NVIDIA GeForce RTX 4090
24 GB · 1,008 GB/s · 450 W
Prices
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No retailer in this market lists it right now.
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 @32k | Prefill | Good for |
|---|---|---|---|---|---|---|---|
| Qwen 3.8 27B Alibaba Qwen | UD-Q5_K_M | fits | 21.4 GB | 60k106k with q8 cache | 28tok/sest. | 1198 tok/s | chat, coding |
| Qwen 3.6 27B Alibaba Qwen | Q5_K_M | fits | 21.2 GB | 64k112k with q8 cache | 29tok/sest. | 1198 tok/s | chat, coding |
| Gemma 4 31B | Q4_K_M | fits | 21.2 GB | 57k110k with q8 cache | 29tok/sest. | 1067 tok/s | chat, coding |
| Qwen 3.6 35B-A3B Alibaba Qwen | UD-Q4_K_M | fits | 22.2 GB | 79k124k with q8 cache | 145tok/sest. | 8550 tok/s | chat, coding, agents, long documents |
| Qwen 3.5 27B Alibaba Qwen | Q5_K_M | fits | 21.3 GB | 62k110k with q8 cache | 29tok/sest. | 1198 tok/s | chat, coding |
| Gemma 4 26B-A4B | UD-Q6_K | fits | 23.3 GB | 36k62k with q8 cache | 84tok/sest. | 7606 tok/s | chat, coding, agents, long documents |
| Qwen 3.5 35B-A3B Alibaba Qwen | Q4_K_M | fits | 22.1 GB | 84k131k with q8 cache | 146tok/sest. | 8550 tok/s | chat, coding, agents, long documents |
| GLM 4.7 Flash Z.ai | Q5_K_M | fits | 22.5 GB | 48k82k with q8 cache | 70tok/sest. | 4464 tok/s | chat, coding, agents |
| Devstral Small 2 24B Mistral AI | Q5_K_M | fits | 21.5 GB | 43k79k with q8 cache | 28tok/sest. | 1264 tok/s | chat, coding |
| gpt-oss 20B OpenAI | MXFP4 | fits | 12.9 GB | 128k | 116tok/sest. | 6383 tok/s | chat, coding, agents, long documents |
| Qwen3 Coder 30B-A3B Alibaba Qwen | Q4_K_M | fits | 21.2 GB | 54k97k with q8 cache | 74tok/sest. | 3932 tok/s | chat, coding, agents |
| Gemma 4 12B | Q8_0 | fits | 13.5 GB | 256k | 46tok/sest. | 2777 tok/s | chat, coding, long documents |
| Qwen 3.5 9B Alibaba Qwen | Q8_0 | fits | 10.8 GB | 256k | 59tok/sest. | 3461 tok/s | chat, coding, long documents |
| Mistral Small 3.2 24B Mistral AI | Q5_K_M | fits | 21.5 GB | 43k79k with q8 cache | 28tok/sest. | 1264 tok/s | chat, coding |
| Gemma 3 27B | Q5_K_M | fits | 21.7 GB | 51k95k with q8 cache | 28tok/sest. | 1271 tok/s | chat, coding |
| Ministral 3 14B Mistral AI | Q8_0 | fits | 19.3 GB | 57k104k with q8 cache | 32tok/sest. | 1922 tok/s | chat, coding |
| Qwen3 14B Alibaba Qwen | Q8_0 | fits | 20.5 GB | 40k | 30tok/sest. | 1728 tok/s | chat, coding |
| DeepSeek R1 0528 Qwen3 8B DeepSeek | Q8_0 | fits | 13.5 GB | 99k128k with q8 cache | 46tok/sest. | 2851 tok/s | chat, coding |
| Qwen3 8B Alibaba Qwen | Q8_0 | fits | 13.5 GB | 40k | 46tok/sest. | 2851 tok/s | chat, coding |
