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 26 of the 113 open models we track fully in memory; the most capable is gpt-oss 20B at MXFP4, at about 26 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 | 5 h ago | Buy |
| AmazonSapphire Pure AMD Radeon RX 9060 XT 16GB GDDR6 Gaming GPU | New | $820 | In stock | 1 h ago | Buy |
| AmazonASUS AMD Dual Radeon RX 9060 XT 16GB GDDR6 Video Card DUAL-RX9060XT-16G | New | $900 | In stock | 1 h ago | Buy |
| AmazonASUS Dual Radeon RX 9060 XT 16GB GDDR6 Gaming Graphics Card | New | $937 | In stock | 1 h ago | Buy |
| AmazonXFX Swift AMD Radeon RX 9060 XT OC Triple Fan Gaming Edition with 16GB GDDR6… | New | $968 | In stock | 1 h ago | Buy |
| AmazonASUS Prime Radeon RX 9060 XT 16GB GDDR6 OC Edition Gaming Graphics Card | New | $980 | In stock | 1 h ago | Buy |
| AmazonSapphire Technology Nitro+ AMD Radeon RX 9060 XT Gaming OC | New | $1,000 | In stock | 1 h ago | Buy |
| AmazonGigabyte Radeon RX 9060 XT Gaming OC 16G Graphics Card | New | $1,100 | In stock | 1 h ago | Buy |
| AmazonASUS Dual Radeon RX 9060 XT 16GB GDDR6 White Edition | New | $1,140 | In stock | 1 h ago | Buy |
| AmazonASUS AMD Radeon RX 9060 XT Video Card 16GB GDDR6 OC Edition… | New | $2,181 | In stock | 1 h ago | Buy |
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 @64k | Prefill | Good for |
|---|---|---|---|---|---|---|---|
| gpt-oss 20B OpenAI | MXFP4 | fits | 14.0 GB | 106k128k with q8 cache | 26tok/sest. | 2427 tok/s | chat, coding, agents, long documents |
| Gemma 4 12B | Q8_0 | fits | 14.3 GB | 106k171k with q8 cache | 13tok/sest. | 1248 tok/s | – |
| Qwen 3.5 9B Alibaba Qwen | Q8_0 | fits | 12.1 GB | 147k243k with q8 cache | 15tok/sest. | 1528 tok/s | chat |
| DeepSeek R1 0528 Qwen3 8B DeepSeek | Q4_K_M | fits | 14.9 GB | 66k120k with q8 cache | 12tok/sest. | 1010 tok/s | chat |
| Qwen3 8B Alibaba Qwen | Q4_K_M | fits | 14.9 GB | 40k | 12tok/sest. | 1010 tok/s | chat |
| Qwen 3.5 4B Alibaba Qwen | Q8_0 | fits | 7.4 GB | 256k | 26tok/sest. | 2648 tok/s | chat, coding, long documents |
| Ministral 3 8B Mistral AI | Q4_K_M | fits | 14.5 GB | 68k125k with q8 cache | 12tok/sest. | 999 tok/s | chat |
| Gemma 4 E4B | Q8_0 | fits | 10.6 GB | 128k | 17tok/sest. | 2001 tok/s | chat |
| LFM2.5 8B-A1B Liquid AI | Q8_0 | fits | 12.6 GB | 113k125k with q8 cache | 25tok/sest. | 4270 tok/s | chat, coding |
| Llama 3.1 8B Meta | Q5_K_M | fits | 14.5 GB | 69k125k with q8 cache | 12tok/sest. | 1085 tok/s | chat |
| Qwen2.5 0.5B Alibaba Qwen | Q8_0 | fits | 2.4 GB | 32k | 132tok/sest. | 9470 tok/s | chat, coding, long documents |
