AMD Radeon RX 9060 XT 16GB
16 GB · 320 GB/s · 160 W
In short
The AMD Radeon RX 9060 XT 16GB has 16 GB of GDDR6 memory with 320 GB/s of bandwidth, which sets how fast it generates text. At 64k context it runs 49 of the 113 open models we track fully in memory; the most capable is Qwen 3.8 27B at Q6_K, at about 6 tok/s (estimated). The best new price for the AMD Radeon RX 9060 XT 16GB in Canada is $800 at Amazon; used, it starts at $665 (asking price).
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 |
|---|---|---|---|---|---|
| eBayGigabyte Radeon RX 9060 XT Gaming OC 16GB | used, asking price | $665 | In stock | 3 h ago | Buy |
| AmazonSapphire Pulse AMD Radeon RX 9060 XT 16GB GDDR6 Gaming GPU | New | $800 | In stock | 6 h ago | Buy |
| AmazonSapphire Pure AMD Radeon RX 9060 XT 16GB GDDR6 Gaming GPU | New | $820 | In stock | 2 h ago | Buy |
| AmazonASUS AMD Dual Radeon RX 9060 XT 16GB GDDR6 Video Card DUAL-RX9060XT-16G | New | $900 | In stock | 2 h ago | Buy |
| AmazonASUS Dual Radeon RX 9060 XT 16GB GDDR6 Gaming Graphics Card | New | $937 | In stock | 2 h ago | Buy |
| AmazonXFX Swift AMD Radeon RX 9060 XT OC Triple Fan Gaming Edition with 16GB GDDR6… | New | $968 | In stock | 2 h ago | Buy |
| AmazonASUS Prime Radeon RX 9060 XT 16GB GDDR6 OC Edition Gaming Graphics Card | New | $980 | In stock | 2 h ago | Buy |
| AmazonSapphire Technology Nitro+ AMD Radeon RX 9060 XT Gaming OC | New | $1,000 | In stock | 2 h ago | Buy |
| AmazonGigabyte Radeon RX 9060 XT Gaming OC 16G Graphics Card | New | $1,100 | In stock | 2 h ago | Buy |
| AmazonASUS Dual Radeon RX 9060 XT 16GB GDDR6 White Edition | New | $1,140 | In stock | 2 h ago | Buy |
| AmazonASUS AMD Radeon RX 9060 XT Video Card 16GB GDDR6 OC Edition… | New | $2,181 | In stock | 2 h ago | Buy |
Price history
Lowest price shown each day over the last 90 days.
Not enough price history yet.
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 |
|---|---|---|---|---|---|---|---|
| Qwen 3.8 27B Alibaba Qwen | Q6_K | fits | 28.0 GB | 98k172k with q8 cache | 6tok/sest. | 527 tok/s | – |
| Qwen 3.6 27B Alibaba Qwen | Q6_K | fits | 26.8 GB | 116k204k with q8 cache | 7tok/sest. | 527 tok/s | – |
| Gemma 4 31B | Q5_K_M | fits | 27.6 GB | 97k179k with q8 cache | 6tok/sest. | 481 tok/s | – |
| Qwen 3.6 35B-A3B Alibaba Qwen | UD-Q6_K | fits | 30.3 GB | 68k107k with q8 cache | 27tok/sest. | 3171 tok/s | chat, coding, agents, long documents |
| Qwen 3.5 27B Alibaba Qwen | Q6_K | fits | 26.7 GB | 117k206k with q8 cache | 7tok/sest. | 527 tok/s | – |
| Gemma 4 26B-A4B | Q8_0 | fits | 28.1 GB | 153k245k with q8 cache | 19tok/sest. | 3247 tok/s | chat, coding |
| Qwen 3.5 35B-A3B Alibaba Qwen | Q6_K | fits | 29.9 GB | 85k133k with q8 cache | 28tok/sest. | 3171 tok/s | chat, coding, agents, long documents |
| GLM 4.7 Flash Z.ai | Q6_K | fits | 28.0 GB | 106k183k with q8 cache | 14tok/sest. | 1573 tok/s | chat |
| Devstral Small 2 24B Mistral AI | Q6_K | fits | 29.7 GB | 68k124k with q8 cache | 6tok/sest. | 519 tok/s | – |
