B Thinking Z.ai | mlx-8bit | fits | 13.5 GB | 64k | 24tok/sest. | 419 tok/s | chat, coding |
| Ministral 3 8B Reasoning 2512 Mistral AI | Q8_0 | fits | 13.9 GB | 95k173k with q8 cache | 23tok/sest. | 486 tok/s | chat, coding |
| Ministral 3 14B Reasoning 2512 Mistral AI | Q8_0 | fits | 19.7 GB | 51k93k with q8 cache | 16tok/sest. | 340 tok/s | chat |
| Muse Glimmer 30B meta-models | Q5_K_M | fits | 20.5 GB | 128k | 15tok/sest. | 204 tok/s | chat |
| DeepSeek R1 Distill Qwen 14B DeepSeek | Q8_0 | fits | 21.9 GB | 36k67k with q8 cache | 14tok/sest. | 287 tok/s | chat |
| Devstral Small 2507 Mistral AI | Q5_K_M | fits | 21.9 GB | 37k68k with q8 cache | 14tok/sest. | 227 tok/s | chat |
| Magistral Small 2506 Mistral AI | Q5_K_M | fits | 21.9 GB | 37k40k with q8 cache | 14tok/sest. | 227 tok/s | chat |
| Magistral Small 2507 Mistral AI | Q5_K_M | fits | 21.9 GB | 37k40k with q8 cache | 14tok/sest. | 227 tok/s | chat |
| Magistral Small 2509 Mistral AI | Q5_K_M | fits | 21.9 GB | 37k68k with q8 cache | 14tok/sest. | 223 tok/s | chat |
| Qwen3.8 27B OBLITERATED OBLITERATUS | mlx-4bit | fits | 22.7 GB | 33k59k with q8 cache | 14tok/sest. | 212 tok/s | – |
| Qwen 3.5 122B-A10B Alibaba Qwen | UD-Q2_K_XL | offload | 41.1 GB | – | 14tok/sest. | 359 tok/s | chat |
| Qwen3 Coder Next Alibaba Qwen | Q4_K_M | offload | 47.3 GB | – | 16tok/sest. | 814 tok/s | chat |
| Mistral Small 4 119B Mistral AI | UD-Q2_K_XL | offload | 39.4 GB | – | 19tok/sest. | 407 tok/s | chat |
| Qwen3 Next 80B-A3B Alibaba Qwen | Q4_K_M | offload | 47.3 GB | – | 20tok/sest. | 968 tok/s | chat |
| Nemotron 3.5 Lightning 30B-A3B NVIDIA | MXFP4 | offload | 23.1 GB | 3k4k with q8 cache | 71tok/sest. | 1707 tok/s | chat, coding, agents, long documents |
| Kimi Linear 48B-A3B Moonshot AI | Q4_K_M | offload | 29.6 GB | – | 41tok/sest. | 1115 tok/s | chat, coding, long documents |
| Qwen3 32B Alibaba Qwen | Q4_K_M | offload | 27.7 GB | 12k23k with q8 cache | 6tok/sest. | 117 tok/s | – |
| DeepSeek R1 Distill Qwen 32B DeepSeek | Q4_K_M | offload | 27.8 GB | 12k23k with q8 cache | 6tok/sest. | 134 tok/s | – |
| granite 4.2 30b IBM | Q4_K_M | offload | 26.1 GB | 19k35k with q8 cache | 7tok/sest. | 159 tok/s | chat |
| 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 | – | – | – | – |
| Nemotron 3 Super 120B-A12B NVIDIA | UD-Q2_K_XL | won't run | 52.6 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 | – | – | – | – |
| 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 4070
- Memory
- 12 GB GDDR6X
- Memory bus
- 192-bit
- Memory bandwidth
- 504.2 GB/s
- FP16 compute
- 29.146 TFLOPS
- Software
- CUDA
- Board power
- 200 W
- PCIe generation
- Gen 4
- PCIe lanes
- x16
Compare with
Questions
- How much memory does the NVIDIA GeForce RTX 4070 have for AI models?
- The NVIDIA GeForce RTX 4070 has 12 GB of GDDR6X memory with 504.2 GB/s of bandwidth, which sets how fast it generates text.
- What is the best model the NVIDIA GeForce RTX 4070 can run locally?
- Qwen 3.8 27B (UD-Q5_K_M) fits fully in its memory at 32k context and generates at about 14 tok/s, an estimate from its memory bandwidth. 56 of the 125 models we track fit at that context.
- Can the NVIDIA GeForce RTX 4070 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 4070 and system RAM can hold.
- How much does the NVIDIA GeForce RTX 4070 cost in Canada?
- The best new price for the NVIDIA GeForce RTX 4070 in Canada is $1,230 at Amazon.
Speeds on this page are for 32k context. See the best hardware for local AI at every budget.