AMD Radeon RX 7900 XTX
24 GB · 960 GB/s · 355 W
In short
The AMD Radeon RX 7900 XTX has 24 GB of GDDR6 memory with 960 GB/s of bandwidth, which sets how fast it generates text. At 128k context it runs 23 of the 113 open models we track fully in memory; the most capable is Gemma 4 26B-A4B at Q5_K_M, at about 54 tok/s (estimated). The best new price for the AMD Radeon RX 7900 XTX in Germany is 1.555 € at eBay; used, it starts at 1.050 € (asking price).
Updated . Speeds are estimates; how they are worked out.
Prices
Every retailer we track in Germany, cheapest first. Buy links may earn us a commission.
| Retailer | Condition | Price | Stock | Checked | Buy |
|---|---|---|---|---|---|
| eBayPowerColor Hellhound Radeon RX 7900 XTX | used, asking price | 1.050 € | In stock | 2 h ago | Buy |
| eBayASRock Radeon RX 7900XTX Taichi 24gb | used, asking price | 1.200 €+ 8 € delivery | In stock | 2 h ago | Buy |
| eBayAMD Radeon RX 7900 XTX INGRAM | used, asking price | 1.300 €+ 8 € delivery | In stock | 2 h ago | Buy |
| eBayXFX Speedster MERC 310 Radeon RX 7900 XTX Black Edition | New | 1.555 € | In stock | 2 h ago | Buy |
| eBaySapphire Radeon RX 7900XTX Pulse Gaming OC 24GB GDDR6 3x DP 1x HDMI | used, asking price | 1.651 €+ 30 € delivery | In stock | 2 h ago | Buy |
| eBaySAPPHIRE NITRO+ AMD Radeon RX 7900 XTX Vapor-X 24GB GDDR6 | used, asking price | 2.999 €+ 7 € delivery | In stock | 2 h ago | Buy |
| AmazonSapphire NITRO+ AMD RADEON RX 7900 XTX O 24.576 MB GDDR6 | New | 1.800 € | Out of stock | 13 h ago | Buy |
Used on eBay: median asking price 1.200 €, most between 1.111 € and 1.300 € (5 listings, 2026-10-15).
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 @128k | Prefill | Good for |
|---|---|---|---|---|---|---|---|
| Gemma 4 26B-A4B | Q5_K_M | fits | 22.2 GB | 165k256k with q8 cache | 54tok/sest. | 3122 tok/s | chat, coding, agents, long documents |
| gpt-oss 20B OpenAI | MXFP4 | fits | 15.8 GB | 128k | 56tok/sest. | 2022 tok/s | chat, coding, agents, long documents |
| Gemma 4 12B | Q8_0 | fits | 15.7 GB | 256k | 35tok/sest. | 1297 tok/s | chat, coding, long documents |
| Qwen 3.5 9B Alibaba Qwen | Q8_0 | fits | 14.5 GB | 256k | 38tok/sest. | 1543 tok/s | chat, coding, long documents |
| Qwen 3.5 4B Alibaba Qwen | Q8_0 | fits | 9.8 GB | 256k | 60tok/sest. | 2404 tok/s | chat, coding, long documents |
| Gemma 4 E4B | Q8_0 | fits | 12.7 GB | 128k | 44tok/sest. | 2164 tok/s | chat, coding, long documents |
| LFM2.5 8B-A1B Liquid AI | Q8_0 | fits | 15.9 GB | 125k | 42tok/sest. | 2892 tok/s | chat, coding, long documents |
| Llama 3.1 8B Meta | Q5_K_M | fits | 22.9 GB | 128k | 23tok/sest. | 854 tok/s | chat, coding, long documents |
| Qwen2.5 0.5B Alibaba Qwen | Q8_0 | fits | 3.5 GB | 32k | 247tok/sest. | 6498 tok/s | chat, coding, long documents |
| Qwen3.5 0.8B Alibaba Qwen | Q8_0 | fits | 3.8 GB | 256k | 216tok/sest. | 8665 tok/s | chat, coding, agents, long documents |
| Qwen3.5 2B Alibaba Qwen | Q5_K_M | fits | 4.4 GB | 256k | 172tok/sest. | 5541 tok/s | chat, coding, agents, long documents |
