Can the NVIDIA RTX 3070 run gpt-oss 120B?

RTX 3070 × gpt-oss 120B8K context
NoDoes not fit in memory
Memory at Q4_K_M72 GB of 8 GB
Weights 70.8 GBKV cache 0.6 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 120B needs about 72 GB, but the NVIDIA RTX 3070 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:120b

At Q4_K_M it needs about 72 GB, so pick a GPU with at least 80 GB.

Run it in the cloud

Too big for the NVIDIA RTX 3070? Run gpt-oss 120B 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 3070 can use.

QuantQualityMemoryFits?Speed
FP16Full quality234.8 GBToo big—
Q8_0Near-lossless125.3 GBToo big—
Q6_KExcellent97 GBToo big—
Q5_K_MVery good84.3 GBToo big—
Q4_K_MGood, most popular72 GBToo big—
Q3_K_MNoticeably degraded58.3 GBToo big—
Q2_KHeavily degraded45 GBToo big—

Frequently asked questions

Can NVIDIA RTX 3070 run gpt-oss 120B?

No. Even at Q4_K_M, gpt-oss 120B needs about 72 GB, but the NVIDIA RTX 3070 has 8 GB usable. You could offload some layers to system RAM, but it will be very slow.

How much VRAM does gpt-oss 120B need?

About 72 GB at Q4_K_M with 8,192 tokens of context, and about 234.8 GB at full FP16 precision.

How fast is gpt-oss 120B on the NVIDIA RTX 3070?

It does not fit in the NVIDIA RTX 3070's memory, so most layers would run from system RAM and generation would be very slow.