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 8k context it runs 31 of the 113 open models we track fully in memory; the most capable is gpt-oss 20B at MXFP4, at about 40 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 @8k | Prefill | Good for |
|---|---|---|---|---|---|---|---|
| gpt-oss 20B OpenAI | MXFP4 | fits | 12.3 GB | 106k128k with q8 cache | 40tok/sest. | 3911 tok/s | chat, coding, agents, long documents |
| Gemma 4 12B | Q8_0 | fits | 13.1 GB | 106k171k with q8 cache | 13tok/sest. | 1435 tok/s | – |
| Qwen 3.5 9B Alibaba Qwen | Q8_0 | fits | 10.0 GB | 147k243k with q8 cache | 18tok/sest. | 1817 tok/s | chat |
| Ministral 3 14B Mistral AI | Q6_K | fits | 12.4 GB | 25k45k with q8 cache | 14tok/sest. | 1179 tok/s | – |
| Qwen3 14B Alibaba Qwen | Q6_K | fits | 13.4 GB | 19k35k with q8 cache | 13tok/sest. | 1096 tok/s | – |
| DeepSeek R1 0528 Qwen3 8B DeepSeek | Q8_0 | fits | 10.1 GB | 42k78k with q8 cache | 18tok/sest. | 1918 tok/s | chat |
| Qwen3 8B Alibaba Qwen | Q8_0 | fits | 10.1 GB | 40k | 18tok/sest. | 1918 tok/s | chat |
| Phi-4 14B Microsoft | Q6_K | fits | 13.6 GB | 15k16k with q8 cache | 13tok/sest. | 1103 tok/s | – |
| Qwen 3.5 4B Alibaba Qwen | Q8_0 | fits | 5.3 GB | 256k | 37tok/sest. | 3657 tok/s | chat, coding, long documents |
| Ministral 3 8B Mistral AI | Q8_0 | fits | 10.3 GB | 43k78k with q8 cache | 17tok/sest. | 1792 tok/s | chat |
| Granite 4.2 8B IBM | Q8_0 | fits | 10.8 GB | 35k64k with q8 cache | 17tok/sest. | 1779 tok/s | chat |
| Gemma 4 E4B | Q8_0 | fits | 8.7 GB | 128k | 21tok/sest. | 2202 tok/s | chat |
| LFM2.5 8B-A1B Liquid AI | Q8_0 | fits | 9.6 GB | 113k125k with q8 cache | 72tok/sest. | 12851 tok/s | chat, coding |
| Llama 3.1 8B Meta | Q6_K | fits | 8.0 GB | 63k114k with q8 cache | 23tok/sest. | 1980 tok/s | chat |
| Qwen2.5 0.5B Alibaba Qwen | Q8_0 | fits | 1.4 GB | 32k | 279tok/sest. | 26895 tok/s | chat, coding, long documents |
| Qwen3.5 0.8B Alibaba Qwen | Q8_0 | fits | 1.7 GB | 256k | 189tok/sest. | 18504 tok/s | chat, coding, agents, long documents |
| Qwen3.5 2B Alibaba Qwen | Q5_K_M | fits | 2.3 GB | 256k | 113tok/sest. | 7590 tok/s | chat, coding, agents, long documents |
| DeepSeek R1 Distill Qwen 1.5B DeepSeek | Q8_0 | fits | 2.8 GB | 128k | 83tok/sest. | 8465 tok/s | chat, coding, long documents |
| MiniCPM5 2B openbmb | Q8_0 | fits | 3.7 GB | 128k | 58tok/sest. | 5595 tok/s | chat, coding, agents, long documents |
| LFM2.5 2.6B Liquid AI | Q8_0 | fits | 4.0 GB | 128k | 52tok/sest. | 5632 tok/s | chat, coding, agents, long documents |
| LFM2.5 VL 3B Liquid AI | Q8_0 | fits | 4.0 GB | 32k | 52tok/sest. | 4970 tok/s | chat, coding, agents, long documents |
| granite vision 4.1 4b IBM | Q8_0 | fits | 4.8 GB | 128k | 41tok/sest. | 3727 tok/s | chat, coding |
| granite 4.2 3b IBM | Q8_0 | fits | 5.1 GB | 128k | 39tok/sest. | 4007 tok/s | chat, coding |
| Kimi VL A3B Moonshot AI | mlx-6bit | fits | 14.0 GB | 42k70k with q8 cache | 38tok/sest. | 5220 tok/s | chat, coding |
| Mellum2.1 12B A2.5B Thinking JetBrains | Q8_0 | fits | 13.0 GB | 118k128k with q8 cache | 37tok/sest. | 6192 tok/s | chat, coding, agents, long documents |