| DeepSeek R1 Distill Qwen 1.5B DeepSeek | Q8_0 | fits | 6.8 GB | 128k | 93tok/sest. | 2903 tok/s | chat, coding, long documents |
| Mellum2.1 12B A2.5B Thinking JetBrains | Q8_0 | fits | 15.4 GB | 128k | 69tok/sest. | 3345 tok/s | chat, coding, agents, long documents |
| MiniCPM5 2B openbmb | Q8_0 | fits | 9.3 GB | 128k | 64tok/sest. | 1561 tok/s | chat, coding, agents, long documents |
| LFM2.5 2.6B Liquid AI | Q8_0 | fits | 11.7 GB | 128k | 48tok/sest. | 2002 tok/s | chat, coding, agents, long documents |
| LFM2.5 VL 3B Liquid AI | Q8_0 | fits | 11.7 GB | 32k | 48tok/sest. | 1926 tok/s | chat, coding, agents, long documents |
| Kimi VL A3B Moonshot AI | mlx-8bit | fits | 21.9 GB | 128k | 43tok/sest. | 2101 tok/s | chat, coding, agents, long documents |
| granite vision 4.1 4b IBM | Q8_0 | fits | 14.9 GB | 128k | 37tok/sest. | 1236 tok/s | chat, coding, long documents |
| granite 4.2 3b IBM | Q8_0 | fits | 15.2 GB | 128k | 36tok/sest. | 1260 tok/s | chat, coding, agents, long documents |
| GLM 4.6V Flash Z.ai | Q8_0 | fits | 15.9 GB | 128k | 34tok/sest. | 678 tok/s | chat, coding, agents, long documents |
| DeepSeek R1 Distill Qwen 7B DeepSeek | Q8_0 | fits | 16.1 GB | 128k | 34tok/sest. | 1036 tok/s | chat, coding, long documents |
| GLM 4.1V 9B Thinking Z.ai | mlx-8bit | fits | 17.5 GB | 64k | 31tok/sest. | 678 tok/s | chat, coding, long documents |
| DeepSeek R1 Distill Llama 8B DeepSeek | Q5_K_M | fits | 22.9 GB | 128k | 23tok/sest. | 854 tok/s | chat, coding, long documents |
| Qwen 3.8 27B Alibaba Qwen | UD-Q4_K_M | offload | 25.0 GB | 101k177k with q8 cache | 12tok/sest. | 505 tok/s | chat, coding |
| Qwen 3.5 122B-A10B Alibaba Qwen | UD-Q2_K_XL | offload | 43.7 GB | – | 9tok/sest. | 666 tok/s | chat |
| Qwen 3.6 27B Alibaba Qwen | Q4_K_M | offload | 25.4 GB | 96k168k with q8 cache | 12tok/sest. | 501 tok/s | chat, coding |
| Gemma 4 31B | Q4_K_M | offload | 29.4 GB | 54k103k with q8 cache | 6tok/sest. | 435 tok/s | chat, coding |
| Mistral Small 4 119B Mistral AI | UD-Q2_K_XL | offload | 41.8 GB | – | 12tok/sest. | 590 tok/s | chat, coding |
| Qwen 3.6 35B-A3B Alibaba Qwen | MXFP4 | offload | 24.3 GB | 83k130k with q8 cache | 48tok/sest. | 2496 tok/s | chat, coding, agents, long documents |
| Qwen 3.5 27B Alibaba Qwen | Q4_K_M | offload | 25.3 GB | 97k170k with q8 cache | 12tok/sest. | 501 tok/s | chat, coding |
| Qwen 3.5 35B-A3B Alibaba Qwen | Q4_K_M | offload | 24.6 GB | 72k113k with q8 cache | 45tok/sest. | 2478 tok/s | chat, coding, agents, long documents |
| GLM 4.7 Flash Z.ai | Q4_K_M | offload | 25.2 GB | 92k160k with q8 cache | 18tok/sest. | 1151 tok/s | chat, coding, agents, long documents |
| Devstral Small 2 24B Mistral AI | Q4_K_M | offload | 34.9 GB | 55k101k with q8 cache | 4tok/sest. | 374 tok/s | chat, coding |
| Nemotron 3.5 Lightning 30B-A3B NVIDIA | UD-Q6_K_XL | offload | 34.9 GB | – | 19tok/sest. | 2730 tok/s | chat, coding, long documents |
| Kimi Linear 48B-A3B Moonshot AI | Q4_K_M | offload | 30.6 GB | – | 33tok/sest. | 1636 tok/s | chat, coding, long documents |
| Qwen3 Coder 30B-A3B Alibaba Qwen | Q4_K_M | offload | 30.8 GB | 51k91k with q8 cache | 7tok/sest. | 631 tok/s | chat, coding, agents |
