✅ FITS IN (W8A8 QUANTIZATION
Llama-4-Scout-17B-16E (109B MoE) on AMD Instinct MI300X 192GB (192GB VRAM)
Full FP16 (226.7 GB) exceeds 192 GB VRAM, but 4-bit GGUF Q4_K_M / AWQ requires 70.22 GB (68.06 GB weights + 1.5 GB KV cache at 8K context), delivering ~280 tokens/sec.
🎛️ Interactive VRAM & KV-Cache Tuner
📊 Real-Time Memory Footprint
70.22 GB / 192 GB
🟦 Weights (68.06 GB)
🟪 KV Cache (1.5 GB)
🟨 Activation Buffer (0.66 GB)
Total Required VRAM70.22 GB
Available Hardware VRAM192 GB (AMD Instinct MI300X 192GB)
VRAM Headroom / Overflow121.78 GB
Estimated Generation Speed~280 tokens/sec
Architecture & AttentionMoE-GQA (48L, 8 KV heads)
🚀 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
CLOUD GPU ALTERNATIVE
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 AMD Instinct MI300X 192GB
| Quantization Format | Bits/Weight | Weights VRAM | KV Cache (8K) | Total VRAM | Fits on 1x AMD Instinct MI300X 192GB (192GB)? | Est. Speed | GPUs Needed |
|---|---|---|---|---|---|---|---|
| FP16 / BF16 (Unquantized) | 16 bpw | 224.54 GB | 1.5 GB | 226.7 GB | No (Over by 34.7GB) | ~12.5 tok/s | 2x AMD Instinct MI300X 192GB |
| FP8 (W8A8 / E4M3) | 8.5 bpw | 119.29 GB | 1.5 GB | 121.45 GB | Yes (63%) | ~181.9 tok/s | 1x AMD Instinct MI300X 192GB |
| GGUF Q8_0 (8-bit) | 8.5 bpw | 119.29 GB | 1.5 GB | 121.45 GB | Yes (63%) | ~181.9 tok/s | 1x AMD Instinct MI300X 192GB |
| GGUF Q6_K (6-bit) | 6.56 bpw | 92.06 GB | 1.5 GB | 94.22 GB | Yes (49%) | ~235.7 tok/s | 1x AMD Instinct MI300X 192GB |
| GGUF Q5_K_M (5-bit) | 5.69 bpw | 79.85 GB | 1.5 GB | 82.01 GB | Yes (43%) | ~271.8 tok/s | 1x AMD Instinct MI300X 192GB |
| GGUF Q4_K_M / AWQ (4-bit) | 4.85 bpw | 68.06 GB | 1.5 GB | 70.22 GB | Yes (37%) | ~280 tok/s | 1x AMD Instinct MI300X 192GB |
| GGUF IQ4_XS (4.25 bpw) | 4.25 bpw | 59.64 GB | 1.5 GB | 61.8 GB | Yes (32%) | ~280 tok/s | 1x AMD Instinct MI300X 192GB |
| GGUF Q3_K_M (3-bit) | 3.91 bpw | 54.87 GB | 1.5 GB | 57.03 GB | Yes (30%) | ~280 tok/s | 1x AMD Instinct MI300X 192GB |
Frequently Asked Questions (Llama-4-Scout-17B-16E (109B MoE) + AMD Instinct MI300X 192GB)
Can AMD Instinct MI300X 192GB (192GB) run Llama-4-Scout-17B-16E (109B MoE) locally?
Yes — AMD Instinct MI300X 192GB (192GB) can run Llama-4-Scout-17B-16E (109B MoE) using FP8 (W8A8 / E4M3). Full FP16 (226.7 GB) exceeds 192 GB VRAM, but 4-bit GGUF Q4_K_M / AWQ requires 70.22 GB (68.06 GB weights + 1.5 GB KV cache at 8K context), delivering ~280 tokens/sec.
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.
Compare Llama-4-Scout-17B-16E (109B MoE) on Other GPUs
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