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HomeCan I run it?gpt-oss-20bgpt-oss-20b on an RTX 5070 Ti

Yes, but it is tight. gpt-oss-20b at Q4_K_M needs about 15.6 GB, and the GeForce RTX 5070 Ti has 16.0 GB usable at a ~4k context.

Can I run gpt-oss-20b on an RTX 5070 Ti?

gpt-oss-20b at Q4_K_M needs about 15.6 GB, and the GeForce RTX 5070 Ti has 16.0 GB usable. That figure is 16 GB of VRAM on the spec sheet, of which 16.0 GB is usable for weights and cache, at 896 GB/s memory bandwidth. OpenAI’s open 20B-class gpt-oss model. Only a few billion parameters are active per token, so decode can be quicker than a dense 20B. These are estimates from our formula, not measured benchmarks.

Quant that fits
Q4_K_M Fits, short context only
Memory needed
15.6 GB at Q4_K_M, ~4k context
Usable VRAM
16.0 GB of 16 GB VRAM
Est. tokens/sec
up to 349 estimate

gpt-oss-20b · GeForce RTX 5070 Ti · Everything this machine runs · Q4 vs Q8 vs FP16

Memory at each quant

QuantNeed vs 16.0 GBVerdict
Q4_K_M15.6 GBQ4Tight
Q8_026.6 GBQ8Won’t fit
FP1649.1 GBFP16Won’t fit

Estimated tokens per second

Estimate: up to 349 tokens/sec at Q4_K_M (upper bound: this is a Mixture-of-Experts, and only the active weights are counted). This is a bandwidth-derived range, not a bench. Efficiency is a 60–85% GPU band from How we calculate.

Context headroom

At Q4_K_M, this machine has headroom out to about 16k tokens under our KV estimate. Longer chats need more memory for the cache. Figures assume an FP16 KV cache.

Context headroom for gpt-oss-20b on an RTX 5070 Ti at Q4_K_M
ContextKV cache (FP16)Total at Q4_K_MVerdict
4k0.1 GB15.6 GBTight
8k0.2 GB15.7 GBTight
16k0.4 GB15.9 GBTight
32k0.8 GB16.3 GBWon’t fit
64k1.6 GB17.1 GBWon’t fit
128k3.2 GB18.7 GBWon’t fit

From the model’s config. Try other sizes in the VRAM calculator or see the context length guide.

What to do instead

Other hardware for gpt-oss-20b

Other models on an RTX 5070 Ti

Quick answers

Can I run gpt-oss-20b on an RTX 5070 Ti?
Yes, but it is tight. gpt-oss-20b at Q4_K_M needs about 15.6 GB, and the GeForce RTX 5070 Ti has 16.0 GB usable.
How much VRAM does gpt-oss-20b need?
About 15.6 GB at Q4_K_M, 26.6 GB at Q8_0 and 49.1 GB at FP16, estimated for a ~4k context.
How fast is gpt-oss-20b on an RTX 5070 Ti?
Our estimate is up to 349 tokens per second at Q4_K_M, as an upper bound for this MoE. It is not a measured benchmark.
What context length can I use?
At Q4_K_M, our KV estimate supports up to about 16k tokens before the cache pushes the total over usable memory.

New models, and what they need to run

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