NVIDIA GeForce RTX 3080 10GB
10 GB · 760 GB/s · 320 W
Prices
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No retailer in this market lists it right now.
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 @64k | Prefill | Good for |
|---|---|---|---|---|---|---|---|
| Gemma 4 26B-A4B | UD-Q4_K_M | fits | 18.7 GB | 67k111k with q8 cache | 68tok/sest. | 2411 tok/s | chat, coding, agents, long documents |
| gpt-oss 20B OpenAI | MXFP4 | fits | 14.3 GB | 128k | 70tok/sest. | 1803 tok/s | chat, coding, agents, long documents |
| Gemma 4 12B | Q8_0 | fits | 14.6 GB | 256k | 34tok/sest. | 927 tok/s | chat, coding, long documents |
| Qwen 3.5 9B Alibaba Qwen | Q8_0 | fits | 12.4 GB | 236k256k with q8 cache | 40tok/sest. | 1135 tok/s | chat, coding, long documents |
| DeepSeek R1 0528 Qwen3 8B DeepSeek | Q8_0 | fits | 18.6 GB | 65k119k with q8 cache | 26tok/sest. | 750 tok/s | chat, coding |
| Qwen3 8B Alibaba Qwen | Q8_0 | fits | 18.6 GB | 40k | 26tok/sest. | 750 tok/s | chat, coding |
| Qwen 3.5 4B Alibaba Qwen | Q8_0 | fits | 7.7 GB | 256k | 71tok/sest. | 1967 tok/s | chat, coding, long documents |
| Ministral 3 8B Mistral AI | Q8_0 | fits | 18.4 GB | 67k121k with q8 cache | 26tok/sest. | 742 tok/s | chat, coding |
| Granite 4.2 8B IBM | Q6_K | fits | 18.5 GB | 65k120k with q8 cache | 26tok/sest. | 686 tok/s | chat, coding, agents |
| Gemma 4 E4B | Q8_0 | fits | 10.9 GB | 128k | 47tok/sest. | 1486 tok/s | chat, coding, long documents |
| LFM2.5 8B-A1B Liquid AI | Q8_0 | fits | 12.9 GB | 125k | 66tok/sest. | 3172 tok/s | chat, coding, long documents |
| Llama 3.1 8B Meta | Q6_K | fits | 15.6 GB | 88k128k with q8 cache | 31tok/sest. | 806 tok/s | chat, coding |
| Qwen 3.8 27B Alibaba Qwen | UD-Q4_K_M | offload | 20.9 GB | 32k56k with q8 cache | 12tok/sest. | 365 tok/s | chat, coding |
| Qwen 3.5 122B-A10B Alibaba Qwen | UD-Q2_K_XL | offload | 42.1 GB | – | 10tok/sest. | 534 tok/s | – |
| Qwen 3.6 27B Alibaba Qwen | Q4_K_M | offload | 21.3 GB | 27k48k with q8 cache | 11tok/sest. | 362 tok/s | chat, coding |
| Gemma 4 31B | Q4_K_M | offload | 24.3 GB | –4k with q8 cache | 7tok/sest. | 305 tok/s | chat |
| Mistral Small 4 119B Mistral AI | UD-Q2_K_XL | offload | 40.3 GB | – | 14tok/sest. | 534 tok/s | chat |
| Qwen 3.6 35B-A3B Alibaba Qwen | UD-Q4_K_M | offload | 23.4 GB | – | 33tok/sest. | 2059 tok/s | chat, coding, agents, long documents |
| Qwen 3.5 27B Alibaba Qwen | Q4_K_M | offload | 21.2 GB | 28k50k with q8 cache | 11tok/sest. | 363 tok/s | chat, coding |
| Qwen 3.5 35B-A3B Alibaba Qwen | Q4_K_M | offload | 23.3 GB | – | 34tok/sest. | 2064 tok/s | chat, coding, agents, long documents |
| GLM 4.7 Flash Z.ai | Q4_K_M | offload | 21.8 GB | 11k19k with q8 cache | 20tok/sest. | 1064 tok/s | chat, coding, agents |
| Devstral Small 2 24B Mistral AI | Q4_K_M | offload | 24.8 GB | 26k48k with q8 cache | 6tok/sest. | 326 tok/s | chat |
| Nemotron 3.5 Lightning 30B-A3B NVIDIA | UD-Q6_K_XL | offload | 34.5 GB | – | 16tok/sest. | 2076 tok/s | chat |
| Kimi Linear 48B-A3B Moonshot AI | Q4_K_M | offload | 30.0 GB | – | 29tok/sest. | 1446 tok/s | chat, coding, long documents |
| Qwen3 Coder 30B-A3B Alibaba Qwen | Q4_K_M | offload | 24.8 GB | 4k7k with q8 cache | 12tok/sest. | 713 tok/s | chat, coding |
| Qwen3 32B Alibaba Qwen | Q4_K_M | offload | 35.9 GB | – | 3tok/sest. | 140 tok/s | – |
| Gemma 3 27B | Q4_K_M | offload | 22.3 GB | 22k43k with q8 cache | 9tok/sest. | 394 tok/s | chat |
| Mistral Small 3.2 24B Mistral AI | Q4_K_M | offload | 24.8 GB | 26k48k with q8 cache | 6tok/sest. | 326 tok/s | chat |
| DeepSeek R1 Distill Qwen 32B DeepSeek | Q4_K_M | offload | 36.0 GB | – | 3tok/sest. | 173 tok/s | – |
| Ministral 3 14B Mistral AI | Q4_K_M | offload | 19.1 GB | 61k112k with q8 cache | 22tok/sest. | 536 tok/s | chat, coding |
| Qwen3 14B Alibaba Qwen | Q4_K_M | offload | 19.9 GB | 40k | 17tok/sest. | 459 tok/s | chat, coding |
| Phi-4 14B Microsoft | Q4_K_M | offload | 22.3 GB | 16k | 9tok/sest. | 429 tok/s | chat, coding |
| DeepSeek V4 Flash DeepSeek | UD-Q2_K_XL | won't run | 97.0 GB | – | – | – | – |
| GLM 5.3 Flash Z.ai | UD-Q2_K_XL | won't run | 103.5 GB | – | – | – | – |
| Qwen 3.8 Flash Next Alibaba Qwen | UD-Q2_K_XL | won't run | 76.5 GB | – | – | – | – |
| MiniMax M2.7 MiniMax | UD-Q2_K_XL | won't run | 87.1 GB | – | – | – | – |
| Nemotron 3 Super 120B-A12B NVIDIA | UD-Q2_K_XL | won't run | 53.0 GB | – | – | – | – |
| Qwen3 Coder Next Alibaba Qwen | Q4_K_M | won't run | 48.2 GB | – | – | – | – |
| gpt-oss 120B OpenAI | MXFP4 | won't run | 62.8 GB | – | – | – | – |
| GLM 4.5 Air Z.ai | Q2_K | won't run | 55.2 GB | – | – | – | – |
| Qwen3 Next 80B-A3B Alibaba Qwen | Q4_K_M | won't run | 48.2 GB | – | – | – | – |
| Llama 3.3 70B Meta | Q4_K_M | won't run | 61.1 GB | – | – | – | – |
Specifications
From the manufacturer's published specifications; sources are linked where we have them.
- GPU
- GeForce RTX 3080 10GB
- Memory
- 10 GB GDDR6X
- Memory bus
- 320-bit
- Memory bandwidth
- 760 GB/s
- FP16 compute
- 59.5 TFLOPS
- Software
- CUDA
- Board power
- 320 W
- PCIe generation
- Gen 4
- PCIe lanes
- x16