MetaLlama 4 Scout on NVIDIA Grace Blackwell Ultra GB300
Yes — Grace Blackwell Ultra GB300 runs Llama 4 Scout excellently at Q8_0 — 40 tok/s. 288 GB VRAM with plenty of headroom.
Model Size
109B
Device VRAM
288 GB
Bandwidth
8000 GB/s
Quantization
Q8_0
Performance by Quantization
OwnRig currently has one published compatibility entry for Llama 4 Scout on NVIDIA Grace Blackwell Ultra GB300 at Q8_0. This is the best supported pairing we can stand behind today.
| Quantization | Speed | TTFT | Fits in VRAM | Rating | Confidence |
|---|---|---|---|---|---|
| Q8_0 | 40 tok/s | 200ms | ✓ Yes | Excellent | estimated |
Notes
Q8_0
Full Q8 quality 109B MoE model. 178GB headroom. One of few devices that can run this at full quality.
About Llama 4 Scout
Llama 4 Scout (109B) is a chat, reasoning, multi-purpose model. Large MoE model (~109B total, ~17B active per token; 16 experts, 2 active). Multimodal (text and image) with an enormous advertised context window (10M tokens); practical local runs are usually capped by VRAM and tooling far below that. MoE loads the full weight set for common local inference paths. Not a consumer GPU model: even Q4_K_M needs ~60GB VRAM. Expect H100-class or dual A100 hardware for practical deployment. Do not recommend to RTX 4090 or Mac users.
View all Llama 4 Scout hardware options →About NVIDIA Grace Blackwell Ultra GB300
NVIDIA Grace Blackwell Ultra GB300 has 288 GB at 8000 GB/s. Street price: $30,000.
See all models NVIDIA Grace Blackwell Ultra GB300 can run →Estimate method: Estimated from GB300 bandwidth ratio vs RTX 4090. Reference hardware source: dell.com (2026-03-23)
Performance varies by driver version, inference engine, quantization method, context length, and system configuration. Figures shown are estimates based on community benchmarks and may not reflect your exact setup. Product names are trademarks of their respective owners. OwnRig is independent and not affiliated with any hardware or AI model provider.