| Kimi Linear 48B-A3B Moonshot AI | Q4_K_M | fits | 30.2 GB | 78k107k with q8 cache | 44tok/sest. | 2547 tok/s | chat, coding, long documents |
| gpt-oss 20B OpenAI | MXFP4 | fits | 14.5 GB | 128k | 26tok/sest. | 2427 tok/s | chat, coding, agents, long documents |
| Qwen3 Coder 30B-A3B Alibaba Qwen | Q5_K_M | fits | 27.9 GB | 89k159k with q8 cache | 12tok/sest. | 1135 tok/s | chat |
| Gemma 4 12B | Q8_0 | fits | 14.8 GB | 256k | 13tok/sest. | 1248 tok/s | – |
| Qwen 3.5 9B Alibaba Qwen | Q8_0 | fits | 12.6 GB | 256k | 15tok/sest. | 1528 tok/s | chat |
| Gemma 3 27B | Q6_K | fits | 27.7 GB | 95k128k with q8 cache | 6tok/sest. | 590 tok/s | – |
| Mistral Small 3.2 24B Mistral AI | Q6_K | fits | 29.7 GB | 68k124k with q8 cache | 6tok/sest. | 519 tok/s | – |
| Ministral 3 14B Mistral AI | Q8_0 | fits | 25.0 GB | 97k176k with q8 cache | 7tok/sest. | 730 tok/s | – |
| Qwen3 14B Alibaba Qwen | Q8_0 | fits | 26.3 GB | 40k | 7tok/sest. | 639 tok/s | – |
| DeepSeek R1 0528 Qwen3 8B DeepSeek | Q8_0 | fits | 18.8 GB | 128k | 10tok/sest. | 1010 tok/s | chat |
| Qwen3 8B Alibaba Qwen | Q8_0 | fits | 18.8 GB | 40k | 10tok/sest. | 1010 tok/s | chat |
| Phi-4 14B Microsoft | Q8_0 | fits | 28.7 GB | 16k | 6tok/sest. | 642 tok/s | – |
| Qwen 3.5 4B Alibaba Qwen | Q8_0 | fits | 7.9 GB | 256k | 26tok/sest. | 2648 tok/s | chat, coding, long documents |
| Ministral 3 8B Mistral AI | Q8_0 | fits | 18.6 GB | 149k256k with q8 cache | 10tok/sest. | 999 tok/s | chat |
| Granite 4.2 8B IBM | Q8_0 | fits | 20.4 GB | 125k128k with q8 cache | 9tok/sest. | 923 tok/s | chat |
| Gemma 4 E4B | Q8_0 | fits | 11.1 GB | 128k | 17tok/sest. | 2001 tok/s | chat |
| LFM2.5 8B-A1B Liquid AI | Q8_0 | fits | 13.1 GB | 125k | 25tok/sest. | 4270 tok/s | chat, coding |
| Llama 3.1 8B Meta | Q6_K | fits | 15.8 GB | 128k | 12tok/sest. | 1085 tok/s | chat |
| Qwen2.5 0.5B Alibaba Qwen | Q8_0 | fits | 2.9 GB | 32k | 132tok/sest. | 9470 tok/s | chat, coding, long documents |
| Qwen3.5 0.8B Alibaba Qwen | Q8_0 | fits | 3.2 GB | 256k | 108tok/sest. | 10737 tok/s | chat, coding, agents, long documents |
| Qwen3.5 2B Alibaba Qwen | Q5_K_M | fits | 3.8 GB | 256k | 78tok/sest. | 5854 tok/s | chat, coding, agents, long documents |
| DeepSeek R1 Distill Qwen 1.5B DeepSeek | Q8_0 | fits | 5.2 GB | 128k | 47tok/sest. | 3922 tok/s | chat, coding, long documents |
| MiniCPM5 2B openbmb | Q8_0 | fits | 6.8 GB | 128k | 32tok/sest. | 2211 tok/s | chat, coding, agents, long documents |
| Mellum2.1 12B A2.5B Thinking JetBrains | Q8_0 | fits | 14.6 GB | 128k | 29tok/sest. | 3957 tok/s | chat, coding, agents, long documents |
| LFM2.5 2.6B Liquid AI | Q8_0 | fits | 8.1 GB | 128k | 26tok/sest. | 2681 tok/s | chat, coding, agents, long documents |
| LFM2.5 VL 3B Liquid AI | Q8_0 | fits | 8.1 GB | 32k | 26tok/sest. | 2521 tok/s | chat, coding, agents, long documents |
| Kimi VL A3B Moonshot AI | mlx-8bit | fits | 20.2 GB | 128k | 20tok/sest. | 2721 tok/s | chat, coding |
