The RX 9060 XT 16GB has 16GB of GDDR6 at 320 GB/s. That fits dense models up to 14B at Q4, with an 8B model at est. 45 tok/s, typical error 10% and a 14B model at est. 28 tok/s. Ollama runs it through ROCm on Linux, where the 9060 XT is on its support list; its Windows ROCm list does not include the card, so on Windows Ollama uses its Vulkan backend. This page covers the 16GB card.
The best local LLM for the RX 9060 XT is Mellum2.1 12B-A2.5B Thinking (est. 83 tok/s, typical error 25%) on its 16GB VRAM. It uses ~8.1GB of VRAM; the RX 9060 XT handles dense models up to ~14B at Q4. A 14B model at Q4 runs at est. 28 tok/s, typical error 10%. (ModelFit, 2026)
Sizing rule: a Q4 model needs about 0.6 GB of VRAM per billion parameters, and ModelFit budgets 90% of this card's 16GB for weights, context, and KV-cache. The per-size table below uses that same budget. Other strong fits: Gemma 4 12B (12B, ~8GB) and Qwen3.5 9B Instruct (Q8) (9B, ~10.7GB). On this card Mellum2.1 12B-A2.5B Thinking runs at est. 83 tok/s, typical error 25%. What it will not run: a 32B model does not fit in VRAM alone, so it spills to system RAM over PCIe and slows sharply.
Speeds are ModelFit estimates from memory bandwidth and model size, not measured benchmarks. Typical error: the median gap between the ModelFit speed model and measured runs on hardware the model was not fitted on, for this backend and model size.
Cite this page: ModelFit, RX 9060 XT 16GB Local LLM: 14B at est. 28 tok/s, ROCm on Linux, https://modelfit.io/gpu/rx-9060-xt/, updated October 2026, CC BY 4.0.
| Model size | Est. speed | Fit on 16GB |
|---|---|---|
| 7B | est. 51 tok/stypical error 10% | Fits in VRAM |
| 14B | est. 28 tok/stypical error 10% | Fits in VRAM |
| 20B MoE (3.6B active) | est. 59 tok/stypical error 25% | Fits in VRAM |
| 32B | est. 3 tok/srough estimate, offload not measured | CPU offload (slow) |
| 35B MoE (3B active) | est. 24 tok/srough estimate, offload not measured | CPU offload (slow) |
| 70B | est. 1 tok/srough estimate, offload not measured | CPU offload (slow) |
| 120B MoE (5.1B active) | est. 15 tok/srough estimate, offload not measured | CPU offload (slow) |
Decode speed, generic Q4_K_M model of each size at 4K context, 320 GB/s, typical error 10-25%
ModelFit estimates, not measured benchmarks: decode speed of a generic Q4_K_M model of each size at 4K context on the RX 9060 XT's 320 GB/s bandwidth. MoE rows scale by active parameters (decode reads only the active experts), so a 35B MoE runs far faster than a dense 32B. "CPU offload" sizes exceed the 16GB VRAM; dense models slow to a crawl there, MoE models degrade less because hot experts stay GPU-resident. Offload rows are rough estimates: no measured run covers offload, so they carry no typical error. What typical error means.
Mellum2.1 12B-A2.5B Thinking loads ~8.1 GB of weights; at 16k context the KV cache adds ~0.2 GB (still fits the ~14 GB usable VRAM), and at 64k it adds ~0.9 GB (still fits).
The model leaves about 6.1 GB of the usable VRAM budget free after weights and 16k context.
KV-cache figures assume an fp16 cache, the llama.cpp/Ollama default. Standard GQA models use a size-class estimate (8 KV heads x 128 head dim class); hybrid linear-attention models (Qwen3.5/3.6, Qwen3-Next) use the exact per-token cost from their published config, since only their sparse full-attention layers cache KV. A q8_0 KV cache roughly halves either figure. Estimates, not measurements.
