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 128k context it runs 38 of the 113 open models we track fully in memory; the most capable is Qwen 3.8 27B at Q5_K_M, 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 @128k | Prefill | Good for |
|---|---|---|---|---|---|---|---|
| Qwen 3.8 27B Alibaba Qwen | Q5_K_M | fits | 29.7 GB | 138k242k with q8 cache | 6tok/sest. | 443 tok/s | – |
| Qwen 3.6 27B Alibaba Qwen | Q5_K_M | fits | 28.4 GB | 157k256k with q8 cache | 6tok/sest. | 443 tok/s | – |
| Gemma 4 31B | Q4_K_M | fits | 29.9 GB | 133k245k with q8 cache | 6tok/sest. | 421 tok/s | – |
| Qwen 3.6 35B-A3B Alibaba Qwen | UD-Q5_K_M | fits | 29.2 GB | 173k256k with q8 cache | 21tok/sest. | 2154 tok/s | chat, coding, agents, long documents |
| Qwen 3.5 27B Alibaba Qwen | Q5_K_M | fits | 28.5 GB | 156k256k with q8 cache | 6tok/sest. | 443 tok/s | – |
| Gemma 4 26B-A4B | Q8_0 | fits | 29.8 GB | 153k245k with q8 cache | 15tok/sest. | 2615 tok/s | chat, coding |
| Qwen 3.5 35B-A3B Alibaba Qwen | Q5_K_M | fits | 29.1 GB | 180k256k with q8 cache | 21tok/sest. | 2154 tok/s | chat, coding, agents, long documents |
| GLM 4.7 Flash Z.ai | Q5_K_M | fits | 28.6 GB | 159k198k with q8 cache | 10tok/sest. | 1016 tok/s | chat |
| gpt-oss 20B OpenAI | MXFP4 | fits | 16.3 GB | 128k | 19tok/sest. | 1693 tok/s | chat, coding, agents, long documents |
| Gemma 4 12B | Q8_0 | fits | 16.2 GB | 256k | 12tok/sest. | 1087 tok/s | – |
| Qwen 3.5 9B Alibaba Qwen | Q8_0 | fits | 15.0 GB | 256k | 13tok/sest. | 1292 tok/s | chat |
| Gemma 3 27B | Q4_K_M | fits | 27.9 GB | 128k | 6tok/sest. | 542 tok/s | – |
| Ministral 3 14B Mistral AI | Q4_K_M | fits | 29.7 GB | 132k241k with q8 cache | 6tok/sest. | 509 tok/s | chat |
| DeepSeek R1 0528 Qwen3 8B DeepSeek | Q8_0 | fits | 28.2 GB | 128k | 6tok/sest. | 655 tok/s | chat |
| Qwen3 8B Alibaba Qwen | Q8_0 | fits | 28.2 GB | 40k | 6tok/sest. | 655 tok/s | chat |
| Qwen 3.5 4B Alibaba Qwen | Q8_0 | fits | 10.3 GB | 256k | 20tok/sest. | 2013 tok/s | chat, coding, long documents |
| Ministral 3 8B Mistral AI | Q8_0 | fits | 27.5 GB | 149k256k with q8 cache | 7tok/sest. | 663 tok/s | chat |
| Granite 4.2 8B IBM | Q6_K | fits | 29.1 GB | 128k | 6tok/sest. | 596 tok/s | chat |
| Gemma 4 E4B | Q8_0 | fits | 13.2 GB | 128k | 15tok/sest. | 1812 tok/s | chat |
| LFM2.5 8B-A1B Liquid AI | Q8_0 | fits | 16.4 GB | 125k | 14tok/sest. | 2422 tok/s | chat, coding |
| Llama 3.1 8B Meta | Q6_K | fits | 24.2 GB | 128k | 7tok/sest. | 715 tok/s | chat |
| Qwen2.5 0.5B Alibaba Qwen | Q8_0 | fits | 4.0 GB | 32k | 82tok/sest. | 5441 tok/s | chat, coding, long documents |
| Qwen3.5 0.8B Alibaba Qwen | Q8_0 | fits | 4.3 GB | 256k | 72tok/sest. | 7256 tok/s | chat, coding, agents, long documents |
| Qwen3.5 2B Alibaba Qwen | Q5_K_M | fits | 4.9 GB | 256k | 57tok/sest. | 4640 tok/s | chat, coding, agents, long documents |
| DeepSeek R1 Distill Qwen 1.5B DeepSeek | Q8_0 | fits | 7.3 GB | 128k | 31tok/sest. | 2431 tok/s | chat, coding, long documents |
| Mellum2.1 12B A2.5B Thinking JetBrains | Q8_0 | fits | 15.9 GB | 128k | 23tok/sest. | 2801 tok/s | chat, coding, agents, long documents |