| granite vision 4.1 4b IBM | Q8_0 | fits | 10.0 GB | 128k | 20tok/sest. | 1683 tok/s | chat, coding |
| granite 4.2 3b IBM | Q8_0 | fits | 10.3 GB | 128k | 19tok/sest. | 1738 tok/s | chat, coding |
| DeepSeek R1 Distill Qwen 7B DeepSeek | Q8_0 | fits | 12.7 GB | 128k | 15tok/sest. | 1270 tok/s | chat |
| LFM2.5 24B A2B Liquid AI | Q6_K | fits | 26.4 GB | 111k128k with q8 cache | 15tok/sest. | 2446 tok/s | chat, coding |
| GLM 4.6V Flash Z.ai | Q8_0 | fits | 13.5 GB | 128k | 14tok/sest. | 857 tok/s | chat |
| GLM 4.1V 9B Thinking Z.ai | mlx-8bit | fits | 15.1 GB | 64k | 12tok/sest. | 857 tok/s | – |
| DeepSeek R1 Distill Llama 8B DeepSeek | Q8_0 | fits | 17.6 GB | 128k | 10tok/sest. | 1085 tok/s | chat |
| Ministral 3 8B Reasoning 2512 Mistral AI | Q8_0 | fits | 18.6 GB | 149k256k with q8 cache | 10tok/sest. | 999 tok/s | chat |
| Devstral Small 2507 Mistral AI | Q5_K_M | fits | 27.3 GB | 83k128k with q8 cache | 6tok/sest. | 525 tok/s | – |
| Magistral Small 2507 Mistral AI | Q5_K_M | fits | 27.3 GB | 40k | 6tok/sest. | 525 tok/s | – |
| Magistral Small 2509 Mistral AI | Q5_K_M | fits | 27.3 GB | 83k128k with q8 cache | 6tok/sest. | 519 tok/s | – |
| DeepSeek R1 Distill Qwen 14B DeepSeek | Q8_0 | fits | 28.3 GB | 74k128k with q8 cache | 6tok/sest. | 584 tok/s | – |
| Magistral Small 2506 Mistral AI | Q6_K | fits | 29.7 GB | 40k | 6tok/sest. | 525 tok/s | – |
| Qwen 3.5 122B-A10B Alibaba Qwen | UD-Q2_K_XL | offload | 42.3 GB | – | 12tok/sest. | 904 tok/s | chat |
| Qwen3 Coder Next Alibaba Qwen | Q4_K_M | offload | 48.4 GB | – | 14tok/sest. | 1952 tok/s | chat |
| Nemotron 3 Super 120B-A12B NVIDIA | UD-Q2_K_XL | offload | 53.2 GB | – | 9tok/sest. | 936 tok/s | – |
| Mistral Small 4 119B Mistral AI | UD-Q2_K_XL | offload | 40.5 GB | – | 17tok/sest. | 909 tok/s | chat, coding |
| GLM 4.5 Air Z.ai | Q2_K | offload | 55.4 GB | – | 3tok/sest. | 240 tok/s | – |
| Qwen3 Next 80B-A3B Alibaba Qwen | Q4_K_M | offload | 48.4 GB | – | 16tok/sest. | 2227 tok/s | chat, coding |
| Nemotron 3.5 Lightning 30B-A3B NVIDIA | UD-Q6_K_XL | offload | 34.7 GB | – | 22tok/sest. | 3604 tok/s | chat, coding, long documents |
| Qwen3 32B Alibaba Qwen | Q4_K_M | offload | 36.1 GB | 40k | 4tok/sest. | 242 tok/s | – |
| DeepSeek R1 Distill Qwen 32B DeepSeek | Q4_K_M | offload | 36.2 GB | 41k76k with q8 cache | 3tok/sest. | 299 tok/s | – |
| granite 4.2 30b IBM | Q4_K_M | offload | 34.5 GB | 48k88k with q8 cache | 4tok/sest. | 359 tok/s | – |
| DeepSeek V4 Flash DeepSeek | UD-Q2_K_XL | won't run | 97.2 GB | – | – | – | – |
| GLM 5.3 Flash Z.ai | UD-Q2_K_XL | won't run | 103.7 GB | – | – | – | – |
| Qwen 3.8 Flash Next Alibaba Qwen | UD-Q2_K_XL | won't run | 76.7 GB | – | – | – | – |
| MiniMax M2.7 MiniMax | UD-Q2_K_XL | won't run | 87.3 GB | – | – | – | – |
| gpt-oss 120B OpenAI | Q4_K_M | won't run | 62.4 GB | – | – | – | – |
| Llama 3.3 70B Meta | Q4_K_M | won't run | 61.3 GB | – | – | – | – |
| DeepSeek R1 DeepSeek | Q4_K_M | won't run | 382.6 GB | – | – | – | – |
