Llama-4-Scout-17B-16E (109B MoE) on NVIDIA RTX 4090 (24GB VRAM)
Even in 4-bit Q4_K_M, Llama-4-Scout-17B-16E (109B MoE) requires 70.22 GB VRAM (3x NVIDIA RTX 4090 GPUs needed for full VRAM execution, or heavy CPU RAM offloading at ~7.8 tok/s).
🎛️ Interactive VRAM & KV-Cache Tuner
📊 Real-Time Memory Footprint
70.22 GB / 24 GB🚀 Ready-to-Run Launch Command
ollama run llama4:scout # Or with llama.cpp server (8K context, Flash Attention): llama-server -hf meta-llama/Llama-4-Scout-17B-16E-Instruct-GGUF -c 8192 -ngl 99 -fa
Need 70.22 GB+ VRAM to run Llama-4-Scout-17B-16E (109B MoE)?
Recommended cloud setup for 4-bit: NVIDIA A100 / H100 (80GB) (or 226.7 GB for full FP16). Spin up a pre-configured vLLM / Ollama instance in 60 seconds:
| Provider | Best For | RTX 4090 (24GB) | A100 (80GB) | H100 (80GB) | Bonus / Trial | Action |
|---|---|---|---|---|---|---|
| RunPod | 1-Click vLLM / Ollama Pods | $0.44 / hr | $1.39 / hr | $2.39 / hr | Instant PyTorch / vLLM Pods + Serverless GPU | Deploy on RunPod → |
| Vast.ai | Cheapest Spot GPU Market | $0.29 / hr | $0.95 / hr | $1.79 / hr | Save 50–70% on RTX 4090 / 5090 / A100 rentals | Check Spot GPUs → |
| Vultr Cloud GPU | Enterprise SLA & Bare Metal | — | $1.79 / hr | $2.69 / hr | $300 Free Credit for New Users | Claim $300 Credit → |
| DigitalOcean GPU Droplets | Managed H100 Droplets | — | — | $2.99 / hr | $200 Free Trial (60 Days) | Get $200 Free → |
All Quantization Formats: Llama-4-Scout-17B-16E (109B MoE) on NVIDIA RTX 4090
| Quantization Format | Bits/Weight | Weights VRAM | KV Cache (8K) | Total VRAM | Fits on 1x NVIDIA RTX 4090 (24GB)? | Est. Speed | GPUs Needed |
|---|---|---|---|---|---|---|---|
| FP16 / BF16 (Unquantized) | 16 bpw | 224.54 GB | 1.5 GB | 226.7 GB | No (Over by 202.7GB) | ~2.4 tok/s | 10x NVIDIA RTX 4090 |
| FP8 (W8A8 / E4M3) | 8.5 bpw | 119.29 GB | 1.5 GB | 121.45 GB | No (Over by 97.5GB) | ~4.5 tok/s | 6x NVIDIA RTX 4090 |
| GGUF Q8_0 (8-bit) | 8.5 bpw | 119.29 GB | 1.5 GB | 121.45 GB | No (Over by 97.5GB) | ~4.5 tok/s | 6x NVIDIA RTX 4090 |
| GGUF Q6_K (6-bit) | 6.56 bpw | 92.06 GB | 1.5 GB | 94.22 GB | No (Over by 70.2GB) | ~5.8 tok/s | 4x NVIDIA RTX 4090 |
| GGUF Q5_K_M (5-bit) | 5.69 bpw | 79.85 GB | 1.5 GB | 82.01 GB | No (Over by 58.0GB) | ~6.7 tok/s | 4x NVIDIA RTX 4090 |
| GGUF Q4_K_M / AWQ (4-bit) | 4.85 bpw | 68.06 GB | 1.5 GB | 70.22 GB | No (Over by 46.2GB) | ~7.8 tok/s | 3x NVIDIA RTX 4090 |
| GGUF IQ4_XS (4.25 bpw) | 4.25 bpw | 59.64 GB | 1.5 GB | 61.8 GB | No (Over by 37.8GB) | ~8.9 tok/s | 3x NVIDIA RTX 4090 |
| GGUF Q3_K_M (3-bit) | 3.91 bpw | 54.87 GB | 1.5 GB | 57.03 GB | No (Over by 33.0GB) | ~9.7 tok/s | 3x NVIDIA RTX 4090 |
Frequently Asked Questions (Llama-4-Scout-17B-16E (109B MoE) + NVIDIA RTX 4090)
Can NVIDIA RTX 4090 (24GB) run Llama-4-Scout-17B-16E (109B MoE) locally?
No — A single NVIDIA RTX 4090 (24GB) cannot fit Llama-4-Scout-17B-16E (109B MoE) in VRAM. Even in 4-bit Q4_K_M, Llama-4-Scout-17B-16E (109B MoE) requires 70.22 GB VRAM (3x NVIDIA RTX 4090 GPUs needed for full VRAM execution, or heavy CPU RAM offloading at ~7.8 tok/s).
How is KV Cache calculated for Llama-4-Scout-17B-16E (109B MoE)?
Llama-4-Scout-17B-16E (109B MoE) uses MoE-GQA with 48 transformer layers, 40 attention heads, and 8 KV heads (head dimension 128). At 8,192 tokens in FP16, KV cache consumes 1.5 GB. Enabling 8-bit KV cache (-ctk q8_0 -ctv q8_0 in llama.cpp or --kv-cache-dtype fp8 in vLLM) cuts this in half to 0.75 GB.