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 32k context it runs 52 of the 113 open models we track fully in memory; the most capable is Qwen 3.8 27B at Q6_K, at about 7 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 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 @32k | Prefill | Good for |
|---|---|---|---|---|---|---|---|
| Qwen 3.8 27B Alibaba Qwen | Q6_K | fits | 25.9 GB | 98k172k with q8 cache | 7tok/sest. | 581 tok/s | – |
| Qwen 3.6 27B Alibaba Qwen | Q8_0 | fits | 30.3 GB | 34k59k with q8 cache | 6tok/sest. | 581 tok/s | – |
| Gemma 4 31B | Q6_K | fits | 28.2 GB | 57k110k with q8 cache | 6tok/sest. | 518 tok/s | – |
| Qwen 3.6 35B-A3B Alibaba Qwen | UD-Q6_K | fits | 29.5 GB | 68k107k with q8 cache | 33tok/sest. | 4151 tok/s | chat, coding, agents, long documents |
| Qwen 3.5 27B Alibaba Qwen | Q8_0 | fits | 30.3 GB | 34k59k with q8 cache | 6tok/sest. | 581 tok/s | – |
| Gemma 4 26B-A4B | Q8_0 | fits | 27.3 GB | 153k245k with q8 cache | 21tok/sest. | 3693 tok/s | chat, coding |
| Qwen 3.5 35B-A3B Alibaba Qwen | Q6_K | fits | 29.0 GB | 85k133k with q8 cache | 34tok/sest. | 4151 tok/s | chat, coding, agents, long documents |
| GLM 4.7 Flash Z.ai | Q6_K | fits | 26.1 GB | 106k183k with q8 cache | 18tok/sest. | 2167 tok/s | chat |
| Devstral Small 2 24B Mistral AI | Q8_0 | fits | 29.8 GB | 35k64k with q8 cache | 6tok/sest. | 614 tok/s | – |
| Kimi Linear 48B-A3B Moonshot AI | Q4_K_M | fits | 29.8 GB | 78k107k with q8 cache | 49tok/sest. | 3578 tok/s | chat, coding, long documents |
| gpt-oss 20B OpenAI | MXFP4 | fits | 13.5 GB | 128k | 33tok/sest. | 3099 tok/s | chat, coding, agents, long documents |
| Qwen3 Coder 30B-A3B Alibaba Qwen | Q6_K | fits | 27.9 GB | 57k102k with q8 cache | 19tok/sest. | 1909 tok/s | chat |
| Qwen3 32B Alibaba Qwen | Q4_K_M | fits | 27.9 GB | 40k | 6tok/sest. | 361 tok/s | – |
| Gemma 4 12B | Q8_0 | fits | 14.1 GB | 256k | 13tok/sest. | 1348 tok/s | – |
| Qwen 3.5 9B Alibaba Qwen | Q8_0 | fits | 11.4 GB | 256k | 17tok/sest. | 1680 tok/s | chat |
| Gemma 3 27B | Q6_K | fits | 25.0 GB | 95k128k with q8 cache | 7tok/sest. | 617 tok/s | – |
| DeepSeek R1 Distill Qwen 32B DeepSeek | Q4_K_M | fits | 28.0 GB | 41k76k with q8 cache | 6tok/sest. | 414 tok/s | – |
| Mistral Small 3.2 24B Mistral AI | Q8_0 | fits | 29.8 GB | 35k64k with q8 cache | 6tok/sest. | 614 tok/s | – |
| Ministral 3 14B Mistral AI | Q8_0 | fits | 19.9 GB | 97k176k with q8 cache | 9tok/sest. | 933 tok/s | – |
| Qwen3 14B Alibaba Qwen | Q8_0 | fits | 21.1 GB | 40k | 8tok/sest. | 839 tok/s | – |
| DeepSeek R1 0528 Qwen3 8B DeepSeek | Q8_0 | fits | 14.1 GB | 128k | 13tok/sest. | 1384 tok/s | chat |
| Qwen3 8B Alibaba Qwen | Q8_0 | fits | 14.1 GB | 40k | 13tok/sest. | 1384 tok/s | chat |
| Phi-4 14B Microsoft | Q8_0 | fits | 22.2 GB | 16k | 8tok/sest. | 844 tok/s | – |
| Qwen 3.5 4B Alibaba Qwen | Q8_0 | fits | 6.7 GB | 256k | 31tok/sest. | 3144 tok/s | chat, coding, long documents |
| Ministral 3 8B Mistral AI | Q8_0 | fits | 14.1 GB | 149k256k with q8 cache | 13tok/sest. | 1337 tok/s | chat |