| MiniCPM5 2B openbmb | Q8_0 | fits | 9.8 GB | 128k | 21tok/sest. | 1307 tok/s | chat, coding, agents, long documents |
| LFM2.5 2.6B Liquid AI | Q8_0 | fits | 12.2 GB | 128k | 16tok/sest. | 1677 tok/s | chat, coding, agents, long documents |
| LFM2.5 VL 3B Liquid AI | Q8_0 | fits | 12.2 GB | 32k | 16tok/sest. | 1613 tok/s | chat, coding, agents, long documents |
| Kimi VL A3B Moonshot AI | mlx-8bit | fits | 22.4 GB | 128k | 14tok/sest. | 1759 tok/s | chat, coding |
| granite vision 4.1 4b IBM | Q8_0 | fits | 15.4 GB | 128k | 12tok/sest. | 1035 tok/s | chat, coding |
| granite 4.2 3b IBM | Q8_0 | fits | 15.7 GB | 128k | 12tok/sest. | 1055 tok/s | chat, coding |
| GLM 4.6V Flash Z.ai | Q8_0 | fits | 16.4 GB | 128k | 12tok/sest. | 567 tok/s | chat |
| DeepSeek R1 Distill Qwen 7B DeepSeek | Q8_0 | fits | 16.6 GB | 128k | 11tok/sest. | 868 tok/s | chat |
| GLM 4.1V 9B Thinking Z.ai | mlx-8bit | fits | 18.0 GB | 64k | 10tok/sest. | 567 tok/s | – |
| LFM2.5 24B A2B Liquid AI | Q5_K_M | fits | 29.3 GB | 128k | 9tok/sest. | 1415 tok/s | chat, coding |
| DeepSeek R1 Distill Llama 8B DeepSeek | Q8_0 | fits | 26.0 GB | 128k | 7tok/sest. | 715 tok/s | chat |
| Ministral 3 8B Reasoning 2512 Mistral AI | Q8_0 | fits | 27.5 GB | 149k256k with q8 cache | 7tok/sest. | 663 tok/s | chat |
| Qwen 3.5 122B-A10B Alibaba Qwen | UD-Q2_K_XL | offload | 44.2 GB | – | 9tok/sest. | 633 tok/s | chat |
| Qwen3 Coder Next Alibaba Qwen | Q4_K_M | offload | 50.3 GB | – | 10tok/sest. | 1315 tok/s | chat |
| Nemotron 3 Super 120B-A12B NVIDIA | UD-Q2_K_XL | offload | 54.1 GB | – | 8tok/sest. | 804 tok/s | – |
| Mistral Small 4 119B Mistral AI | UD-Q2_K_XL | offload | 42.3 GB | – | 12tok/sest. | 562 tok/s | chat, coding |
| Qwen3 Next 80B-A3B Alibaba Qwen | Q4_K_M | offload | 50.3 GB | – | 11tok/sest. | 1437 tok/s | chat, coding |
| Devstral Small 2 24B Mistral AI | Q4_K_M | offload | 35.4 GB | 97k177k with q8 cache | 4tok/sest. | 361 tok/s | – |
| Nemotron 3.5 Lightning 30B-A3B NVIDIA | UD-Q6_K_XL | offload | 35.4 GB | – | 19tok/sest. | 2635 tok/s | chat, coding, long documents |
| Kimi Linear 48B-A3B Moonshot AI | Q4_K_M | offload | 31.1 GB | 78k107k with q8 cache | 34tok/sest. | 1594 tok/s | chat, coding, long documents |
| Qwen3 Coder 30B-A3B Alibaba Qwen | Q4_K_M | offload | 31.3 GB | 118k212k with q8 cache | 7tok/sest. | 615 tok/s | chat, coding |
| Qwen3 32B Alibaba Qwen | Q4_K_M | offload | 52.5 GB | 40k | 2tok/sest. | 131 tok/s | – |
| Mistral Small 3.2 24B Mistral AI | Q4_K_M | offload | 35.4 GB | 97k128k with q8 cache | 4tok/sest. | 361 tok/s | – |
| DeepSeek R1 Distill Qwen 32B DeepSeek | Q4_K_M | offload | 52.5 GB | 41k76k with q8 cache | 2tok/sest. | 175 tok/s | – |
| Qwen3 14B Alibaba Qwen | Q4_K_M | offload | 30.4 GB | 40k | 6tok/sest. | 433 tok/s | chat |
| Phi-4 14B Microsoft | Q4_K_M | offload | 35.3 GB | 16k | 4tok/sest. | 396 tok/s | chat |
| DeepSeek R1 Distill Qwen 14B DeepSeek | Q4_K_M | offload | 34.4 GB | 107k128k with q8 cache | 4tok/sest. | 355 tok/s | chat |
| Devstral Small 2507 Mistral AI | Q4_K_M | offload | 35.4 GB | 97k128k with q8 cache | 4tok/sest. | 364 tok/s | – |
