Can I Run Meta Llama 4 Scout (109B-A17B MoE) on NVIDIA GeForce RTX 4080 Super (16GB GDDR6X)?
Full VRAM footprint analysis, tokens/sec generation benchmark, and copy-paste Ollama & vLLM runtime commands for running Meta Llama 4 Scout (109B-A17B MoE) (109B) on NVIDIA GeForce RTX 4080 Super (16GB GDDR6X) (16 GB).
✕ Out of Memory — Exceeds VRAM Capacity
OLLAMA_FLASH_ATTENTION=1 OLLAMA_KV_CACHE_TYPE=q8_0 ollama run llama4:scoutLocal Hardware Configurations & Pricing
⚠️ Out of VRAM: High-Capacity Hardware Upgrades2x NVIDIA RTX 3090 Dual Rig (48GB Total VRAM)
Best price-to-VRAM homelab rig for 70B Q4_K_M & Qwen 3.8 27B Q8
Apple Mac Studio M4 Max (128GB Unified Memory)
Running Llama 4 Scout 109B & 70B Q8 locally with 128K+ context
NVIDIA GeForce RTX 4080 Super (16GB GDDR6X)
736 GB/s memory bandwidth for fast 14B and MoE 35B models
⚠️ Out of VRAM? Don't downgrade model quality.
Out of VRAMSpin up an on-demand cloud GPU instance in 60 seconds with pre-installed Ollama and vLLM. (Meta Llama 4 Scout (109B-A17B MoE) @ 71.66 GB VRAM)
Quantization Precision Matrix
32K ctx • KV: Q8_0| Quantization | Weights | KV Cache | Total VRAM | Est. Speed | Fit Status |
|---|---|---|---|---|---|
| FP1616 bpw | 218 GB | 3.22 GB | 229.6 GB (1435%) | 0 t/s | OOM |
| Q8_08.5 bpw | 115.54 GB | 3.22 GB | 123.55 GB (772%) | 0 t/s | OOM |
| Q6_K6.56 bpw | 89.38 GB | 3.22 GB | 96.48 GB (603%) | 0 t/s | OOM |
| Q5_K_M5.68 bpw | 77.39 GB | 3.22 GB | 84.07 GB (525%) | 0 t/s | OOM |
| Q4_K_M4.8 bpw | 65.4 GB | 3.22 GB | 71.66 GB (448%) | 0 t/s | OOM |
| FP44.25 bpw | 57.77 GB | 3.22 GB | 63.76 GB (399%) | 0 t/s | OOM |
| IQ3_XS3.3 bpw | 44.69 GB | 3.22 GB | 50.22 GB (314%) | 0 t/s | OOM |
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Frequently Asked Questions: Meta Llama 4 Scout (109B-A17B MoE) on NVIDIA GeForce RTX 4080 Super (16GB GDDR6X)
How much VRAM does Meta Llama 4 Scout (109B-A17B MoE) need on NVIDIA GeForce RTX 4080 Super (16GB GDDR6X)?
At standard 4-bit quantization (Q4_K_M) with 32K context, Meta Llama 4 Scout (109B-A17B MoE) requires 71.66 GB of total VRAM (weights + KV cache + CUDA runtime overhead).
What inference speed will Meta Llama 4 Scout (109B-A17B MoE) achieve on NVIDIA GeForce RTX 4080 Super (16GB GDDR6X)?
Based on NVIDIA GeForce RTX 4080 Super (16GB GDDR6X)'s 736 GB/s memory bandwidth, expected decode speed is approximately 0 tokens per second.
Can I expand context beyond 32K tokens?
Each additional 32K tokens of context requires additional KV cache memory. For longer context windows, consider 8-bit or 4-bit KV cache quantization.