Thurmon Demich

Posted on May 24 • Originally published at bestgpuforllm.com

Best GPU for Llama 4 Scout (109B MoE) in 2026 Ranked

#gpu #llama4 #scout #llm

Cross-posted from Best GPU for LLM — visit the original for our VRAM calculator, GPU comparison table, and current Amazon pricing.

Quick answer: The RTX 5090 (32GB) is the only single consumer GPU that runs Llama 4 Scout locally at usable quality. At Q4_K_M, Scout needs ~25GB VRAM. For everyone else, a used dual-RTX-3090 setup or cloud GPU on RunPod is the practical path.

What is Llama 4 Scout?

Llama 4 Scout is Meta's mixture-of-experts (MoE) model released in April 2025. The architecture:

Total parameters: 109 billion
Active parameters per forward pass: 17 billion (only 16 experts are active at once)
Context window: up to 10 million tokens (theoretical)
Architecture: 16 experts, 1 active per token

The MoE design means Scout behaves like a 17B dense model during inference — not a 109B model. You only compute 17B worth of activations per token. But you must store all 109B parameters in memory simultaneously, which is the VRAM bottleneck.

Llama 4 Scout VRAM requirements

Quantization	Weight Size	VRAM Needed	Single Card?
FP16	~218GB	220GB+	No — multi-GPU cluster
Q8	~109GB	115GB+	No
Q6_K	~82GB	88GB+	No
Q4_K_M	~55GB... wait — MoE adjusted	~25GB	RTX 5090 (32GB)
Q3_K_M	~20GB	~22GB	RTX 4090 (tight)
Q2_K	~14GB	~16GB	RTX 4060 Ti 16GB (degraded)

Note on MoE quantization: Because only 16 out of 109B effective parameters are stored in distinct expert blocks, quantized MoE models compress much better than dense equivalents. Community-quantized Scout GGUF files at Q4_K_M run around 23-27GB depending on the implementation. Plan for 25GB minimum.

VRAM chart available at the original article

GPU benchmarks for Llama 4 Scout

Estimated performance running Scout Q4_K_M with llama.cpp:

GPU	VRAM	Fits Scout Q4?	~Tok/s	Price
RTX 5090	32GB	Yes (comfortable)	~22 tok/s	~$2,000
RTX 4090	24GB	Q3_K_M only (tight)	~12 tok/s	~$1,600
2x RTX 3090	24GB+24GB	Yes (tensor parallel)	~20 tok/s	~$1,800 used
2x RTX 4090	24GB+24GB	Yes, very fast	~35 tok/s	~$3,200
Cloud A100 80GB	80GB	Yes, comfortable	~45 tok/s	RunPod ~$2/hr
Cloud H100 80GB	80GB	Yes, very fast	~80 tok/s	RunPod ~$3.5/hr

The dual RTX 3090 setup is the best value for local Scout inference — two used 3090s come in around $1,800 total and deliver comparable performance to the RTX 5090 at Q4_K_M.

Best GPU options for Llama 4 Scout

RTX 5090 — Best single-card option

The only consumer GPU where Scout runs without compromise. 32GB of GDDR7 fits Q4_K_M with headroom for a 8K context window. If you want a one-card solution and are willing to pay ~$2,000, this is it.

Dual RTX 3090 — Best value local setup

Two used RTX 3090s at ~$900 each gives you 48GB of combined VRAM via tensor parallelism with llama.cpp. This setup delivers similar throughput to the RTX 5090 at lower total cost, especially if you can find good used prices. The trade: complexity of dual-GPU configuration and a compatible motherboard with dual PCIe x16 slots.

Cloud GPU — Best for occasional use

If you run Scout occasionally rather than daily, cloud is cheaper than buying either setup. RunPod's A100 80GB instances at ~$2/hr fit Scout comfortably and deliver faster throughput than any consumer GPU.

Which setup should YOU use for Llama 4 Scout?

Want single-card simplicity? RTX 5090 ($2,000). Only card that fits Scout Q4_K_M on one GPU. Fast GDDR7 bandwidth keeps throughput reasonable.
Want best value locally? 2x RTX 3090 used (~$1,800 total). More VRAM than a single 5090, matched throughput, lower cost — if you can handle dual-GPU setup.
Run Scout occasionally (weekly or less)? Cloud GPU on RunPod. At $2/hr on an A100, you need 900 hours of use to match the cost of buying two 3090s. Occasional users save money in the cloud.
Have an RTX 4090 already? Run Scout at Q3_K_M — it fits with ~22GB. Quality is slightly degraded versus Q4_K_M but usable for most tasks.

Common mistakes to avoid

Thinking Scout runs like a 17B model in terms of VRAM. The 17B active parameters is a compute figure, not a memory figure. All 109B worth of expert weights live in VRAM simultaneously. You need ~25GB for Q4_K_M.
Buying a single RTX 4090 specifically for Scout. 24GB barely fits Q3_K_M with no context headroom. For dedicated Scout inference, the 5090 or dual setup is the right tool.
Underestimating context overhead. Scout's long-context design means a 32K token context window adds gigabytes of KV cache. At long contexts, even the 5090's 32GB gets tight. Keep context windows short or use cloud.
Skipping quantization testing. Q4_K_M MoE quantization quality varies between community implementations. Test a few GGUF files — some Scout quantizations are better than others.

Final verdict

Setup	VRAM	Scout Q4_K_M?	~Tok/s	Cost
RTX 5090	32GB	Yes	~22 tok/s	~$2,000
2x RTX 3090	48GB	Yes	~20 tok/s	~$1,800 used
RTX 4090	24GB	Q3_K_M only	~12 tok/s	~$1,600
RunPod A100	80GB	Yes	~45 tok/s	~$2/hr

For the full Llama 4 lineup across all model sizes, see our best GPU for Llama 4 guide. Curious about Scout's VRAM needs in more depth? See how much VRAM for Llama 4. For a dedicated look at what the larger Maverick model demands, see our Llama 4 Maverick hardware summary. For cloud-first LLM inference setup, check best GPU for Ollama.

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