| Qwen3.5 0.8B Alibaba Qwen | Q8_0 | fits | 2.7 GB | 256k | 108tok/sest. | 10737 tok/s | chat, coding, agents, long documents |
| Qwen3.5 2B Alibaba Qwen | Q5_K_M | fits | 3.3 GB | 256k | 78tok/sest. | 5854 tok/s | chat, coding, agents, long documents |
| DeepSeek R1 Distill Qwen 1.5B DeepSeek | Q8_0 | fits | 4.7 GB | 128k | 47tok/sest. | 3922 tok/s | chat, coding, long documents |
| MiniCPM5 2B openbmb | Q8_0 | fits | 6.3 GB | 128k | 32tok/sest. | 2211 tok/s | chat, coding, agents, long documents |
| Mellum2.1 12B A2.5B Thinking JetBrains | Q8_0 | fits | 14.1 GB | 118k128k with q8 cache | 29tok/sest. | 3957 tok/s | chat, coding, agents, long documents |
| LFM2.5 2.6B Liquid AI | Q8_0 | fits | 7.6 GB | 128k | 26tok/sest. | 2681 tok/s | chat, coding, agents, long documents |
| LFM2.5 VL 3B Liquid AI | Q8_0 | fits | 7.6 GB | 32k | 26tok/sest. | 2521 tok/s | chat, coding, agents, long documents |
| Kimi VL A3B Moonshot AI | mlx-5bit | fits | 14.1 GB | 95k128k with q8 cache | 25tok/sest. | 2721 tok/s | chat, coding, agents |
| granite vision 4.1 4b IBM | Q8_0 | fits | 9.5 GB | 128k | 20tok/sest. | 1683 tok/s | chat, coding |
| granite 4.2 3b IBM | Q8_0 | fits | 9.8 GB | 128k | 19tok/sest. | 1738 tok/s | chat, coding |
| DeepSeek R1 Distill Qwen 7B DeepSeek | Q8_0 | fits | 12.2 GB | 113k128k with q8 cache | 15tok/sest. | 1270 tok/s | chat |
| GLM 4.6V Flash Z.ai | Q8_0 | fits | 13.0 GB | 113k128k with q8 cache | 14tok/sest. | 857 tok/s | chat |
| DeepSeek R1 Distill Llama 8B DeepSeek | Q5_K_M | fits | 14.5 GB | 69k125k with q8 cache | 12tok/sest. | 1085 tok/s | chat |
| Ministral 3 8B Reasoning 2512 Mistral AI | Q4_K_M | fits | 14.5 GB | 68k125k with q8 cache | 12tok/sest. | 999 tok/s | chat |
| GLM 4.1V 9B Thinking Z.ai | mlx-8bit | fits | 14.6 GB | 64k | 12tok/sest. | 857 tok/s | – |
| Qwen 3.8 27B Alibaba Qwen | UD-Q4_K_M | offload | 20.6 GB | – | 5tok/sest. | 438 tok/s | – |
| Qwen 3.6 27B Alibaba Qwen | Q4_K_M | offload | 21.0 GB | – | 5tok/sest. | 435 tok/s | – |
| Gemma 4 31B | Q4_K_M | offload | 24.0 GB | – | 4tok/sest. | 370 tok/s | – |
| Mistral Small 4 119B Mistral AI | UD-Q2_K_XL | offload | 40.0 GB | – | 11tok/sest. | 665 tok/s | chat |
| Qwen 3.6 35B-A3B Alibaba Qwen | MXFP4 | offload | 22.7 GB | – | 18tok/sest. | 2508 tok/s | chat, coding |
| Qwen 3.5 27B Alibaba Qwen | Q4_K_M | offload | 20.9 GB | – | 5tok/sest. | 435 tok/s | – |
| Gemma 4 26B-A4B | UD-Q4_K_M | offload | 18.4 GB | – | 18tok/sest. | 2884 tok/s | chat, coding |
