gpt-oss 20B on 16GB GPUs: RTX 5060 Ti vs RX 9060 XT vs RTX 4070 Ti Super

Who this page is for

You want to run OpenAI's open-weight gpt-oss 20B on your own machine, and you want to buy one graphics card to do it. The official gpt-oss-20b model card on Hugging Face describes it as a mixture-of-experts model whose expert weights ship natively in MXFP4, sized to run within 16GB of memory. That one fact changes the buying question. On a 16GB card the whole model stays in VRAM, so you are not choosing between "fits" and "doesn't fit". You are choosing how many tokens per second you get, and how much of the 128K context window you can use before generation speed sags.

This page is the 16GB-tier view. If you are still deciding whether gpt-oss is the right model at all, start with the broader best GPU for gpt-oss 20B guide. If you already own a 12GB card, the gpt-oss on a 12GB GPU community consensus covers what changes at that tier, and the RTX 3060 12GB vs Ryzen 5 5600G CPU-only comparison shows what you give up without a GPU at all.

Everything below is synthesis of public benchmark reports. Each table row names its source, and where a number could not be sourced the cell says so instead of guessing. The cards covered are the ASUS Dual GeForce RTX 5060 Ti 16GB OC, the GIGABYTE Radeon RX 9060 XT Gaming OC 16G, the GIGABYTE GeForce RTX 4070 Ti Super Eagle OC 16G and the ZOTAC Gaming GeForce RTX 4080 16GB Trinity OC, with the MSI Gaming GeForce RTX 3060 12GB as the step-down reference.

Key takeaways

• Fastest mainstream 16GB card: RTX 4070 Ti Super, 128.9 tok/s at MXFP4, 4K context (Hardware Corner).
• Value pick: RTX 5060 Ti 16GB, 92.1 tok/s at MXFP4, 4K context, and Hardware Corner reports it holds the full 128K context in VRAM (Hardware Corner).
• AMD option: RX 9060 XT 16GB, 105.8 tok/s generation on llama.cpp's Vulkan backend, 92.8 tok/s on HIP/ROCm (llama.cpp discussion #15021).
• Long context roughly halves speed: the RTX 4070 Ti Super drops from 128.9 tok/s at 4K to 57.5 tok/s at 128K (Hardware Corner GPU ranking).

How fast is gpt-oss 20B on each 16GB card?

Generation speed (tokens per second while the model writes its answer) is the number you feel. The table below collects the published gpt-oss 20B results for each card at short context.

Two notes before you read too much into the gaps. First, the RX 9060 XT figure is a llama-bench tg128 run posted in a community thread, while the NVIDIA figures come from Hardware Corner's own 4K-context runs. Different builds, drivers and prompt lengths mean the AMD row is indicative, not a controlled head-to-head. Second, the glukhov.org RTX 4080 run is labelled Q4_K_M by Ollama, but the author notes the official gpt-oss build is MXFP4, so treat it as a second data point for the same weights rather than a different quant.

Prompt processing (how fast the card reads your input) separates the cards more sharply. Per the same Hardware Corner pages at 4K context, prefill runs at 3,585 tok/s on the RTX 5060 Ti 16GB, 5,628 tok/s on the RTX 4070 Ti Super, 6,219 tok/s on the RTX 4080 and 6,364 tok/s on the RTX 4080 Super. The llama.cpp thread reports 3,464 tok/s pp512 on the RX 9060 XT under Vulkan. If you paste long documents or run retrieval pipelines, that prefill gap matters more than the generation gap.

Which quant should you run: MXFP4, Q4_K_M or Q4_K_XL?

For gpt-oss 20B the quant question is smaller than it is for dense models. The expert weights, which are most of the parameter count, are released in MXFP4 per the Hugging Face model card. Community GGUF files labelled Q4_K_M or Q4_K_XL mostly re-encode the remaining layers, so file sizes and speeds land close together.

The practical answer: run the official MXFP4 build unless your runtime does not support it. It is the format the published 16GB numbers were measured on, and it leaves the most VRAM for KV cache. Larger GGUF variants buy little quality on a model whose experts were trained for MXFP4, and they eat the context headroom that makes a 16GB card worth buying.