| DeepSeek R1 Distill Qwen 7B DeepSeek | Q8_0 | fits | 8.8 GB | 113k128k with q8 cache | 21tok/sest. | 2136 tok/s | chat |
| DeepSeek R1 Distill Llama 8B DeepSeek | Q8_0 | fits | 9.8 GB | 49k89k with q8 cache | 18tok/sest. | 1980 tok/s | chat |
| Ministral 3 8B Reasoning 2512 Mistral AI | Q8_0 | fits | 10.3 GB | 43k78k with q8 cache | 17tok/sest. | 1792 tok/s | chat |
| GLM 4.6V Flash Z.ai | Q8_0 | fits | 10.5 GB | 113k128k with q8 cache | 17tok/sest. | 1549 tok/s | chat |
| GLM 4.1V 9B Thinking Z.ai | mlx-8bit | fits | 12.1 GB | 64k | 15tok/sest. | 1549 tok/s | – |
| DeepSeek R1 Distill Qwen 14B DeepSeek | Q6_K | fits | 13.6 GB | 16k29k with q8 cache | 13tok/sest. | 1074 tok/s | – |
| Qwen 3.8 27B Alibaba Qwen | UD-Q4_K_M | offload | 16.8 GB | – | 8tok/sest. | 592 tok/s | – |
| Qwen 3.5 122B-A10B Alibaba Qwen | UD-Q2_K_XL | offload | 40.1 GB | – | 12tok/sest. | 1033 tok/s | – |
| Qwen 3.6 27B Alibaba Qwen | Q4_K_M | offload | 17.1 GB | – | 8tok/sest. | 585 tok/s | – |
| Gemma 4 31B | Q4_K_M | offload | 19.3 GB | – | 6tok/sest. | 476 tok/s | – |
| Mistral Small 4 119B Mistral AI | UD-Q2_K_XL | offload | 38.5 GB | – | 17tok/sest. | 1371 tok/s | chat |
| Qwen 3.6 35B-A3B Alibaba Qwen | MXFP4 | offload | 21.3 GB | – | 31tok/sest. | 4440 tok/s | chat, coding |
| Qwen 3.5 27B Alibaba Qwen | Q4_K_M | offload | 17.1 GB | – | 8tok/sest. | 587 tok/s | – |
| Gemma 4 26B-A4B | UD-Q4_K_M | offload | 17.0 GB | – | 28tok/sest. | 3845 tok/s | chat, coding |
| Qwen 3.5 35B-A3B Alibaba Qwen | Q4_K_M | offload | 21.6 GB | – | 30tok/sest. | 4408 tok/s | chat, coding |
| GLM 4.7 Flash Z.ai | Q4_K_M | offload | 18.3 GB | – | 22tok/sest. | 2701 tok/s | chat |
| Devstral Small 2 24B Mistral AI | Q4_K_M | offload | 15.4 GB | 6k11k with q8 cache | 11tok/sest. | 704 tok/s | – |
| Nemotron 3.5 Lightning 30B-A3B NVIDIA | UD-Q6_K_XL | offload | 33.5 GB | – | 14tok/sest. | 3735 tok/s | – |
| Qwen3 Coder 30B-A3B Alibaba Qwen | Q4_K_M | offload | 18.9 GB | – | 27tok/sest. | 3431 tok/s | chat |
| Kimi Linear 48B-A3B Moonshot AI | Q4_K_M | offload | 28.9 GB | – | 26tok/sest. | 3629 tok/s | chat, coding, long documents |
| Qwen3 32B Alibaba Qwen | Q4_K_M | offload | 21.3 GB | – | 5tok/sest. | 400 tok/s | – |
| Mistral Small 3.2 24B Mistral AI | Q4_K_M | offload | 15.4 GB | 6k11k with q8 cache | 11tok/sest. | 704 tok/s | – |
| Gemma 3 27B | Q4_K_M | offload | 17.3 GB | – | 8tok/sest. | 590 tok/s | – |
| DeepSeek R1 Distill Qwen 32B DeepSeek | Q4_K_M | offload | 21.3 GB | – | 5tok/sest. | 417 tok/s | – |
| LFM2.5 24B A2B Liquid AI | Q4_K_M | offload | 15.8 GB | 1k2k with q8 cache | 50tok/sest. | 6594 tok/s | chat |
| Devstral Small 2507 Mistral AI | Q4_K_M | offload | 15.4 GB | 6k11k with q8 cache | 11tok/sest. | 716 tok/s | – |
| Magistral Small 2506 Mistral AI | Q4_K_M | offload | 15.4 GB | 6k11k with q8 cache | 11tok/sest. | 716 tok/s | – |
| Magistral Small 2507 Mistral AI | Q4_K_M | offload | 15.4 GB | 6k11k with q8 cache | 11tok/sest. | 716 tok/s | – |