| Gemma 3 27B | Q4_K_M | offload | 27.4 GB | 78k128k with q8 cache | 8tok/sest. | 585 tok/s | chat, coding |
| Mistral Small 3.2 24B Mistral AI | Q4_K_M | offload | 34.9 GB | 55k101k with q8 cache | 4tok/sest. | 374 tok/s | chat, coding |
| Ministral 3 14B Mistral AI | Q4_K_M | offload | 29.2 GB | 90k165k with q8 cache | 6tok/sest. | 528 tok/s | chat, coding |
| Qwen3 14B Alibaba Qwen | Q4_K_M | offload | 29.9 GB | 40k | 6tok/sest. | 444 tok/s | chat, coding |
| DeepSeek R1 0528 Qwen3 8B DeepSeek | Q4_K_M | offload | 24.2 GB | 120k128k with q8 cache | 16tok/sest. | 761 tok/s | chat, coding, long documents |
| Qwen3 8B Alibaba Qwen | Q4_K_M | offload | 24.2 GB | 40k | 16tok/sest. | 761 tok/s | chat, coding, long documents |
| Phi-4 14B Microsoft | Q4_K_M | offload | 34.8 GB | 16k | 4tok/sest. | 410 tok/s | chat, coding |
| Ministral 3 8B Mistral AI | Q4_K_M | offload | 23.4 GB | 126k229k with q8 cache | 21tok/sest. | 788 tok/s | chat, coding, long documents |
| Granite 4.2 8B IBM | Q4_K_M | offload | 26.7 GB | 106k128k with q8 cache | 9tok/sest. | 652 tok/s | chat, coding, agents |
| Ministral 3 8B Reasoning 2512 Mistral AI | Q4_K_M | offload | 23.4 GB | 126k229k with q8 cache | 21tok/sest. | 788 tok/s | chat, coding, agents, long documents |
| LFM2.5 24B A2B Liquid AI | Q4_K_M | offload | 25.8 GB | 96k128k with q8 cache | 13tok/sest. | 1579 tok/s | chat, coding, agents |
| DeepSeek R1 Distill Qwen 14B DeepSeek | Q4_K_M | offload | 33.9 GB | 72k128k with q8 cache | 4tok/sest. | 367 tok/s | chat, coding |
| Devstral Small 2507 Mistral AI | Q4_K_M | offload | 34.9 GB | 55k101k with q8 cache | 4tok/sest. | 377 tok/s | chat, coding |
| Magistral Small 2506 Mistral AI | Q4_K_M | offload | 34.9 GB | 40k | 4tok/sest. | 377 tok/s | chat, coding |
| Magistral Small 2507 Mistral AI | Q4_K_M | offload | 34.9 GB | 40k | 4tok/sest. | 377 tok/s | chat, coding |
| Magistral Small 2509 Mistral AI | Q4_K_M | offload | 34.9 GB | 55k101k with q8 cache | 4tok/sest. | 374 tok/s | chat, coding |
| DeepSeek V4 Flash DeepSeek | UD-Q2_K_XL | won't run | 102.5 GB | – | – | – | – |
| GLM 5.3 Flash Z.ai | UD-Q2_K_XL | won't run | 104.3 GB | – | – | – | – |
| Qwen 3.8 Flash Next Alibaba Qwen | UD-Q2_K_XL | won't run | 78.1 GB | – | – | – | – |
| MiniMax M2.7 MiniMax | UD-Q2_K_XL | won't run | 102.7 GB | – | – | – | – |
| Nemotron 3 Super 120B-A12B NVIDIA | UD-Q2_K_XL | won't run | 53.6 GB | – | – | – | – |
| Qwen3 Coder Next Alibaba Qwen | Q4_K_M | won't run | 49.8 GB | – | – | – | – |
| gpt-oss 120B OpenAI | Q4_K_M | won't run | 64.5 GB | – | – | – | – |
| GLM 4.5 Air Z.ai | Q2_K | won't run | 66.8 GB | – | – | – | – |
| Qwen3 Next 80B-A3B Alibaba Qwen | Q4_K_M | won't run | 49.8 GB | – | – | – | – |
| Qwen3 32B Alibaba Qwen | Q4_K_M | won't run | 52.0 GB | 15k28k with q8 cache | – | – | – |
| DeepSeek R1 Distill Qwen 32B DeepSeek | Q4_K_M | won't run | 52.0 GB | 15k28k with q8 cache | – | – | – |
| Llama 3.3 70B Meta | Q4_K_M | won't run | 81.2 GB | – | – | – | – |
| DeepSeek R1 DeepSeek | Q4_K_M | won't run | 386.8 GB | – | – | – | – |