| Magistral Small 2506 Mistral AI | Q4_K_M | offload | 35.4 GB | 40k | 4tok/sest. | 364 tok/s | – |
| Magistral Small 2507 Mistral AI | Q4_K_M | offload | 35.4 GB | 40k | 4tok/sest. | 364 tok/s | – |
| Magistral Small 2509 Mistral AI | Q4_K_M | offload | 35.4 GB | 97k128k with q8 cache | 4tok/sest. | 361 tok/s | – |
| granite 4.2 30b IBM | Q4_K_M | offload | 50.8 GB | 48k88k with q8 cache | 2tok/sest. | 212 tok/s | – |
| DeepSeek V4 Flash DeepSeek | UD-Q2_K_XL | won't run | 103.0 GB | – | – | – | – |
| GLM 5.3 Flash Z.ai | UD-Q2_K_XL | won't run | 104.8 GB | – | – | – | – |
| Qwen 3.8 Flash Next Alibaba Qwen | UD-Q2_K_XL | won't run | 78.6 GB | – | – | – | – |
| MiniMax M2.7 MiniMax | UD-Q2_K_XL | won't run | 103.2 GB | – | – | – | – |
| gpt-oss 120B OpenAI | Q4_K_M | won't run | 65.0 GB | – | – | – | – |
| GLM 4.5 Air Z.ai | Q2_K | won't run | 67.3 GB | – | – | – | – |
| Llama 3.3 70B Meta | Q4_K_M | won't run | 81.7 GB | – | – | – | – |
| DeepSeek R1 DeepSeek | Q4_K_M | won't run | 387.3 GB | – | – | – | – |
| DeepSeek R1 0528 DeepSeek | Q4_K_M | won't run | 387.8 GB | – | – | – | – |
| DeepSeek R1 Distill Llama 70B DeepSeek | Q4_K_M | won't run | 81.7 GB | – | – | – | – |
| DeepSeek V3 0324 DeepSeek | Q4_K_M | won't run | 387.3 GB | – | – | – | – |
| DeepSeek V3.1 DeepSeek | Q4_K_M | won't run | 388.2 GB | – | – | – | – |
| DeepSeek V3.2 DeepSeek | Q4_K_M | won't run | 388.2 GB | – | – | – | – |
| DeepSeek V4 Flash 0731 DeepSeek | MXFP4 | won't run | 158.4 GB | – | – | – | – |
| DeepSeek V4 Flash Vision Exp DeepSeek | MXFP4 | won't run | 167.2 GB | – | – | – | – |
| Devstral 2 123B Instruct 2512 Mistral AI | Q8_0 | won't run | 169.8 GB | – | – | – | – |
| GLM 4.5V Z.ai | Q4_K_M | won't run | 84.3 GB | – | – | – | – |
| GLM 4.6V Z.ai | Q4_K_M | won't run | 90.7 GB | – | – | – | – |
| GLM 4.7 Z.ai | Q4_K_M | won't run | 249.6 GB | – | – | – | – |
| GLM 5.1 Z.ai | MXFP4 | won't run | 433.0 GB | – | – | – | – |
| GLM 5.2 Z.ai | UD-Q4_K_M | won't run | 446.9 GB | – | – | – | – |
| GLM 5.3 Z.ai | Q8_0 | won't run | 759.3 GB | – | – | – | – |
| Kimi K2 Moonshot AI | Q4_K_M | won't run | 588.8 GB | – | – | – | – |
| Kimi K2 Instruct 0905 Moonshot AI | Q4_K_M | won't run | 589.2 GB | – | – | – | – |
| Kimi K2 Thinking Moonshot AI | Q4_K_M | won't run | 589.2 GB | – | – | – | – |
| Kimi K2.5 Moonshot AI | Q4_K_M | won't run | 589.2 GB | – | – | – | – |
| MiniMax M2 MiniMax | Q4_K_M | won't run | 161.9 GB | – | – | – | – |
| MiniMax M2.1 MiniMax | Q4_K_M | won't run | 161.9 GB | – | – | – | – |
| MiniMax M2.5 MiniMax | MXFP4 | won't run | 148.3 GB | – | – | – | – |
| MiniMax M3 MiniMax | UD-Q4_K_M | won't run | 263.8 GB | – | – | – | – |
| Mistral Medium 3.5 128B Mistral AI | Q4_K_M | won't run | 115.8 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 (Q5_K_M) fits fully in its memory at 128k context and generates at about 6 tok/s, an estimate from its memory bandwidth. 38 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 103 GB at 128k 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 128k context. See the best hardware for local AI at every budget.