HARDWARE FIT CHECK • Meta Llama 4 Scout (109B-A17B MoE) + AMD Radeon RX 7900 XT (20GB GDDR6)71.66 GB / 20 GB VRAM

Can I Run Meta Llama 4 Scout (109B-A17B MoE) on AMD Radeon RX 7900 XT (20GB GDDR6)?

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 AMD Radeon RX 7900 XT (20GB GDDR6) (20 GB).

✕ OOM71.66 GB (358%)0 t/s

Meta Llama 4 Scout (109B-A17B MoE) on AMD Radeon RX 7900 XT (20GB GDDR6)

✕ Out of Memory — Exceeds VRAM Capacity

✕ OOM

Required VRAM

71.66 GB

358% of hardware capacity (20 GB)

Est. Decode Speed

0 tok/s

17B active params @ 800 GB/s

Available Headroom

-51.66 GB

Needs +51.66 GB more VRAM

Memory Allocation & Headroom

71.66 GB allocated of 20 GB capacity

358% utilized

Weights: 65.4 GB

KV: (32K): 3.22 GB

Overhead: 3.04 GB

Deficit: -51.66 GB

⚡ Architecture & Throughput Transparency: Single-user decode speed (~0 tok/s) is memory-bandwidth bound at batch size = 1 on AMD Radeon RX 7900 XT (20GB GDDR6) (800 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:scout

Local Hardware Configurations & Pricing

⚠️ Out of VRAM: High-Capacity Hardware Upgrades

High-bandwidth VRAM specs and current market prices

Multi-GPU Rig↑ Upgrade

48 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 →

Apple Silicon✓ Fits Model

112 GB VRAM

Apple Mac Studio M4 Max (128GB Unified Memory)

Running Llama 4 Scout 109B & 70B Q8 locally with 128K+ context

546 GB/s145W TDP

$3,499Amazon Prime →

Consumer GPU

20 GB VRAM

AMD Radeon RX 7900 XT (20GB GDDR6)

Sub-$700 GPU with 20GB VRAM fitting 14B Q8 and 27B Q4 models

800 GB/s315W TDP

$699Amazon Prime →

⚠️ Out of VRAM? Don't downgrade model quality.

Out of VRAM

Spin 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)

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$0.34/hr (RTX 4070 Ti) – $2.39/hr (H100)

Launch 1-Click GPU Pod →

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$0.18/hr (RTX 3060) – $0.55/hr (RTX 4090)

Rent Spot GPU →

Lambda GPU CloudEnterprise 8x H100 Clusters

$1.99/hr (GH200 / H100)

Check H100 Availability →

Quantization Precision Matrix

32K ctx • KV: Q8_0

Quantization	Weights	KV Cache	Total VRAM	Fit Status
FP1616 bpw	218 GB	3.22 GB	229.6 GB (1148%)	OOM
Q8_08.5 bpw	115.54 GB	3.22 GB	123.55 GB (618%)	OOM
Q6_K6.56 bpw	89.38 GB	3.22 GB	96.48 GB (482%)	OOM
Q5_K_M5.68 bpw	77.39 GB	3.22 GB	84.07 GB (420%)	OOM
Q4_K_M4.8 bpw	65.4 GB	3.22 GB	71.66 GB (358%)	OOM
FP44.25 bpw	57.77 GB	3.22 GB	63.76 GB (319%)	OOM
IQ3_XS3.3 bpw	44.69 GB	3.22 GB	50.22 GB (251%)	OOM

Export Full Architecture & Infrastructure Spec

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FAQ & Reference

Frequently Asked Questions: Meta Llama 4 Scout (109B-A17B MoE) on AMD Radeon RX 7900 XT (20GB GDDR6)

How much VRAM does Meta Llama 4 Scout (109B-A17B MoE) need on AMD Radeon RX 7900 XT (20GB GDDR6)?

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 AMD Radeon RX 7900 XT (20GB GDDR6)?

Based on AMD Radeon RX 7900 XT (20GB GDDR6)'s 800 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.