Llama 4 Scout 17B 16E needs ~73.0 GB but RTX A5000 24GB only has 24.0 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
49.0 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
3.1 tok/s
TTFT
62783 ms
Safe context
4K
Memory
73.0 GB / 24.0 GB
Offload
70%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 73.0 GB, but this setup only exposes 24.0 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 | 3.1 tok/s | 34245 ms | 4K |
| Coding | F | Too heavy | 3.1 tok/s | 62783 ms | 4K |
| Agentic Coding | F | Too heavy | 3.1 tok/s | 91321 ms | 4K |
| Reasoning | F | Too heavy | 3.1 tok/s | 74198 ms | 4K |
| RAG | F | Too heavy | 3.1 tok/s | 114151 ms | 4K |
How Llama 4 Scout 17B 16E (109B params) fits at each quantization level on RTX A5000 24GB (24.0 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 |
Upgrade options
RTX PRO 6000 Blackwell Workstation Edition 96GBBudget pickMakes 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.
~$9,999 MSRP
RTX PRO 6000 Blackwell Server Edition 96GBBest valueMakes 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.
~$9,999 MSRP
NVIDIA H20 96GBNVIDIA upgradeMakes 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.
~$12,000 MSRP
No, Llama 4 Scout 17B 16E requires more memory than RTX A5000 24GB provides.
Llama 4 Scout 17B 16E (109B parameters) requires approximately 73.0 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 RTX A5000 24GB, Llama 4 Scout 17B 16E achieves approximately 3.1 tokens per second decode speed with a time-to-first-token of 62783ms using Q4_K_M quantization.
For coding workloads, Llama 4 Scout 17B 16E on RTX A5000 24GB receives a F grade with 3.1 tok/s and 4K context.
On RTX A5000 24GB, 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/llama-4-scout-17b-16e-on-a5000-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 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 |
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.