What happens to speed as context grows?

KV cache grows with every token of context, and reading it each step eats memory bandwidth. Hardware Corner publishes a context sweep for each NVIDIA card. The 4070 Ti Super values below match its GPU ranking table, where the same runs are labelled Q4_K_XL; the per-card pages label the model MXFP4.

All values are generation tok/s. Slowdown ratios for the RTX 4070 Ti Super, each against its 4K figure:

• 16K: 113.6 / 128.9 = 0.88, a 12% drop.
• 32K: 98.7 / 128.9 = 0.77, a 23% drop.
• 64K: 78.7 / 128.9 = 0.61, a 39% drop.
• 128K: 57.5 / 128.9 = 0.45, a 55% drop.

The RTX 5060 Ti 16GB keeps 48% of its 4K speed at 128K (43.8 / 92.1), and the RTX 4080 keeps 44% (60.0 / 136.5). The curve is gradual, not a cliff, which tells you all four cards hold the 128K cache in VRAM rather than spilling to system RAM. One worked example: an agent that carries a 64K-token history will see about 58 tok/s on the RTX 5060 Ti 16GB and about 79 tok/s on the RTX 4070 Ti Super, per the table above. Both are faster than most people read.

The RX 9060 XT thread does not include a long-context sweep, so its 128K behaviour is not covered by the cited sources. With 320 GB/s of memory bandwidth, the lowest here, expect it to slow at least as steeply as the RTX 5060 Ti; measure it on your own workload before relying on long contexts.

Is 12GB enough, or do you need 16GB?

The step-down card is the RTX 3060 12GB, the most common budget AI card in the catalog. Across 41 published 7-9B model runs from 20 sources, it posts a median of 59.5 tok/s, per the SpecPicks RTX 3060 12GB benchmark page. That is a capable card for 8B-class models.

gpt-oss 20B is different. Its MXFP4 weights occupy most of a 12GB card before any context is allocated, so long prompts force a shorter context window or partial CPU offload. The 12GB community consensus page collects what owners report at that tier. The short version:

• If gpt-oss 20B is a side model and you mostly run 7-9B models, a 12GB card is fine.
• If gpt-oss 20B is your daily driver and you want more than a few thousand tokens of context, 16GB removes the constraint.
• If you plan to try 27-32B dense models later, 16GB is the floor; see the Qwen 3.6 27B on 16GB quant table.

Spec-delta table

Specs are from the TechPowerUp GPU Database; gpt-oss figures repeat the 4K-context results above. Launch MSRP only, never street price.

The bandwidth column explains most of the NVIDIA ordering: gpt-oss 20B activates only a few billion parameters per token, so the cards are reading weights and cache from memory faster than they can compute with them. The RX 9060 XT is the outlier, posting more tok/s than its 320 GB/s would suggest. That is one more reason to treat its single community result as indicative until a second source confirms it.

Does CUDA vs ROCm/Vulkan change the answer for the RX 9060 XT?

Yes, by enough to matter. In the llama.cpp performance discussion #15021, the same RX 9060 XT 16GB posted 105.8 tok/s generation and 3,464 tok/s prompt processing on the Vulkan backend (RADV driver), against 92.8 tok/s generation and 3,372 tok/s prompt processing on the HIP/ROCm backend. That is roughly a 14% generation gap from the backend alone, with the model fully offloaded (-ngl 99) and flash attention on.

What that means for buyers:

• llama.cpp and LM Studio users can pick the Vulkan backend and get the faster number without installing ROCm.
• Ollama users should check which backend their build uses on RDNA 4; support has moved quickly through 2026.
• vLLM, ExLlamaV2 and most training tools assume CUDA. If those are on your list, the AMD card's speed advantage at the price does not help you.

NVIDIA cards run every major runtime on CUDA without backend choices. That is the real cost of the AMD option, more than the tok/s.

