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Run LLM Locally

Llama 4 Scout 17B-16E Instruct on the RTX 5090

Llama 4 Scout 17B-16E Instruct at Q4_K_M needs about 68.6 GB, more than the 32 GB of the RTX 5090: part of the model runs from system RAM, which slows generation. At Q3_K_M it does not fit in VRAM.

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Llama 4 Scout 17B-16E Instruct

Q4_K_M · 8K context

Possible but slow

The model needs about 68.6 GB but only 31 GB of VRAM is usable: 37.5 GB must be loaded into system RAM. It runs, 45% on the GPU, but more slowly.

Your hardware
RTX 5090 32 GB · 64 GB RAM
Estimated memory
~68.6 GB estimate
VRAM (usable memory)
31 GB
GPU only
No
CPU/GPU offload
Required (37.5 GB)
Generation speed
3–6 tok/s estimate
Largest context in fast memory
None
Confidence: low

Based on: memory bandwidth (1,792 GB/s) and model size · system RAM bandwidth (~51 GB/s)

Memory breakdown

~68.6 GB (66.9 GB–70.3 GB) needed · estimate

37.5 GB must be offloaded to system RAM

  • Model weights62.9 GB
  • KV cache1.5 GB
  • Runtime overhead0.9 GB
  • Safety margin3.3 GB
Assumptions: context 8K tokens · KV cache f16 · llama.cpp (CUDA) · flash attention on · weights = real file size

EstimateData updated 24 days agoSource: Run LLM Locally engine, Hugging Face config.json + model card

What makes this estimate less reliable

  • no benchmark for this hardware and model yet
  • split CPU/GPU runs vary a lot with the CPU and PCIe link
  • system RAM speed unknown (dual-channel DDR4-3200 assumed)
  • mixture-of-experts: speed depends on expert routing

Other quantizations on the RTX 5090

Llama 4 Scout 17B-16E Instruct on RTX 5090 + 64 GB RAM · 8K context
Llama 4 Scout 17B-16E Instruct Q8_08K~113 GB · No dataIncompatible
Llama 4 Scout 17B-16E Instruct Q6_K8K~89 GB · No dataIncompatible
Llama 4 Scout 17B-16E Instruct Q5_K_M8K~77.4 GB · 2.5–5 tok/s estimatePossible but slow
Llama 4 Scout 17B-16E Instruct Q4_K_M8K~68.6 GB · 3–6 tok/s estimatePossible but slow
Llama 4 Scout 17B-16E Instruct IQ4_XS8K~61.1 GB · 4–7.5 tok/s estimateCPU/GPU offload
Llama 4 Scout 17B-16E Instruct Q3_K_M8K~55.7 GB · 4.5–9 tok/s estimateCPU/GPU offload

Alternatives

GPUs that run Llama 4 Scout 17B-16E Instruct Q4_K_M entirely in VRAM

Other models that fit the RTX 5090

Hardware that runs Llama 4 Scout 17B-16E Instruct Q4_K_M entirely in memory

Prices and links follow the market you choose.

Radeon PRO W7900

AMD

ExcellentRuns Llama 4 Scout 17B-16E Instruct Q4_K_M entirely in memory

Price not available

Availability unknown

View on amazon.com

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NVIDIA DGX Spark (GB10)

Unified memory PC

ExcellentRuns Llama 4 Scout 17B-16E Instruct Q4_K_M entirely in memory

Price not available

Availability unknown

View on amazon.com

Affiliate link

No current offer imported for this market: the link opens a marketplace search.

Frequently asked questions

Can the RTX 5090 run Llama 4 Scout 17B-16E Instruct?
The model needs about 68.6 GB but only 31 GB of VRAM is usable: 37.5 GB must be loaded into system RAM. It runs, 45% on the GPU, but more slowly.
Which quantization should I use on the RTX 5090?
None fits entirely in VRAM at 8K context; the smallest, Q3_K_M, still needs offload.
What is the largest context on the RTX 5090?
At Q4_K_M, the model does not fit entirely in VRAM even at a short context.
How fast is Llama 4 Scout 17B-16E Instruct (RTX 5090)?
We estimate 3–6 tok/s (estimate, confidence low). Based on: memory bandwidth (1,792 GB/s) and model size, system RAM bandwidth (~51 GB/s).