Llama 4 Scout 17B 16E needs ~78.6 GB VRAM. NVIDIA A100 80GB has 80.0 GB. With Q4_K_M quantization, expect ~66 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
Fit status
Runs with offload
Decode
65.5 tok/s
TTFT
2956 ms
Safe context
24K
Memory
78.6 GB / 80.0 GB
This setup is broadly balanced for this model.
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.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs with offload | 65.5 tok/s | 1612 ms | 24K |
| Coding | A | Runs with offload | 65.5 tok/s | 2956 ms | 24K |
| Agentic Coding | A | Runs with offload (needs ~1.3 GB host RAM) | 54.1 tok/s | 5203 ms | 24K |
| Reasoning | A | Runs with offload | 65.5 tok/s | 3493 ms | 24K |
| RAG | A | Runs with offload (needs ~1.3 GB host RAM) | 54.1 tok/s | 6504 ms | 24K |
How Llama 4 Scout 17B 16E (109B params) fits at each quantization level on NVIDIA A100 80GB (80.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 42.5 GB | Low | A76 |
Q3_K_S | 3 | 53.4 GB | Low | A76 |
NVFP4Best for your GPU | 4 | 61.0 GB | Medium | A76 |
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 startYour hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Devstral 2 123B Instruct | 123B | A | 17.6 tok/s | |
 Qwen 3.5 122B A10B | 122B | A | 52.1 tok/s | |
| Mistral Small 4 119B | 119B | A | 55.3 tok/s | |
 GPT-OSS 120B | 117B | A | 20 tok/s | |
 Command A 111B | 111B | S | 23.2 tok/s |
Yes, NVIDIA A100 80GB can run Llama 4 Scout 17B 16E with a A grade (Runs with offload). Expected decode speed: 65.5 tok/s.
Llama 4 Scout 17B 16E (109B parameters) requires approximately 78.6 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 NVIDIA A100 80GB, Llama 4 Scout 17B 16E achieves approximately 65.5 tokens per second decode speed with a time-to-first-token of 2956ms using Q4_K_M quantization.
For coding workloads, Llama 4 Scout 17B 16E on NVIDIA A100 80GB receives a A grade with 65.5 tok/s and 24K context.
On NVIDIA A100 80GB, Llama 4 Scout 17B 16E can safely use up to 24K tokens of context. The model's official context limit is 10.5M, but available memory constrains the safe maximum.
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/llama-4-scout-17b-16e-on-a100-80gb" 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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