| DeepSeek R1 0528 DeepSeek | Q4_K_M | won't run | 383.1 GB | – | – | – | – |
| DeepSeek R1 Distill Llama 70B DeepSeek | Q4_K_M | won't run | 61.3 GB | – | – | – | – |
| DeepSeek V3 0324 DeepSeek | Q4_K_M | won't run | 382.6 GB | – | – | – | – |
| DeepSeek V3.1 DeepSeek | Q4_K_M | won't run | 383.5 GB | – | – | – | – |
| DeepSeek V3.2 DeepSeek | Q4_K_M | won't run | 383.5 GB | – | – | – | – |
| DeepSeek V4 Flash 0731 DeepSeek | MXFP4 | won't run | 152.7 GB | – | – | – | – |
| DeepSeek V4 Flash Vision Exp DeepSeek | MXFP4 | won't run | 161.5 GB | – | – | – | – |
| Devstral 2 123B Instruct 2512 Mistral AI | Q8_0 | won't run | 147.4 GB | – | – | – | – |
| GLM 4.5V Z.ai | Q4_K_M | won't run | 72.4 GB | – | – | – | – |
| GLM 4.6V Z.ai | Q4_K_M | won't run | 78.8 GB | – | – | – | – |
| GLM 4.7 Z.ai | Q4_K_M | won't run | 226.3 GB | – | – | – | – |
| GLM 5.1 Z.ai | MXFP4 | won't run | 427.1 GB | – | – | – | – |
| GLM 5.2 Z.ai | UD-Q4_K_M | won't run | 441.0 GB | – | – | – | – |
| GLM 5.3 Z.ai | Q8_0 | won't run | 753.5 GB | – | – | – | – |
| Kimi K2 Moonshot AI | Q4_K_M | won't run | 584.1 GB | – | – | – | – |
| Kimi K2 Instruct 0905 Moonshot AI | Q4_K_M | won't run | 584.5 GB | – | – | – | – |
| Kimi K2 Thinking Moonshot AI | Q4_K_M | won't run | 584.5 GB | – | – | – | – |
| Kimi K2.5 Moonshot AI | Q4_K_M | won't run | 584.5 GB | – | – | – | – |
| MiniMax M2 MiniMax | Q4_K_M | won't run | 146.0 GB | – | – | – | – |
| MiniMax M2.1 MiniMax | Q4_K_M | won't run | 146.0 GB | – | – | – | – |
| MiniMax M2.5 MiniMax | MXFP4 | won't run | 132.4 GB | – | – | – | – |
| MiniMax M3 MiniMax | UD-Q4_K_M | won't run | 255.9 GB | – | – | – | – |
| Mistral Medium 3.5 128B Mistral AI | Q4_K_M | won't run | 93.4 GB | – | – | – | – |
Specifications
From the manufacturer's published specifications; sources are linked where we have them.
- GPU
- Radeon RX 9060 XT 16GB
- Memory
- 16 GB GDDR6
- Memory bus
- 128-bit
- Memory bandwidth
- 320 GB/s
- FP16 compute
- 103 TFLOPS
- Software
- ROCm
- Board power
- 160 W
- PCIe generation
- Gen 5
- PCIe lanes
- x16
Compare with
Questions
- How much memory does the AMD Radeon RX 9060 XT 16GB have for AI models?
- The AMD Radeon RX 9060 XT 16GB has 16 GB of GDDR6 memory with 320 GB/s of bandwidth, which sets how fast it generates text.
- What is the best model the AMD Radeon RX 9060 XT 16GB can run locally?
- Qwen 3.8 27B (Q6_K) fits fully in its memory at 64k context and generates at about 6 tok/s, an estimate from its memory bandwidth. 49 of the 113 models we track fit at that context.
- Can the AMD Radeon RX 9060 XT 16GB run DeepSeek V4 Flash?
- No: even at UD-Q2_K_XL, DeepSeek V4 Flash needs about 97 GB at 64k context, more than the AMD Radeon RX 9060 XT 16GB and system RAM can hold.
- How much does the AMD Radeon RX 9060 XT 16GB cost in Canada?
- The best new price for the AMD Radeon RX 9060 XT 16GB in Canada is $800 at Amazon; used, it starts at $665 (asking price).
Speeds on this page are for 64k context. See the best hardware for local AI at every budget.