HARDWARE FIT CHECK • Meta Llama 4 Scout (109B-A17B MoE) + NVIDIA H100 SXM (80GB HBM3 Cloud GPU)71.66 GB / 80 GB VRAM
Can I Run Meta Llama 4 Scout (109B-A17B MoE) on NVIDIA H100 SXM (80GB HBM3 Cloud GPU)?
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 H100 SXM (80GB HBM3 Cloud GPU) (80 GB).
⚡ Fits in VRAM (Tight Headroom — Use Q8/Q4 KV Cache)71.66 GB (90%)~219 t/s
Meta Llama 4 Scout (109B-A17B MoE) on NVIDIA H100 SXM (80GB HBM3 Cloud GPU)
⚡ Tight Fit — Runs 100% on GPU (Low Headroom)
⚡ TIGHT FIT
Required VRAM
71.66 GB
90% of hardware capacity (80 GB)Est. Decode Speed
~219 tok/s
17B active params @ 3350 GB/sAvailable Headroom
+8.34 GB
Safe for OS display & full contextMemory Allocation & Headroom
71.66 GB allocated of 80 GB capacity
90% utilized
Weights: 65.4 GB
KV: (32K): 3.22 GB
Overhead: 3.04 GB
Free Headroom: +8.34 GB
⚡ Architecture & Throughput Transparency: Single-user decode speed (~219 tok/s) is memory-bandwidth bound at batch size = 1 on NVIDIA H100 SXM (80GB HBM3 Cloud GPU) (3350 GB/s). Prompt prefill / TTFT (~3,176 tok/s) is compute-bound and utilizes GPU Tensor cores. Actual latency varies with FlashAttention-2 and runtime engine (Ollama/vLLM/llama.cpp).
OLLAMA_FLASH_ATTENTION=1 OLLAMA_KV_CACHE_TYPE=q8_0 ollama run llama4:scoutCloud Instance Options for Meta Llama 4 Scout (109B-A17B MoE)
On-DemandDeploy this model on an on-demand cloud GPU without local hardware constraints or power usage:
RunPod Cloud GPUsBest 1-Click Ollama & vLLM
$0.34/hr (RTX 4070 Ti) – $2.39/hr (H100)Vast.ai GPU MarketplaceCheapest Dual RTX 3090 / 4090
$0.18/hr (RTX 3060) – $0.55/hr (RTX 4090)Lambda GPU CloudEnterprise 8x H100 Clusters
$1.99/hr (GH200 / H100)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 (287%) | 0 t/s | OOM |
| Q8_08.5 bpw | 115.54 GB | 3.22 GB | 123.55 GB (154%) | 0 t/s | OOM |
| Q6_K6.56 bpw | 89.38 GB | 3.22 GB | 96.48 GB (121%) | ~36 t/s | PARTIAL |
| Q5_K_M5.68 bpw | 77.39 GB | 3.22 GB | 84.07 GB (105%) | ~41 t/s | PARTIAL |
| Q4_K_M4.8 bpw | 65.4 GB | 3.22 GB | 71.66 GB (90%) | ~219 t/s | FITS |
| FP44.25 bpw | 57.77 GB | 3.22 GB | 63.76 GB (80%) | ~246 t/s | FITS |
| IQ3_XS3.3 bpw | 44.69 GB | 3.22 GB | 50.22 GB (63%) | ~310 t/s | FITS |
Local Hardware Configurations & Pricing
High-bandwidth VRAM specs and current market pricesDatacenter Cloud GPU80 GB VRAM
NVIDIA H100 SXM (80GB HBM3 Cloud GPU)
Production vLLM high-concurrency serving & 70B FP8 inference
3350 GB/s700W TDP
$28,000Amazon Prime →
Consumer GPU32 GB VRAM
NVIDIA GeForce RTX 5090 (32GB GDDR7)
Maximum tokens/sec on 27B–35B models & Blackwell FP4 quantization
1792 GB/s575W TDP
$1,999Amazon Prime →
Consumer GPU24 GB VRAM
NVIDIA GeForce RTX 4090 (24GB GDDR6X)
The 24GB CUDA enthusiast standard for 14B–32B Q4_K_M models
1008 GB/s450W TDP
$1,699Amazon Prime →
Multi-GPU Rig48 GB VRAM
2x NVIDIA RTX 3090 Dual Rig (48GB Total VRAM)
Best price-to-VRAM homelab rig for 70B Q4_K_M & Qwen 3.8 27B Q8
936 GB/s700W TDP
$1,550Amazon Prime →
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