| Granite 4.2 8B IBM | Q8_0 | fits | 15.2 GB | 125k128k with q8 cache | 12tok/sest. | 1273 tok/s | chat |
| Gemma 4 E4B | Q8_0 | fits | 10.0 GB | 128k | 19tok/sest. | 2111 tok/s | chat |
| LFM2.5 8B-A1B Liquid AI | Q8_0 | fits | 11.4 GB | 125k | 40tok/sest. | 6905 tok/s | chat, coding |
| Llama 3.1 8B Meta | Q6_K | fits | 11.6 GB | 128k | 16tok/sest. | 1462 tok/s | chat |
| Qwen2.5 0.5B Alibaba Qwen | Q8_0 | fits | 2.4 GB | 32k | 189tok/sest. | 15037 tok/s | chat, coding, long documents |
| Qwen3.5 0.8B Alibaba Qwen | Q8_0 | fits | 2.6 GB | 256k | 143tok/sest. | 14125 tok/s | chat, coding, agents, long documents |
| Qwen3.5 2B Alibaba Qwen | Q5_K_M | fits | 3.2 GB | 256k | 95tok/sest. | 6734 tok/s | chat, coding, agents, long documents |
| DeepSeek R1 Distill Qwen 1.5B DeepSeek | Q8_0 | fits | 4.1 GB | 128k | 62tok/sest. | 5657 tok/s | chat, coding, long documents |
| MiniCPM5 2B openbmb | Q8_0 | fits | 5.3 GB | 128k | 43tok/sest. | 3378 tok/s | chat, coding, agents, long documents |
| LFM2.5 2.6B Liquid AI | Q8_0 | fits | 6.0 GB | 128k | 36tok/sest. | 3827 tok/s | chat, coding, agents, long documents |
| LFM2.5 VL 3B Liquid AI | Q8_0 | fits | 6.0 GB | 32k | 36tok/sest. | 3509 tok/s | chat, coding, agents, long documents |
| Mellum2.1 12B A2.5B Thinking JetBrains | Q8_0 | fits | 14.0 GB | 128k | 33tok/sest. | 4985 tok/s | chat, coding, agents, long documents |
| granite vision 4.1 4b IBM | Q8_0 | fits | 7.4 GB | 128k | 28tok/sest. | 2452 tok/s | chat, coding |
| granite 4.2 3b IBM | Q8_0 | fits | 7.6 GB | 128k | 27tok/sest. | 2569 tok/s | chat, coding |
| Kimi VL A3B Moonshot AI | mlx-8bit | fits | 19.0 GB | 128k | 25tok/sest. | 3746 tok/s | chat, coding |
| LFM2.5 24B A2B Liquid AI | Q8_0 | fits | 27.7 GB | 63k113k with q8 cache | 22tok/sest. | 3848 tok/s | chat, coding |
| DeepSeek R1 Distill Qwen 7B DeepSeek | Q8_0 | fits | 10.8 GB | 128k | 18tok/sest. | 1653 tok/s | chat |
| GLM 4.6V Flash Z.ai | Q8_0 | fits | 12.1 GB | 128k | 16tok/sest. | 1151 tok/s | chat |
| DeepSeek R1 Distill Llama 8B DeepSeek | Q8_0 | fits | 13.4 GB | 128k | 14tok/sest. | 1462 tok/s | chat |
| GLM 4.1V 9B Thinking Z.ai | mlx-8bit | fits | 13.7 GB | 64k | 13tok/sest. | 1151 tok/s | – |
| Ministral 3 8B Reasoning 2512 Mistral AI | Q8_0 | fits | 14.1 GB | 149k256k with q8 cache | 13tok/sest. | 1337 tok/s | chat |
| DeepSeek R1 Distill Qwen 14B DeepSeek | Q8_0 | fits | 22.1 GB | 74k128k with q8 cache | 8tok/sest. | 790 tok/s | – |
| granite 4.2 30b IBM | Q5_K_M | fits | 29.1 GB | 37k68k with q8 cache | 6tok/sest. | 476 tok/s | – |
| Devstral Small 2507 Mistral AI | Q8_0 | fits | 29.8 GB | 35k64k with q8 cache | 6tok/sest. | 623 tok/s | – |
| Magistral Small 2506 Mistral AI | Q8_0 | fits | 29.8 GB | 35k40k with q8 cache | 6tok/sest. | 623 tok/s | – |
| Magistral Small 2507 Mistral AI | Q8_0 | fits | 29.8 GB | 35k40k with q8 cache | 6tok/sest. | 623 tok/s | – |
| Magistral Small 2509 Mistral AI | Q8_0 | fits | 29.8 GB | 35k64k with q8 cache | 6tok/sest. | 614 tok/s | – |
| Qwen 3.5 122B-A10B Alibaba Qwen | UD-Q2_K_XL | offload | 41.3 GB | – | 15tok/sest. | 1139 tok/s | chat |
