Cheapest way to run gpt-oss-20b
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gpt-oss-20b needs 11.9 GB at 4-bit — cheapest rental $0.16/hr.
Quick answer
At 4-bit with an 8K-token conversation, gpt-oss-20b needs about 11.9 GB. It fits a Arc A770 16 GB (16 GB), so if you already own one it costs nothing per token; the cheapest rental big enough is RTX A5000 on Runpod at $0.16 an hour.
| Option | What you need | Cost | Notes |
|---|---|---|---|
| Your own GPU | Arc A770 16 GB (16 GB) at MXFP4 (native), needs 14.0 GB | No GPU purchase prices in our data. Check price on Amazon Amazon affiliate link | $0 per token or hour once you own the card (power not counted) |
| Mac (unified memory) | Mac, 16 GB at MXFP4 (native), needs 14.0 GB | No Mac prices in our data | $0 per token once you own it |
| Rent a GPU | RTX A5000 (24 GB total) on Runpod Referral link, Community Cloud | $0.16 per hour | Pay only while it runs. Price checked 2026-11-09 |
Fit rule: the Q4_K_M file (or the highest precision that fits) plus an 8K-token FP16 KV cache under 90% of memory, same as the calculator. Sizes and sources: gpt-oss-20b VRAM page. Rental prices: GPU prices (checked 2026-11-09). Hourly and per-token prices measure different things, so which is cheaper depends on how much you use it.
Links marked Referral link, Affiliate partner or Amazon affiliate link may earn us credit or a commission at no cost to you. Disclosure.
Precision ladder: what each quant costs
| Precision | Needs at 8K | Smallest consumer GPU | Smallest Mac | Cheapest single-GPU rental | Cheapest rental overall (any GPU count) |
|---|---|---|---|---|---|
| MXFP4 (native) | 14.0 GB | Arc A770 16 GB (16 GB) | Mac, 16 GB at MXFP4 (native) | RTX A5000 (24 GB) on Runpod, $0.16/hr | RTX A5000 (24 GB) on Runpod, $0.16/hr |
| 8-bit (Q8_0) | 12.3 GB | Arc A770 16 GB (16 GB) | Mac, 16 GB at 8-bit (Q8_0) | RTX A5000 (24 GB) on Runpod, $0.16/hr | RTX A5000 (24 GB) on Runpod, $0.16/hr |
| 4-bit (Q4_K_M) | 11.9 GB | Arc A770 16 GB (16 GB) | Mac, 16 GB at 4-bit (Q4_K_M) | RTX A5000 (24 GB) on Runpod, $0.16/hr | RTX A5000 (24 GB) on Runpod, $0.16/hr |
Every row names its precision, Mac rows included (Macs share unified memory with the system, so the same 90% rule applies). Single-GPU and multi-GPU rentals are listed separately: a multi-GPU rental needs software that splits the model across cards (llama.cpp, vLLM tensor parallel). Prices checked 2026-11-09.
How context length changes the bill (4-bit)
| Context | Needs | Cheapest rental overall |
|---|---|---|
| 8K tokens | 11.9 GB | RTX A5000 (24 GB) on Runpod, $0.16/hr |
| 32K tokens | 12.5 GB | RTX A5000 (24 GB) on Runpod, $0.16/hr |
| 128K tokens | 14.9 GB | RTX A5000 (24 GB) on Runpod, $0.16/hr |
What $100 of rental buys you
We list no GPU purchase prices (they change daily and we cannot check them), so this page does not compare renting with buying. At the cheapest rental that fits (RTX A5000 (24 GB) on Runpod, $0.16/hr), $100 of rental buys about 625 hours of running gpt-oss-20b at 4-bit with 8K context. Electricity, storage and setup time are not counted.
Fit quirks for this model
- Mixture-of-experts: about 3.6B parameters are active per token, but every expert must still be in memory, so VRAM follows the total size, not the active size.
- 12 full-attention layers × 8 KV heads × 64 head dim × 2 × 2 bytes. The other 12 layers use a 128-token sliding window, so their cache stays tiny.
- The MoE experts stay in MXFP4 in every GGUF, so Q4_K_M, Q8_0 and BF16 files are close in size.
- Maximum context is 131,072 tokens; longer settings are capped.
Model data checked 2026-10-23; rental prices checked 2026-11-09. For file sizes, sources and the full fit table see the gpt-oss-20b VRAM requirements page; this page is only about cost.
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