| DeepSeek R1 0528 DeepSeek | Q4_K_M | won't run | 387.3 GB | – | – | – | – |
| DeepSeek R1 Distill Llama 70B DeepSeek | Q4_K_M | won't run | 81.2 GB | – | – | – | – |
| DeepSeek V3 0324 DeepSeek | Q4_K_M | won't run | 386.8 GB | – | – | – | – |
| DeepSeek V3.1 DeepSeek | Q4_K_M | won't run | 387.7 GB | – | – | – | – |
| DeepSeek V3.2 DeepSeek | Q4_K_M | won't run | 387.7 GB | – | – | – | – |
| DeepSeek V4 Flash 0731 DeepSeek | MXFP4 | won't run | 157.9 GB | – | – | – | – |
| DeepSeek V4 Flash Vision Exp DeepSeek | MXFP4 | won't run | 166.7 GB | – | – | – | – |
| Devstral 2 123B Instruct 2512 Mistral AI | Q8_0 | won't run | 169.3 GB | – | – | – | – |
| GLM 4.5V Z.ai | Q4_K_M | won't run | 83.8 GB | – | – | – | – |
| GLM 4.6V Z.ai | Q4_K_M | won't run | 90.2 GB | – | – | – | – |
| GLM 4.7 Z.ai | Q4_K_M | won't run | 249.1 GB | – | – | – | – |
| GLM 5.1 Z.ai | MXFP4 | won't run | 432.5 GB | – | – | – | – |
| GLM 5.2 Z.ai | UD-Q4_K_M | won't run | 446.4 GB | – | – | – | – |
| GLM 5.3 Z.ai | Q8_0 | won't run | 758.8 GB | – | – | – | – |
| Kimi K2 Moonshot AI | Q4_K_M | won't run | 588.3 GB | – | – | – | – |
| Kimi K2 Instruct 0905 Moonshot AI | Q4_K_M | won't run | 588.7 GB | – | – | – | – |
| Kimi K2 Thinking Moonshot AI | Q4_K_M | won't run | 588.7 GB | – | – | – | – |
| Kimi K2.5 Moonshot AI | Q4_K_M | won't run | 588.7 GB | – | – | – | – |
| MiniMax M2 MiniMax | Q4_K_M | won't run | 161.4 GB | – | – | – | – |
| MiniMax M2.1 MiniMax | Q4_K_M | won't run | 161.4 GB | – | – | – | – |
| MiniMax M2.5 MiniMax | MXFP4 | won't run | 147.8 GB | – | – | – | – |
| MiniMax M3 MiniMax | UD-Q4_K_M | won't run | 263.3 GB | – | – | – | – |
| Mistral Medium 3.5 128B Mistral AI | Q4_K_M | won't run | 115.3 GB | – | – | – | – |
| granite 4.2 30b IBM | Q4_K_M | won't run | 50.3 GB | 21k40k with q8 cache | – | – | – |
Specifications
From the manufacturer's published specifications; sources are linked where we have them.
- GPU
- Radeon RX 7900 XTX
- Memory
- 24 GB GDDR6
- Memory bus
- 384-bit
- Memory bandwidth
- 960 GB/s
- FP16 compute
- 123 TFLOPS
- Software
- ROCm
- Board power
- 355 W
- PCIe generation
- Gen 4
- PCIe lanes
- x16
Compare with
Questions
- How much memory does the AMD Radeon RX 7900 XTX have for AI models?
- The AMD Radeon RX 7900 XTX has 24 GB of GDDR6 memory with 960 GB/s of bandwidth, which sets how fast it generates text.
- What is the best model the AMD Radeon RX 7900 XTX can run locally?
- Gemma 4 26B-A4B (Q5_K_M) fits fully in its memory at 128k context and generates at about 54 tok/s, an estimate from its memory bandwidth. 23 of the 113 models we track fit at that context.
- Can the AMD Radeon RX 7900 XTX run DeepSeek V4 Flash?
- No: even at UD-Q2_K_XL, DeepSeek V4 Flash needs about 102 GB at 128k context, more than the AMD Radeon RX 7900 XTX and system RAM can hold.
- How much does the AMD Radeon RX 7900 XTX cost in Germany?
- The best new price for the AMD Radeon RX 7900 XTX in Germany is 1.555 € at eBay; used, it starts at 1.050 € (asking price).
Speeds on this page are for 128k context. See the best hardware for local AI at every budget.