| Magistral Small 2509 Mistral AI | Q4_K_M | offload | 15.4 GB | 6k11k with q8 cache | 11tok/sest. | 704 tok/s | – |
| granite 4.2 30b IBM | Q4_K_M | offload | 19.6 GB | – | 6tok/sest. | 492 tok/s | – |
| DeepSeek V4 Flash DeepSeek | UD-Q2_K_XL | won't run | 91.7 GB | – | – | – | – |
| GLM 5.3 Flash Z.ai | UD-Q2_K_XL | won't run | 102.3 GB | – | – | – | – |
| Qwen 3.8 Flash Next Alibaba Qwen | UD-Q2_K_XL | won't run | 74.6 GB | – | – | – | – |
| MiniMax M2.7 MiniMax | UD-Q2_K_XL | won't run | 72.9 GB | – | – | – | – |
| Nemotron 3 Super 120B-A12B NVIDIA | UD-Q2_K_XL | won't run | 52.0 GB | – | – | – | – |
| Qwen3 Coder Next Alibaba Qwen | Q4_K_M | won't run | 46.3 GB | – | – | – | – |
| gpt-oss 120B OpenAI | Q4_K_M | won't run | 59.6 GB | – | – | – | – |
| GLM 4.5 Air Z.ai | Q2_K | won't run | 44.5 GB | – | – | – | – |
| Qwen3 Next 80B-A3B Alibaba Qwen | Q4_K_M | won't run | 46.3 GB | – | – | – | – |
| Llama 3.3 70B Meta | Q4_K_M | won't run | 42.9 GB | – | – | – | – |
| DeepSeek R1 DeepSeek | Q4_K_M | won't run | 378.0 GB | – | – | – | – |
| DeepSeek R1 0528 DeepSeek | Q4_K_M | won't run | 378.5 GB | – | – | – | – |
| DeepSeek R1 Distill Llama 70B DeepSeek | Q4_K_M | won't run | 42.9 GB | – | – | – | – |
| DeepSeek V3 0324 DeepSeek | Q4_K_M | won't run | 378.0 GB | – | – | – | – |
| DeepSeek V3.1 DeepSeek | Q4_K_M | won't run | 378.9 GB | – | – | – | – |
| DeepSeek V3.2 DeepSeek | Q4_K_M | won't run | 378.9 GB | – | – | – | – |
| DeepSeek V4 Flash 0731 DeepSeek | MXFP4 | won't run | 147.2 GB | – | – | – | – |
| DeepSeek V4 Flash Vision Exp DeepSeek | MXFP4 | won't run | 155.9 GB | – | – | – | – |
| Devstral 2 123B Instruct 2512 Mistral AI | Q8_0 | won't run | 127.3 GB | – | – | – | – |
| GLM 4.5V Z.ai | Q4_K_M | won't run | 61.5 GB | – | – | – | – |
| GLM 4.6V Z.ai | Q4_K_M | won't run | 67.9 GB | – | – | – | – |
| GLM 4.7 Z.ai | Q4_K_M | won't run | 205.3 GB | – | – | – | – |
| GLM 5.1 Z.ai | MXFP4 | won't run | 421.5 GB | – | – | – | – |
| GLM 5.2 Z.ai | UD-Q4_K_M | won't run | 435.4 GB | – | – | – | – |
| GLM 5.3 Z.ai | Q8_0 | won't run | 747.9 GB | – | – | – | – |
| Kimi K2 Moonshot AI | Q4_K_M | won't run | 579.5 GB | – | – | – | – |
| Kimi K2 Instruct 0905 Moonshot AI | Q4_K_M | won't run | 580.0 GB | – | – | – | – |
| Kimi K2 Thinking Moonshot AI | Q4_K_M | won't run | 580.0 GB | – | – | – | – |
| Kimi K2.5 Moonshot AI | Q4_K_M | won't run | 580.0 GB | – | – | – | – |
| MiniMax M2 MiniMax | Q4_K_M | won't run | 131.6 GB | – | – | – | – |
| MiniMax M2.1 MiniMax | Q4_K_M | won't run | 131.6 GB | – | – | – | – |
| MiniMax M2.5 MiniMax | MXFP4 | won't run | 118.1 GB | – | – | – | – |
| MiniMax M3 MiniMax | UD-Q4_K_M | won't run | 248.5 GB | – | – | – | – |
| Mistral Medium 3.5 128B Mistral AI | Q4_K_M | won't run | 73.4 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?
- gpt-oss 20B (MXFP4) fits fully in its memory at 8k context and generates at about 40 tok/s, an estimate from its memory bandwidth. 31 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 92 GB at 8k 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 8k context. See the best hardware for local AI at every budget.