Perf-per-dollar and perf-per-watt

Computed only from the figures cited above, at launch MSRP and rated board power. Board power is the TechPowerUp rating, not a measured inference draw; real draw during gpt-oss generation is usually lower.

At MSRP, the two cheaper cards deliver roughly twice the tokens per dollar of the RTX 4080. The RX 9060 XT leads both columns, with the caveats on its single source and runtime support. Street prices move constantly, especially around late-November retail events, so check the live price buttons on this page before you redo the maths.

Common pitfalls

• Buying the 8GB variant by mistake. The RTX 5060 Ti and RX 9060 XT both ship in 8GB versions with near-identical names. gpt-oss 20B will not fit in 8GB without heavy offload. Confirm "16GB" in the exact listing title.
• Assuming the 128K figure holds at 4K speed. Every card loses roughly half its generation speed by 128K context, per the sweep above.
• Comparing numbers across runtimes. Ollama, llama.cpp CUDA, llama.cpp Vulkan and HIP each report different results on the same card; compare like with like.
• Running a bigger GGUF "for quality". On a model whose experts are natively MXFP4, larger files mainly cost you context headroom.

Verdict matrix

• Get the RTX 5060 Ti 16GB if you want CUDA, full 128K context in VRAM, and about 92 tok/s at short context for the lowest NVIDIA launch price in the 16GB tier.
• Get the RX 9060 XT 16GB if you run llama.cpp or LM Studio, are comfortable choosing a Vulkan or ROCm backend, and want the most tok/s per dollar at MSRP.
• Get the RTX 4070 Ti Super if prefill speed matters (5,628 tok/s at 4K) and you want about 40% more generation speed than the RTX 5060 Ti at every context length.
• Get the RTX 4080 if you also game at 4K or plan heavier models, and the extra 6% over the 4070 Ti Super at 4K context is worth the higher launch price to you.
• Stay on the RTX 3060 12GB if you mostly run 7-9B models and use gpt-oss 20B occasionally with short prompts.

Recommended pick

For most readers, the RTX 5060 Ti 16GB is the card to buy for gpt-oss 20B. It runs the model fully in VRAM at the full 128K context, generates at 92.1 tok/s at 4K and 43.8 tok/s at 128K per Hardware Corner, and runs every CUDA tool without backend choices. The deciding spec is 16GB of GDDR7 at 448 GB/s for a $429 launch price. Step up to the RTX 4070 Ti Super only if you process long inputs often enough for its 57% faster prefill to matter.

Live price comparison

Prices change daily. Compare current listings side by side:

• RTX 5060 Ti 16GB vs RX 9060 XT 16GB
• RTX 5060 Ti 16GB vs RTX 4070 Ti Super

Related guides

• Best GPU for gpt-oss 20B in 2026
• gpt-oss 20B: RTX 3060 12GB vs Ryzen 5 5600G CPU-only
• Qwen 3.6 27B on 16GB: RTX 5060 Ti vs RX 9060 XT by quant
• Qwen 3.6 35B-A3B on a 16GB GPU
• Best 16GB GPU for local LLMs in 2026

Citations and sources

All sources accessed 2026-11-08.

• Hardware Corner — GPU ranking for local LLMs
• Hardware Corner — RTX 5060 Ti 16GB LLM benchmarks
• Hardware Corner — RTX 4070 Ti Super LLM benchmarks
• Hardware Corner — RTX 4080 LLM benchmarks
• Hardware Corner — RTX 4080 Super LLM benchmarks
• llama.cpp GitHub discussion #15021 (ROCm/Vulkan performance)
• glukhov.org — Choosing the best LLM for Ollama on a 16GB VRAM GPU
• Hugging Face — openai/gpt-oss-20b model card
• TechPowerUp GPU Database — RTX 3060 12 GB
• TechPowerUp GPU Database — RTX 5060 Ti 16 GB
• TechPowerUp GPU Database — RX 9060 XT 16 GB
• TechPowerUp GPU Database — RTX 4070 Ti Super
• TechPowerUp GPU Database — RTX 4080

This piece is editorial synthesis based on publicly available information. No independent first-party benchmarking is reported.