Can the NVIDIA RTX 4060 run gpt-oss 20B?

RTX 4060 × gpt-oss 20B8K context
NoDoes not fit in memory
Memory at Q4_K_M13.7 GB of 8 GB
Weights 12.7 GBKV cache 0.4 GBRuntime 0.6 GB
  1. FP16
  2. Q8_0
  3. Q6_K
  4. Q5_K_M
  5. Q4_K_M
  6. Q3_K_M
  7. Q2_K

No. Even at Q4_K_M, gpt-oss 20B needs about 13.7 GB, but the NVIDIA RTX 4060 has 8 GB usable. You could offload some layers to system RAM, but it will be very slow.

Run it on a rented GPU

On a cloud GPU with enough memory, the same command you'd use locally works:

ollama run gpt-oss:20b

At Q4_K_M it needs about 13.7 GB, so pick a GPU with at least 16 GB.

Run it in the cloud

Too big for the NVIDIA RTX 4060? Run gpt-oss 20B on a rented GPU instead.

Rent a cloud GPU by the hour. No upfront cost, stop any time.

Memory needed by quantization

At 8,192 tokens of context. The black line marks the 8 GB the NVIDIA RTX 4060 can use.

QuantQualityMemoryFits?Speed
FP16Full quality42.8 GBToo big—
Q8_0Near-lossless23.2 GBToo big—
Q6_KExcellent18.1 GBToo big—
Q5_K_MVery good15.9 GBToo big—
Q4_K_MGood, most popular13.7 GBToo big—
Q3_K_MNoticeably degraded11.2 GBToo big—
Q2_KHeavily degraded8.8 GBToo big—

Frequently asked questions

Can NVIDIA RTX 4060 run gpt-oss 20B?

No. Even at Q4_K_M, gpt-oss 20B needs about 13.7 GB, but the NVIDIA RTX 4060 has 8 GB usable. You could offload some layers to system RAM, but it will be very slow.

How much VRAM does gpt-oss 20B need?

About 13.7 GB at Q4_K_M with 8,192 tokens of context, and about 42.8 GB at full FP16 precision.

How fast is gpt-oss 20B on the NVIDIA RTX 4060?

It does not fit in the NVIDIA RTX 4060's memory, so most layers would run from system RAM and generation would be very slow.