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Can the NVIDIA GeForce RTX 3080 run gpt-oss-20b?

gpt-oss-20b · NVIDIA GeForce RTX 3080 · 8,192 ctx · whole model residentQ2_K sizereconstructed sizeSpeedestimate

IT FITS

Yes. 1 of 9 formats evaluated fit in 10 GB, at 8,192 tokens. No published file exists for this pair, so every size is rebuilt from the pinned architecture. The best quality that fits is Q2_K, needing 8.82 GB and running at an estimated 292 tokens per second.

Fitting in memory is not the same as loading. Whether the runtime and version you have supports this architecture and format on this machine has not been tested here.

Find the NVIDIA GeForce RTX 3080: Amazon ↗ · eBay (new and used) ↗Store links may pay us a commission. They never decide which card is suggested — the memory arithmetic does.

1/9formats that fit
10 GBdevice memory
8.82 GBneeded at best quality
~292tokens per second, estimatedFaster than you read
760GB/s bandwidth
Run it in the cloud

Run gpt-oss-20b properly, for cents an hour

The NVIDIA GeForce RTX 3080 only holds it at Q2_K, at an estimated ~292 tokens per second. A rented card runs a better format at full speed, billed by the second.

Your card Or keep Q4_K_M on your own card: with about 5 GB of it in system RAM, the NVIDIA GeForce RTX 3080 runs it at ~62 tokens per second (faster than you read) — free, if you have the RAM. A rented card below holds all of it on the GPU. Size the offload →

Prices · 18:30 UTC, 22 Sept
Cheapest16 GB

1× RTX 4060 Ti 16GB

$0.090per hour

Speed
~78 tok/sFaster than you read
Per million tokens
$0.32

Vast.ai · Marketplace · 99.4% reliable

Rent on Vast.ai

Lowest price per hour

Compare all 27 rentable configurations →

No GPU at all

Use gpt-oss-20b by the token

For one person chatting, that is about 1.5× cheaper than the best-value rented card above ($0.13 per million tokens). A rented GPU pays off when you keep it busy — many requests batched together, long agent runs — or when the data must stay on a machine you control, or the exact file you want is not served anywhere.

First time renting a GPU? How it works, in four steps
  1. Create an account and add credit. Both providers are prepaid: $10 is enough to try any card on this page for hours. A new RunPod account that arrives through a referral link gets a one-time $5 credit when it first adds $10.
  2. Pick the card and the count shown here, and a template: llama.cpp or vLLM for an OpenAI-compatible API, or one with a web chat if you only want to talk to the model. “How to launch it” below gives the exact image and command.
  3. Wait for the download. The weights are fetched on the machine; tens of gigabytes take a few minutes on a data-centre connection. You pay by the second from the moment it starts.
  4. Stop it when you are done. A running machine bills even when idle. A stopped one costs nothing per hour, though its disk is usually still billed until you delete it.
How to launch it

No Q4_K_M file of gpt-oss-20b is catalogued here, so there is no exact command to vouch for. Start the machine from the llama.cpp server image ghcr.io/ggml-org/llama.cpp:server-cuda and point -hf at a Q4_K_M build from the publisher or a quantizer you trust — search Hugging Face for one. Check its file size against the memory figure on this page before you rent.

Every machine holds the whole model at Q4_K_M and 8,192 tokens of context, no offload. Speeds are this site’s single-stream decode estimates; prices are what each provider’s own API quoted, on-demand, for the whole machine. Vast hosts below 98% measured reliability are left out.

Referral links Vast.ai, RunPod and Novita pay us a share of what you spend if you sign up through these buttons. It costs you nothing, and it never decides an order or a recommendation: both are computed from the live price and the speed, and options that pay us nothing are listed and recommended on the same terms. How we rank

Runs well on your NVIDIA GeForce RTX 3080 instead

The models nearest to gpt-oss-20b — same lab first, then closest in size — that the NVIDIA GeForce RTX 3080 holds whole at the standard format and runs at a usable speed. Nearest in size is not the same as equally good; compare them on the task you care about.

How long a conversation. At Q2_K, this pair holds 56,290 tokens of context — roughly 42,218 words — needing 10.00 GB. Past that the NVIDIA GeForce RTX 3080 runs out of memory, not the model out of context, which would allow 131,072. This is the wall a long chat hits after it has already loaded fine. The nearest round setting below it is 32,768.

Every format evaluated

No published file exists for this pair, so every size is rebuilt from the pinned architecture. The Basis column says which is which for each row. Decode and first-token figures are calibrated estimates, not runs on this card.

FormatNeedsOf 10 GBFitsDecodeFirst tokenBasis
FP1642.8 GB428%short by 32.8 GBdoes not run—reconstructed size
Q8_023.2 GB232%short by 13.2 GBdoes not run—reconstructed size
Q6_K18.2 GB182%short by 8.17 GBdoes not run—reconstructed size
Q5_K_M15.9 GB159%short by 5.90 GBdoes not run—reconstructed size
Q5_015.5 GB155%short by 5.47 GBdoes not run—reconstructed size
Q4_K_M13.8 GB138%short by 3.76 GBdoes not run—reconstructed size
Q4_012.9 GB129%short by 2.93 GBdoes not run—reconstructed size
Q3_K_M11.5 GB115%short by 1.48 GBdoes not run—reconstructed size
Q2_K8.82 GB88%yes~292 tok/sno comparable peakreconstructed size

Sized at 8,192 tokens of context with the whole model resident — weights, the KV cache and the runtime reserve, offload off. Speed is only quoted for a format that fits: a rate for a configuration that cannot load is not a fact about anything.

Why the first-token figure is the same on every row

First token is modelled from the arithmetic the prompt requires, and that count does not change with the weight format — which is why it reads the same on every row. Real prefill does vary by format, because a quantized matmul is a different kernel; this model does not capture that, and the figure should be read as an order of magnitude rather than a ranking between formats.

What the labels mean
published data
Read from a published source — a file's byte count, a model's configuration or a manufacturer's specification — or exact arithmetic on such values. Not a measurement on a machine.
reconstructed size
Weight size reconstructed from the pinned architecture, because no published file exists.
size range
Only a lower and an upper bound are claimed for this weight size.
estimate
Calculated from sourced inputs by a stated method; an estimate, not a measurement.
Change anything

This page fixes the context at 8,192 tokens and one device. Batch, concurrent users, KV-cache format, clusters and rental cost are all in the calculator, already set to this pairing.

Open in the calculator →Everything the NVIDIA GeForce RTX 3080 runsBest models for 10 GB

Where these numbers come from

gpt-oss-20b’s architecture is read from its publisher’s own config.json at a pinned revision, and the NVIDIA GeForce RTX 3080’s 10 GB and 760 GB/s come from the manufacturer’s specification. How far each figure can be trusted is published, per format and worst case included, on the accuracy scorecard.

Building this into your own product? A free API key returns exactly these figures, and the widget puts this answer on a product page with one script tag.

Use this answer in your own product

As JSON, from the API
curl -s https://llmbottleneck.com/v1/analyze \
  -H "Authorization: Bearer $LLMB_KEY" -H "content-type: application/json" \
  -d '{"model":"openai-gpt-oss-20b","quantization":"Q2_K","context":8192,"hardware":"nvidia-geforce-rtx-3080"}'

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<script src="https://llmbottleneck.com/widget.js"
  data-model="openai-gpt-oss-20b" data-quantization="Q2_K"
  data-hardware="nvidia-geforce-rtx-3080" data-context="8192"></script>

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