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ModelFit budgets about 14.4GB of the 16GB for weights and context: 14B models at Q4 (~11GB) fit with room for context, and 20B-class mixture-of-experts models load too. Bandwidth is the trade-off: 320 GB/s sits near the RTX 4060 Ti 16GB (288 GB/s) and at half the RX 9070 XT (640 GB/s, est. 79 tok/s, typical error 10% on 8B). Dense 27B and 32B models spill into system RAM.
| Hardware | Memory | Speed | Bandwidth |
|---|---|---|---|
| RTX 4060 Ti | 16 GB | est. 43 tok/stypical error 5% | 288 GB/s |
| RX 9060 XT | 16 GB | est. 45 tok/stypical error 10% | 320 GB/s |
| RTX 5060 Ti | 16 GB | est. 68 tok/stypical error 5% | 448 GB/s |
| RX 9070 XT | 16 GB | est. 79 tok/stypical error 10% | 640 GB/s |
| RX 7900 XT | 20 GB | est. 91 tok/stypical error 10% | 800 GB/s |
Best for: Agentic coding, Small machines·Pop: 76/100
Perf: est. 83 tok/s, typical error 25% · first token ~0.2s
Fits in 16 GB VRAM with room to spare. Best for agentic coding, small machines on RX 9060 XT.
Best for: Chat, Coding, Multimodal·Pop: 80/100
Perf: est. 32 tok/s, typical error 10% · first token ~0.6s
Fits in 16 GB VRAM with room to spare. Best for chat, coding, multimodal on RX 9060 XT.
Best for: Quality, Coding, Reasoning·Pop: 86/100
Perf: est. 31 tok/s, typical error 10% · first token ~1.7s
Fits in 16 GB VRAM with room to spare. Best for quality, coding, reasoning on RX 9060 XT.
Best for: Voice assistants, Tool calling, Chat·Pop: 75/100
Perf: est. 60 tok/s, typical error 10% · first token ~0.4s
Fits in 16 GB VRAM with room to spare. Best for voice assistants, tool calling, chat on RX 9060 XT.
Best for: On-device, Mobile, Chat·Pop: 82/100
Perf: est. 48 tok/s, typical error 10% · first token ~1.5s
Fits in 16 GB VRAM with room to spare. Best for on-device, mobile, chat on RX 9060 XT.
Best for: Chat, Coding, Multimodal·Pop: 80/100
Perf: est. 20 tok/s, typical error 10% · first token ~2.4s
Fits in 16 GB VRAM with room to spare. Best for chat, coding, multimodal on RX 9060 XT.
Best for: Local AI agents, privacy-first tool calling, MCP workflows·Pop: 80/100
Perf: est. 91 tok/s, typical error 25% · first token ~0.2s
Fits in 16 GB VRAM with room to spare. Best for local ai agents, privacy-first tool calling, mcp workflows on RX 9060 XT.
Best for: Quality, Coding, Reasoning·Pop: 86/100
Perf: est. 48 tok/s, typical error 10% · first token ~0.5s
Fits in 16 GB VRAM with room to spare. Best for quality, coding, reasoning on RX 9060 XT.
Best for: Agentic coding on small machines·Pop: 76/100
Perf: est. 48 tok/s, typical error 10% · first token ~0.5s
Fits in 16 GB VRAM with room to spare. Best for agentic coding on small machines on RX 9060 XT.
Best for: Chat, Reasoning, Vision·Pop: 74/100
Perf: est. 28 tok/s, typical error 10% · first token ~0.7s
Fits in 16 GB VRAM with room to spare. Best for chat, reasoning, vision on RX 9060 XT.
No stated VRAM figure or published run covers AMD for these models yet. By memory size: video models · image models.
Yes. On Linux, Ollama lists the RX 9060 XT among its ROCm cards and needs the AMD ROCm v7 driver. Its Windows ROCm list does not include the card, so on Windows Ollama runs it through its Vulkan backend.
It has 16GB of GDDR6 at up to 320 GB/s (AMD specifications). ModelFit budgets about 14.4GB of it for weights and context, enough for dense models up to 14B at Q4 quantization.
Both have 16GB, so they hold the same models. The RTX 5060 Ti streams 448 GB/s against 320 GB/s and runs CUDA, so it is 51% faster on 8B models (est. 68 tok/s, typical error 5% vs est. 45 tok/s, typical error 10%). The 9060 XT runs Ollama and llama.cpp through ROCm or Vulkan.
Both have 16GB. The 9070 XT has twice the bandwidth (640 vs 320 GB/s), so it decodes faster on every model both hold: est. 79 tok/s, typical error 10% against est. 45 tok/s on 8B.
The RX 9060 XT's 16GB of VRAM cannot fit a 32B model comfortably. The largest size class it fits is 20B MoE (3.6B active), at est. 59 tok/s, typical error 25%. The largest individual model in the catalog that fits is LFM2 24B-A2B Instruct (24B).
Time a model that fits your machine with Ollama, using the same prompts as every other run. The CLI shows your result and asks before it sends anything; your machine then joins the speed test leaderboard. It does not change the quality benchmark.
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