| Qwen 3.5 35B-A3B Alibaba Qwen | Q4_K_M | offload | 23.0 GB | – | 18tok/sest. | 2492 tok/s | chat, coding |
| GLM 4.7 Flash Z.ai | Q4_K_M | offload | 21.5 GB | – | 9tok/sest. | 1279 tok/s | chat |
| Devstral Small 2 24B Mistral AI | Q4_K_M | offload | 24.5 GB | 6k11k with q8 cache | 4tok/sest. | 394 tok/s | – |
| Nemotron 3.5 Lightning 30B-A3B NVIDIA | UD-Q6_K_XL | offload | 34.2 GB | – | 12tok/sest. | 2560 tok/s | – |
| Kimi Linear 48B-A3B Moonshot AI | Q4_K_M | offload | 29.7 GB | – | 20tok/sest. | 1770 tok/s | chat, coding, long documents |
| Qwen3 Coder 30B-A3B Alibaba Qwen | Q4_K_M | offload | 24.5 GB | – | 7tok/sest. | 864 tok/s | chat |
| Qwen3 32B Alibaba Qwen | Q4_K_M | offload | 35.6 GB | – | 2tok/sest. | 173 tok/s | – |
| Gemma 3 27B | Q4_K_M | offload | 22.0 GB | – | 5tok/sest. | 475 tok/s | – |
| Mistral Small 3.2 24B Mistral AI | Q4_K_M | offload | 24.5 GB | 6k11k with q8 cache | 4tok/sest. | 394 tok/s | – |
| DeepSeek R1 Distill Qwen 32B DeepSeek | Q4_K_M | offload | 35.7 GB | – | 2tok/sest. | 214 tok/s | – |
| Ministral 3 14B Mistral AI | Q4_K_M | offload | 18.8 GB | 41k75k with q8 cache | 6tok/sest. | 640 tok/s | chat |
| Qwen3 14B Alibaba Qwen | Q4_K_M | offload | 19.6 GB | 37k40k with q8 cache | 6tok/sest. | 549 tok/s | chat |
| Phi-4 14B Microsoft | Q4_K_M | offload | 22.0 GB | 16k | 5tok/sest. | 517 tok/s | chat |
| Granite 4.2 8B IBM | Q4_K_M | offload | 16.3 GB | 57k103k with q8 cache | 9tok/sest. | 882 tok/s | chat |
| LFM2.5 24B A2B Liquid AI | Q4_K_M | offload | 20.5 GB | 1k2k with q8 cache | 10tok/sest. | 2045 tok/s | chat |
| DeepSeek R1 Distill Qwen 14B DeepSeek | Q4_K_M | offload | 21.5 GB | 31k57k with q8 cache | 5tok/sest. | 475 tok/s | chat |
| Devstral Small 2507 Mistral AI | Q4_K_M | offload | 24.5 GB | 6k11k with q8 cache | 4tok/sest. | 400 tok/s | – |
| Magistral Small 2506 Mistral AI | Q4_K_M | offload | 24.5 GB | 6k11k with q8 cache | 4tok/sest. | 400 tok/s | – |
| Magistral Small 2507 Mistral AI | Q4_K_M | offload | 24.5 GB | 6k11k with q8 cache | 4tok/sest. | 400 tok/s | – |
| Magistral Small 2509 Mistral AI | Q4_K_M | offload | 24.5 GB | 6k11k with q8 cache | 4tok/sest. | 394 tok/s | – |
| granite 4.2 30b IBM | Q4_K_M | offload | 34.0 GB | – | 2tok/sest. | 255 tok/s | – |
| DeepSeek V4 Flash DeepSeek | UD-Q2_K_XL | won't run | 96.7 GB | – | – | – | – |
| GLM 5.3 Flash Z.ai | UD-Q2_K_XL | won't run | 103.2 GB | – | – | – | – |
| Qwen 3.8 Flash Next Alibaba Qwen | UD-Q2_K_XL | won't run | 76.2 GB | – | – | – | – |
| MiniMax M2.7 MiniMax | UD-Q2_K_XL | won't run | 86.8 GB | – | – | – | – |
