HARDWARE FIT CHECK โข Meta Llama 4 Scout (109B-A17B MoE) + NVIDIA GeForce RTX 5090 (32GB GDDR7)71.66 GB / 32 GB VRAM
Can I Run Meta Llama 4 Scout (109B-A17B MoE) on NVIDIA GeForce RTX 5090 (32GB GDDR7)?
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 5090 (32GB GDDR7) (32 GB).
โ Out of Memory (OOM) โ Exceeds Hardware VRAM71.66 GB (224%)0 t/s
Meta Llama 4 Scout (109B-A17B MoE) on NVIDIA GeForce RTX 5090 (32GB GDDR7)
โ Out of Memory โ Exceeds VRAM Capacity
โ OOM
Required VRAM
71.66 GB
224% of hardware capacity (32 GB)Est. Decode Speed
0 tok/s
17B active params @ 1792 GB/sAvailable Headroom
-39.66 GB
Needs +39.66 GB more VRAMMemory Allocation & Headroom
71.66 GB allocated of 32 GB capacity
224% utilized
Weights: 65.4 GB
KV: (32K): 3.22 GB
Overhead: 3.04 GB
Deficit: -39.66 GB
โก Architecture & Throughput Transparency: Single-user decode speed (~0 tok/s) is memory-bandwidth bound at batch size = 1 on NVIDIA GeForce RTX 5090 (32GB GDDR7) (1792 GB/s). Prompt prefill / TTFT (~0 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:scoutโ ๏ธ 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)
RunPod Cloud GPUsBest 1-Click Ollama & vLLM
$0.34/hr (RTX 4070 Ti) โ $2.39/hr (H100)Vast.ai GPU MarketplaceCheapest Dual RTX 3090 / 4090
$0.18/hr (RTX 3060) โ $0.55/hr (RTX 4090)Lambda GPU CloudEnterprise 8x H100 Clusters
$1.99/hr (GH200 / H100)Quantization Precision Matrix
32K ctx โข KV: Q8_0| Quantization | Weights | KV Cache | Total VRAM | Est. Speed | Fit Status |
|---|---|---|---|---|---|
| FP1616 bpw | 218 GB | 3.22 GB | 229.6 GB (718%) | 0 t/s | OOM |
| Q8_08.5 bpw | 115.54 GB | 3.22 GB | 123.55 GB (386%) | 0 t/s | OOM |
| Q6_K6.56 bpw | 89.38 GB | 3.22 GB | 96.48 GB (302%) | 0 t/s | OOM |
| Q5_K_M5.68 bpw | 77.39 GB | 3.22 GB | 84.07 GB (263%) | 0 t/s | OOM |
| Q4_K_M4.8 bpw | 65.4 GB | 3.22 GB | 71.66 GB (224%) | 0 t/s | OOM |
| FP44.25 bpw | 57.77 GB | 3.22 GB | 63.76 GB (199%) | 0 t/s | OOM |
| IQ3_XS3.3 bpw | 44.69 GB | 3.22 GB | 50.22 GB (157%) | 0 t/s | OOM |
Local Hardware Configurations & Pricing
High-bandwidth VRAM specs and current market pricesConsumer GPU32 GB VRAM
NVIDIA GeForce RTX 5090 (32GB GDDR7)
Maximum tokens/sec on 27Bโ35B models & Blackwell FP4 quantization
1792 GB/s575W TDP
$1,999Amazon Prime โ
Consumer GPU24 GB VRAM
NVIDIA GeForce RTX 4090 (24GB GDDR6X)
The 24GB CUDA enthusiast standard for 14Bโ32B Q4_K_M models
1008 GB/s450W TDP
$1,699Amazon Prime โ
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 โ
Consumer GPU16 GB VRAM
NVIDIA GeForce RTX 5080 (16GB GDDR7)
Blazing fast 8Bโ14B coding models with FP4/Q6_K precision
960 GB/s360W TDP
$999Amazon Prime โ
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