| Phi-4 14B Microsoft | Q8_0 | fits | 21.6 GB | 16k | 28tok/sest. | 1737 tok/s | chat, coding |
| Qwen 3.5 4B Alibaba Qwen | Q8_0 | fits | 6.1 GB | 256k | 112tok/sest. | 6475 tok/s | chat, coding, long documents |
| Ministral 3 8B Mistral AI | Q8_0 | fits | 13.5 GB | 103k187k with q8 cache | 46tok/sest. | 2754 tok/s | chat, coding |
| Granite 4.2 8B IBM | Q8_0 | fits | 14.6 GB | 86k128k with q8 cache | 43tok/sest. | 2622 tok/s | chat, coding, agents |
| Gemma 4 E4B | Q8_0 | fits | 9.4 GB | 128k | 68tok/sest. | 4348 tok/s | chat, coding, long documents |
| LFM2.5 8B-A1B Liquid AI | Q8_0 | fits | 10.8 GB | 125k | 140tok/sest. | 14221 tok/s | chat, coding, long documents |
| Llama 3.1 8B Meta | Q6_K | fits | 11.0 GB | 126k128k with q8 cache | 57tok/sest. | 3012 tok/s | chat, coding, long documents |
| Qwen 3.5 122B-A10B Alibaba Qwen | UD-Q2_K_XL | offload | 40.7 GB | – | 16tok/sest. | 2075 tok/s | chat |
| Qwen3 Coder Next Alibaba Qwen | Q4_K_M | offload | 46.9 GB | – | 18tok/sest. | 4694 tok/s | chat |
| Mistral Small 4 119B Mistral AI | UD-Q2_K_XL | offload | 39.0 GB | – | 22tok/sest. | 2354 tok/s | chat, coding |
| GLM 4.5 Air Z.ai | Q2_K | offload | 48.8 GB | – | 4tok/sest. | 712 tok/s | – |
| Qwen3 Next 80B-A3B Alibaba Qwen | Q4_K_M | offload | 46.9 GB | – | 22tok/sest. | 5586 tok/s | chat, coding |
| Nemotron 3.5 Lightning 30B-A3B NVIDIA | UD-Q6_K_XL | offload | 33.7 GB | – | 24tok/sest. | 7916 tok/s | chat, coding, long documents |
| Kimi Linear 48B-A3B Moonshot AI | Q4_K_M | offload | 29.2 GB | – | 55tok/sest. | 6467 tok/s | chat, coding, long documents |
| Qwen3 32B Alibaba Qwen | Q4_K_M | offload | 27.3 GB | 16k30k with q8 cache | 9tok/sest. | 678 tok/s | chat |
| DeepSeek R1 Distill Qwen 32B DeepSeek | Q4_K_M | offload | 27.4 GB | 16k30k with q8 cache | 9tok/sest. | 777 tok/s | chat |
| DeepSeek V4 Flash DeepSeek | UD-Q2_K_XL | won't run | 93.8 GB | – | – | – | – |
| GLM 5.3 Flash Z.ai | UD-Q2_K_XL | won't run | 102.6 GB | – | – | – | – |
| Qwen 3.8 Flash Next Alibaba Qwen | UD-Q2_K_XL | won't run | 75.2 GB | – | – | – | – |
| MiniMax M2.7 MiniMax | UD-Q2_K_XL | won't run | 78.8 GB | – | – | – | – |
| Nemotron 3 Super 120B-A12B NVIDIA | UD-Q2_K_XL | won't run | 52.2 GB | – | – | – | – |
| gpt-oss 120B OpenAI | MXFP4 | won't run | 61.1 GB | – | – | – | – |
| Llama 3.3 70B Meta | Q4_K_M | won't run | 50.5 GB | – | – | – | – |
Specifications
From the manufacturer's published specifications; sources are linked where we have them.
- GPU
- GeForce RTX 4090
- Memory
- 24 GB GDDR6X
- Memory bus
- 384-bit
- Memory bandwidth
- 1,008 GB/s
- FP16 compute
- 165 TFLOPS
- Software
- CUDA
- Board power
- 450 W
- PCIe generation
- Gen 4
- PCIe lanes
- x16