| Qwen 3.5 122B-A10B Alibaba Qwen | UD-Q2_K_XL | won't run | 41.8 GB | – | – | – | – |
| Nemotron 3 Super 120B-A12B NVIDIA | UD-Q2_K_XL | won't run | 52.7 GB | – | – | – | – |
| Qwen3 Coder Next Alibaba Qwen | Q4_K_M | won't run | 47.9 GB | – | – | – | – |
| gpt-oss 120B OpenAI | Q4_K_M | won't run | 61.9 GB | – | – | – | – |
| GLM 4.5 Air Z.ai | Q2_K | won't run | 54.9 GB | – | – | – | – |
| Qwen3 Next 80B-A3B Alibaba Qwen | Q4_K_M | won't run | 47.9 GB | – | – | – | – |
| Llama 3.3 70B Meta | Q4_K_M | won't run | 60.8 GB | – | – | – | – |
| DeepSeek R1 DeepSeek | Q4_K_M | won't run | 382.1 GB | – | – | – | – |
| DeepSeek R1 0528 DeepSeek | Q4_K_M | won't run | 382.6 GB | – | – | – | – |
| DeepSeek R1 Distill Llama 70B DeepSeek | Q4_K_M | won't run | 60.8 GB | – | – | – | – |
| DeepSeek V3 0324 DeepSeek | Q4_K_M | won't run | 382.1 GB | – | – | – | – |
| DeepSeek V3.1 DeepSeek | Q4_K_M | won't run | 383.0 GB | – | – | – | – |
| DeepSeek V3.2 DeepSeek | Q4_K_M | won't run | 383.0 GB | – | – | – | – |
| DeepSeek V4 Flash 0731 DeepSeek | MXFP4 | won't run | 152.2 GB | – | – | – | – |
| DeepSeek V4 Flash Vision Exp DeepSeek | MXFP4 | won't run | 161.0 GB | – | – | – | – |
| Devstral 2 123B Instruct 2512 Mistral AI | Q8_0 | won't run | 146.9 GB | – | – | – | – |
| GLM 4.5V Z.ai | Q4_K_M | won't run | 71.9 GB | – | – | – | – |
| GLM 4.6V Z.ai | Q4_K_M | won't run | 78.3 GB | – | – | – | – |
| GLM 4.7 Z.ai | Q4_K_M | won't run | 225.8 GB | – | – | – | – |
| GLM 5.1 Z.ai | MXFP4 | won't run | 426.6 GB | – | – | – | – |
| GLM 5.2 Z.ai | UD-Q4_K_M | won't run | 440.5 GB | – | – | – | – |
| GLM 5.3 Z.ai | Q8_0 | won't run | 753.0 GB | – | – | – | – |
| Kimi K2 Moonshot AI | Q4_K_M | won't run | 583.6 GB | – | – | – | – |
| Kimi K2 Instruct 0905 Moonshot AI | Q4_K_M | won't run | 584.0 GB | – | – | – | – |
| Kimi K2 Thinking Moonshot AI | Q4_K_M | won't run | 584.0 GB | – | – | – | – |
| Kimi K2.5 Moonshot AI | Q4_K_M | won't run | 584.0 GB | – | – | – | – |
| MiniMax M2 MiniMax | Q4_K_M | won't run | 145.5 GB | – | – | – | – |
| MiniMax M2.1 MiniMax | Q4_K_M | won't run | 145.5 GB | – | – | – | – |
| MiniMax M2.5 MiniMax | MXFP4 | won't run | 131.9 GB | – | – | – | – |
| MiniMax M3 MiniMax | UD-Q4_K_M | won't run | 255.4 GB | – | – | – | – |
| Mistral Medium 3.5 128B Mistral AI | Q4_K_M | won't run | 92.9 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?
- gpt-oss 20B (MXFP4) fits fully in its memory at 64k context and generates at about 26 tok/s, an estimate from its memory bandwidth. 26 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.