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 NVIDIA GeForce RTX 4060 is not on sale new in the UK right now; used, it starts at £290 (asking price).
Updated . Speeds are estimates; how they are worked out.
Prices
Every retailer we track in the UK, cheapest first. Buy links may earn us a commission.
| Retailer | Condition | Price | Stock | Checked | Buy |
|---|---|---|---|---|---|
| eBayASUS RTX 4060 DUAL 8GB Graphics Card | used, asking price | £290+ £7 delivery | In stock | 3 h ago | Buy |
| eBayUsed GeForce RTX 4060 on eBay | used, asking price | £308 | In stock | 6 h ago | Buy |
| eBayGigabyte RTX 4060 AERO OC 8GB White Graphics Card | used, asking price | £323+ £6 delivery | In stock | 3 h ago | Buy |
| eBayGigabyte NVIDIA GeForce RTX 4060 Eagle OC Graphics Card | used, asking price | £323+ £7 delivery | In stock | 3 h ago | Buy |
Used on eBay: median asking price £308, most between £296 and £323 (10 listings, 2026-11-03). Used price history
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 the UK?
- The NVIDIA GeForce RTX 4060 is not on sale new in the UK right now; used, it starts at £290 (asking price).
Speeds on this page are for 32k context. See the best hardware for local AI at every budget.