Can the Radeon RX 9060 XT 16GB Run gpt-oss-20b?
Runs
Yes. gpt-oss-20b at MXFP4 is a 12.1 GB download (Hugging Face file listing for ggml-org/gpt-oss-20b-GGUF), and the Radeon RX 9060 XT 16GB has 17.2 GB (16 GiB) of VRAM, leaving about 5.1 GB for the KV cache and runtime. One published run puts generation at 106 tokens per second; a single cited measurement, not a median. Cited run: llama.cpp GitHub ROCm perf discussion #15021.
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Published runs for gpt-oss-20b on the Radeon RX 9060 XT 16GB
| Generation | Quant | Runtime | Context | Source | Date |
|---|---|---|---|---|---|
| 106 tok/s | MXFP4 | llama.cpp (Vulkan) | — | llama.cpp GitHub ROCm perf discussion #15021labelled "gpt-oss:20b" | 2026-10-02 |
More about this pair
How much VRAM does gpt-oss-20b need?
The MXFP4 file is 12.1 GB. Add roughly 1.5 GB for the runtime and an ~8K-token context, so about 13.6 GB of VRAM holds it comfortably. Longer contexts grow the KV cache and need more.
What speed does gpt-oss-20b reach on the Radeon RX 9060 XT 16GB?
The fastest published run is 106 tokens per second (llama.cpp GitHub ROCm perf discussion #15021, MXFP4, llama.cpp (Vulkan)). gpt-oss-20b is a mixture-of-experts model that reads about 2.08 GB of active weights per token, so the Radeon RX 9060 XT 16GB's 320 GB/s of memory bandwidth puts the theoretical ceiling near 154 tokens per second. That is an upper bound, not a speed estimate: kernel, launch and compute overhead keep real runs below it, and SpecPicks rejects any published figure above it.
Check another pair
See the full matrix → · All Radeon RX 9060 XT 16GB benchmarks →
gpt-oss-20b on other GPUs
- RTX 4060 — Offload
- RTX 3060 Ti — Offload
- RTX 3060 12GB — Tight fit, 75 tok/s (1 run)
- Intel Arc B580 — Tight fit
- RTX 4070 SUPER — Tight fit
- RTX 4060 Ti 16GB — Runs, 63.2 tok/s (1 run)
- RTX 5060 Ti 16GB — Runs, 102 tok/s median
- Intel Arc A770 16GB — Runs
- Radeon RX 9070 XT — Runs, 91.9 tok/s (1 run)
- RTX 4070 Ti SUPER — Runs, 106 tok/s median
- RTX 4080 — Runs, 138 tok/s median
- RTX 5070 Ti — Runs, 156 tok/s (1 run)
- RTX 5080 — Runs, 190 tok/s (1 run)
- Tesla P40 24GB — Runs
- RTX 3090 — Runs, 148 tok/s (1 run)
- Radeon RX 7900 XTX — Runs
- RTX 4090 — Runs, 191 tok/s (1 run)
- RTX 5090 — Runs, 298 tok/s (1 run)
- RTX A6000 48GB — Runs
- RTX PRO 5000 Blackwell 48GB — Runs, 194 tok/s median
- Instinct MI210 64GB — Runs
- RTX PRO 6000 Blackwell 96GB — Runs
Other models on the Radeon RX 9060 XT 16GB
- Llama 3.2 1B — Runs
- Llama 3.2 3B — Runs
- Mistral 7B — Runs, 65.1 tok/s (1 run)
- Qwen2.5 7B — Runs
- Llama 3.1 8B — Runs
- DeepSeek-R1-Distill-Llama 8B — Runs
- Qwen3 8B — Runs
- Gemma 2 9B — Runs
- Gemma 3 12B — Runs
- Qwen2.5 14B — Runs
- DeepSeek-R1-Distill-Qwen 14B — Runs
- Qwen3 14B — Runs
- Phi-4 14B — Runs
- Gemma 3 27B — Tight fit
- Gemma 2 27B — Tight fit
- Qwen3 30B-A3B — Offload
- Qwen3 32B — Offload
- Qwen2.5 32B — Offload
- DeepSeek-R1-Distill-Qwen 32B — Offload
- Qwen3 32B (Q6_K) — Offload
- Qwen2.5 32B (Q6_K) — Offload
- DeepSeek-R1-Distill-Qwen 32B (Q6_K) — Offload
- Gemma 3 27B (Q8_0) — Offload
- Llama 3.1 70B — Offload
- Llama 3.3 70B — Offload
- DeepSeek-R1-Distill-Llama 70B — Offload
How the checks work
The fit is the MXFP4 file size against the card's VRAM, with 1.5 GB kept for the runtime and an ~8K context. A published run counts only if the pair fits, the run is Q4-class, its source does not call it an estimate, it is under the bandwidth ceiling (memory bandwidth ÷ GB read per token: the whole file for a dense model, only the active experts' weights for a mixture-of-experts model such as gpt-oss-20b or Qwen3 30B-A3B, which is why those can post figures above bandwidth ÷ file size), it is not slower than the same card on a model twice its size, a single run is not slower than the same card on a larger dense model, and it is not one figure pasted across many cards. The whole matrix is downloadable as a CSV under CC BY 4.0.
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This page is editorial synthesis based on publicly available information. No independent first-party benchmarking is reported.