Rent a GPU

Can the NVIDIA RTX 4080 Super run Llama 4 Scout 17B-16E?

RTX 4080 Super × Llama 4 Scout 17B-16E8K context
NoDoes not fit in memory
Memory at Q4_K_M68.3 GB of 16 GB
Weights 66.1 GBKV cache 1.6 GBRuntime 0.6 GB
On the GPU21%of the Q4_K_M weights
In system RAM52.3 GBthe rest spills over
Offload speed~4.3tokens per second
  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, Llama 4 Scout 17B-16E needs about 68.3 GB, but the NVIDIA RTX 4080 Super has 16 GB usable. You could offload some layers to system RAM, but it will be very slow.

Try other context lengths

Run it on a rented GPU

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

ollama run llama4:scout

At Q4_K_M it needs about 68.3 GB, so pick a GPU with at least 72 GB.

New to this? Step-by-step setup for the RTX 4080 Super

Run it in the cloud

Too big for the NVIDIA RTX 4080 Super? Run Llama 4 Scout 17B-16E on a rented GPU instead.

Rent a cloud GPU by the hour. No upfront cost, stop any time. Not sure whether to rent or buy? Compare the cost.

Rent a GPU on RunPodRent a GPU on Vast.ai

Memory needed by quantization

At 8,192 tokens of context. The black line marks the 16 GB the NVIDIA RTX 4080 Super can use.

QuantQualityMemoryFits?Speed
FP16Full quality220.2 GBToo big—
Q8_0Near-lossless118 GBToo big—
Q6_KExcellent91.6 GBToo big—
Q5_K_MVery good79.7 GBToo big—
Q4_K_MGood, most popular68.3 GBToo big—
Q3_K_MNoticeably degraded55.5 GBToo big—
Q2_KHeavily degraded43.1 GBToo big—

Measured speed from real RTX 4080 Super owners

Tokens per second that people measured on their own NVIDIA RTX 4080 Super, next to our estimate.

No one has measured Llama 4 Scout 17B-16E on the NVIDIA RTX 4080 Super yet. The speeds above are estimates. Run the benchmark and yours becomes the first real number on this page.

Have an RTX 4080 Super? Add your real speed

One command times Llama 4 Scout 17B-16E in your own Ollama, shows you exactly what it would send, and asks before sending anything. No sign-up, no personal data.

macOS and Linux

curl -fsSL https://gpufit.dev/bench.sh | sh -s -- llama4:scout

Windows (PowerShell)

$env:GPUFIT_MODEL="llama4:scout"; irm https://gpufit.dev/bench.ps1 | iex

How the benchmark works

GPUs that run Llama 4 Scout 17B-16E comfortably

Check RTX PRO 6000 Blackwell prices

Models that run well on the NVIDIA RTX 4080 Super

Frequently asked questions

Can NVIDIA RTX 4080 Super run Llama 4 Scout 17B-16E?

No. Even at Q4_K_M, Llama 4 Scout 17B-16E needs about 68.3 GB, but the NVIDIA RTX 4080 Super has 16 GB usable. You could offload some layers to system RAM, but it will be very slow.

How much VRAM does Llama 4 Scout 17B-16E need?

About 68.3 GB at Q4_K_M with 8,192 tokens of context, and about 220.2 GB at full FP16 precision.

How fast is Llama 4 Scout 17B-16E on the NVIDIA RTX 4080 Super?

It does not fit in the NVIDIA RTX 4080 Super's memory, so most layers would run from system RAM and generation would be very slow.