| Qwen3 Coder Next Alibaba Qwen | Q4_K_M | offload | 47.5 GB | – | 18tok/sest. | 2553 tok/s | chat |
| Nemotron 3 Super 120B-A12B NVIDIA | UD-Q2_K_XL | offload | 52.8 GB | – | 9tok/sest. | 1018 tok/s | – |
| Mistral Small 4 119B Mistral AI | UD-Q2_K_XL | offload | 39.6 GB | – | 22tok/sest. | 1296 tok/s | chat, coding |
| GLM 4.5 Air Z.ai | Q2_K | offload | 49.4 GB | – | 4tok/sest. | 386 tok/s | – |
| Qwen3 Next 80B-A3B Alibaba Qwen | Q4_K_M | offload | 47.5 GB | – | 21tok/sest. | 3038 tok/s | chat, coding |
| Nemotron 3.5 Lightning 30B-A3B NVIDIA | UD-Q6_K_XL | offload | 34.3 GB | – | 23tok/sest. | 4397 tok/s | chat, coding, long documents |
| Llama 3.3 70B Meta | Q4_K_M | offload | 51.1 GB | – | 2tok/sest. | 147 tok/s | – |
| DeepSeek R1 Distill Llama 70B DeepSeek | Q4_K_M | offload | 51.1 GB | – | 2tok/sest. | 147 tok/s | – |
| DeepSeek V4 Flash DeepSeek | UD-Q2_K_XL | won't run | 94.4 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 | 75.8 GB | – | – | – | – |
| MiniMax M2.7 MiniMax | UD-Q2_K_XL | won't run | 79.4 GB | – | – | – | – |
| gpt-oss 120B OpenAI | Q4_K_M | won't run | 61.1 GB | – | – | – | – |
| DeepSeek R1 DeepSeek | Q4_K_M | won't run | 380.3 GB | – | – | – | – |
| DeepSeek R1 0528 DeepSeek | Q4_K_M | won't run | 380.8 GB | – | – | – | – |
| DeepSeek V3 0324 DeepSeek | Q4_K_M | won't run | 380.3 GB | – | – | – | – |
| DeepSeek V3.1 DeepSeek | Q4_K_M | won't run | 381.2 GB | – | – | – | – |
| DeepSeek V3.2 DeepSeek | Q4_K_M | won't run | 381.2 GB | – | – | – | – |
| DeepSeek V4 Flash 0731 DeepSeek | MXFP4 | won't run | 149.8 GB | – | – | – | – |
| DeepSeek V4 Flash Vision Exp DeepSeek | MXFP4 | won't run | 158.6 GB | – | – | – | – |
| Devstral 2 123B Instruct 2512 Mistral AI | Q8_0 | won't run | 136.2 GB | – | – | – | – |
| GLM 4.5V Z.ai | Q4_K_M | won't run | 66.5 GB | – | – | – | – |
| GLM 4.6V Z.ai | Q4_K_M | won't run | 72.8 GB | – | – | – | – |
| GLM 4.7 Z.ai | Q4_K_M | won't run | 214.6 GB | – | – | – | – |
| GLM 5.1 Z.ai | MXFP4 | won't run | 424.2 GB | – | – | – | – |
| GLM 5.2 Z.ai | UD-Q4_K_M | won't run | 438.1 GB | – | – | – | – |
| GLM 5.3 Z.ai | Q8_0 | won't run | 750.6 GB | – | – | – | – |
| Kimi K2 Moonshot AI | Q4_K_M | won't run | 581.8 GB | – | – | – | – |
| Kimi K2 Instruct 0905 Moonshot AI | Q4_K_M | won't run | 582.2 GB | – | – | – | – |
| Kimi K2 Thinking Moonshot AI | Q4_K_M | won't run | 582.2 GB | – | – | – | – |
| Kimi K2.5 Moonshot AI | Q4_K_M | won't run | 582.2 GB | – | – | – | – |
| MiniMax M2 MiniMax | Q4_K_M | won't run | 138.1 GB | – | – | – | – |
| MiniMax M2.1 MiniMax | Q4_K_M | won't run | 138.1 GB | – | – | – | – |
| MiniMax M2.5 MiniMax | MXFP4 | won't run | 124.5 GB | – | – | – | – |
| MiniMax M3 MiniMax | UD-Q4_K_M | won't run | 252.0 GB | – | – | – | – |
| Mistral Medium 3.5 128B Mistral AI | Q4_K_M | won't run | 82.2 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 32k context and generates at about 7 tok/s, an estimate from its memory bandwidth. 52 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 94 GB at 32k 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 32k context. See the best hardware for local AI at every budget.