Can you run GPT-OSS 20B on RTX 4070?
GPT-OSS 20B MXFP4 on the NVIDIA GeForce RTX 4070 — verdict, VRAM math and estimated speed.
Yes, but slowly — GPT-OSS 20B spills past the RTX 4070. 13.8 GB weights at MXFP4 vs 10.8 GB usable VRAM; ~33 tok/s est..
VRAM math and speed are ModelFit engine estimates, not measurements. Commands are registry-verified Ollama tags.
Cite this page: ModelFit, GPT-OSS 20B on RTX 4070, https://modelfit.io/can-i-run/gpt-oss-20b-on-rtx-4070/, updated September 2026, CC BY 4.0.
Last updated: September 3, 2026 · Editor: ModelFit Team
Quant explorer
Switch between the quality-gated builds tracked for this exact combo. Q4_K_M stays the default recommendation; heavier Q6/Q8 builds only appear when their exact Ollama tags are registry-verified.
Why no 2-bit builds: Q1/Q2-class quants can look attractive in a memory table, but their quality loss is large enough that ModelFit excludes them from rankings instead of inflating the catalog with junk options. Usable VRAM: 10.8 GB.
Workload verdicts
GPT-OSS 20B on the RTX 4070, graded per use case from the model's registry-verified tuning and the engine's speed estimate for this exact combo.
Grades combine tag-verified tuning (a model not built for the workload caps at C) with the tok/s floor each workload needs to feel usable. A combo that partially offloads caps at C; one that does not fit is D everywhere.
Memory math: weights + context vs budget
Weights take 13.8 GB. Context costs extra KV-cache on top — this is where long-context sessions break on cards that technically fit the weights.
This configuration exceeds the comfortable VRAM budget by about 7.0 GB. Expect offload pressure or a shorter safe 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.
See how fast it feels
A deterministic typing simulation for GPT-OSS 20B: first token ~0.5s (instant prefill), then ~33 tokens/sec.
ModelFit engine estimate, not a measured benchmark. Real speed varies with prompt length, thermals, runtime, and KV-cache settings.
Upgrade path
The cheapest tracked card that runs GPT-OSS 20B comfortably is the AMD Radeon RX 7900 XT (20 GB VRAM).
See the RX 7900 XT pageGPT-OSS 20B on RTX 4070: FAQ
Can the NVIDIA GeForce RTX 4070 run GPT-OSS 20B?
Barely. GPT-OSS 20B (MXFP4) needs about 13.8 GB but the RTX 4070 has 10.8 GB usable VRAM, so part of the model spills to system RAM and speed drops to at roughly 33 tokens/sec (est.).
How much VRAM does GPT-OSS 20B need?
About 13.8 GB for the weights at MXFP4, plus KV-cache for context: roughly 4.0 GB extra at 16k tokens. The RTX 4070 budget is 10.8 GB (12 GB x 90%).
What is the best quantization of GPT-OSS 20B for the RTX 4070?
Stick with the MXFP4 build at 13.8 GB — every heavier quant exceeds the 10.8 GB usable VRAM.
What GPU do I need to run GPT-OSS 20B comfortably?
The cheapest tracked card that runs GPT-OSS 20B (MXFP4) fully in VRAM is the AMD Radeon RX 7900 XT (20 GB).