Llama 4 Scout 17B 16E needs ~73.5 GB but Radeon AI PRO R9700 32GB only has 32.0 GB. Try a smaller quantization or lighter model.
Operating mode
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
41.5 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
2.2 tok/s
TTFT
89386 ms
Safe context
4K
Memory
73.5 GB / 32.0 GB
Offload
60%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 73.5 GB, but this setup only exposes 32.0 GB of usable VRAM.
Add more VRAM headroom
The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 2.2 tok/s | 48756 ms | 4K |
| Coding | F | Too heavy | 2.2 tok/s | 89386 ms | 4K |
| Agentic Coding | F | Too heavy | 2.2 tok/s | 130016 ms | 4K |
| Reasoning | F | Too heavy | 2.2 tok/s | 105638 ms | 4K |
| RAG | F | Too heavy | 2.2 tok/s | 162520 ms | 4K |
How Llama 4 Scout 17B 16E (109B params) fits at each quantization level on Radeon AI PRO R9700 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 42.5 GB | Low | F0 |
Q3_K_S | 3 | 53.4 GB | Low | F0 |
NVFP4 | 4 | 61.0 GB | Medium | F0 |
Q4_K_M | 4 | 66.5 GB | Medium | F0 |
Q5_K_M | 5 | 78.5 GB | High | F0 |
Q6_K | 6 | 89.4 GB | High | F0 |
Q8_0 | 8 | 116.6 GB | Very High | F0 |
F16 | 16 | 223.5 GB | Maximum | F0 |
Upgrade-Optionen
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
ca. $8,000 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 891%.
ca. $10,000 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
ca. $12,000 MSRP
No, Llama 4 Scout 17B 16E requires more memory than Radeon AI PRO R9700 32GB provides.
Llama 4 Scout 17B 16E (109B parameters) requires approximately 73.5 GB of memory with Q4_K_M quantization.
The recommended quantization for Llama 4 Scout 17B 16E is Q4_K_M, which balances quality and memory efficiency.
On Radeon AI PRO R9700 32GB, Llama 4 Scout 17B 16E achieves approximately 2.2 tokens per second decode speed with a time-to-first-token of 89386ms using Q4_K_M quantization.
For coding workloads, Llama 4 Scout 17B 16E on Radeon AI PRO R9700 32GB receives a F grade with 2.2 tok/s and 4K context.
On Radeon AI PRO R9700 32GB, Llama 4 Scout 17B 16E can safely use up to 4K tokens of context. The model's official context limit is 10.5M, but available memory constrains the safe maximum.
Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/llama-4-scout-17b-16e-on-radeon-ai-pro-r9700-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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