Llama 4 Scout 17B 16E needs ~51.1 GB VRAM. Radeon Pro W7900 48GB has 48.0 GB. With Q2_K quantization, expect ~17 tok/s.
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
27.1 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
5.7 tok/s
TTFT
33995 ms
Safe context
4K
Memory
75.1 GB / 48.0 GB
Offload
40%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly 2.6 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 5.9 tok/s | 17790 ms | 4K |
| Coding | F | Too heavy | 5.7 tok/s | 33995 ms | 4K |
| Agentic Coding | F | Too heavy | 5.3 tok/s | 53594 ms | 4K |
| Reasoning | F | Too heavy | 5.7 tok/s | 40176 ms | 4K |
| RAG | F | Too heavy | 5.3 tok/s | 66993 ms | 4K |
How Llama 4 Scout 17B 16E (109B params) fits at each quantization level on Radeon Pro W7900 48GB (48.0 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 |
Copy-paste commands to run Llama 4 Scout 17B 16E on your machine.
Run
lms load Llama-4-Scout-17B-16E-Instruct && lms server startOpciones de mejora
Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.
~$8,000 MSRP
Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Sube la velocidad estimada de decodificación alrededor de un 282%.
~$10,000 MSRP
Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.
~$12,000 MSRP
Yes, Radeon Pro W7900 48GB can run Llama 4 Scout 17B 16E at Q2_K quantization (Runs with offload (needs ~2.6 GB host RAM)). The recommended Q4_K_M requires 75.1 GB which exceeds available memory, but at Q2_K it needs only 51.1 GB. Expected decode speed: 17.0 tok/s.
Llama 4 Scout 17B 16E (109B parameters) requires approximately 75.1 GB at Q4_K_M quantization. On Radeon Pro W7900 48GB, it fits at Q2_K using 51.1 GB.
The recommended quantization is Q4_K_M, but on Radeon Pro W7900 48GB the best fitting quantization is Q2_K, which uses 51.1 GB.
On Radeon Pro W7900 48GB, Llama 4 Scout 17B 16E achieves approximately 17.0 tokens per second decode speed with a time-to-first-token of 11382ms using Q2_K quantization.
For coding workloads, Llama 4 Scout 17B 16E on Radeon Pro W7900 48GB receives a F grade with 5.7 tok/s and 4K context.
On Radeon Pro W7900 48GB, Llama 4 Scout 17B 16E can safely use up to 4K tokens of context at Q2_K quantization. The model's official context limit is 10.5M, but available memory constrains the safe maximum.
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
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