Llama 4 Scout 17B 16E needs ~76.7 GB VRAM. NVIDIA A16 64GB has 64.0 GB. With Q4_K_M quantization, expect ~9 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
12.7 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~11 GB host RAM)
Decode
9.2 tok/s
TTFT
21125 ms
Safe context
4K
Memory
76.7 GB / 64.0 GB
Offload
20%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 20% 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 11.0 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Very compromised (needs ~9.9 GB host RAM) | 9.5 tok/s | 11065 ms | 4K |
| Coding | B | Very compromised (needs ~11 GB host RAM) | 9.2 tok/s | 21125 ms | 4K |
| Agentic Coding | F | Too heavy | 8.5 tok/s | 33250 ms | 4K |
| Reasoning | B | Very compromised (needs ~11 GB host RAM) | 9.2 tok/s | 24966 ms | 4K |
| RAG | F | Too heavy | 8.5 tok/s | 41563 ms | 4K |
How Llama 4 Scout 17B 16E (109B params) fits at each quantization level on NVIDIA A16 64GB (64.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_KBest for your GPU | 2 | 42.5 GB | Low | A76 |
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 startアップグレードオプション
RTX PRO 6000 Blackwell Workstation Edition 96GBコスパ重視Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 526%.
〜$9,999 MSRP
RTX PRO 6000 Blackwell Server Edition 96GBコスパ最良Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 458%.
〜$9,999 MSRP
NVIDIA H20 96GBNVIDIAアップグレードRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 1247%.
〜$12,000 MSRP
Yes, NVIDIA A16 64GB can run Llama 4 Scout 17B 16E with a B grade (Very compromised (needs ~11 GB host RAM)). Expected decode speed: 9.2 tok/s.
Llama 4 Scout 17B 16E (109B parameters) requires approximately 76.7 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 A16 64GB, Llama 4 Scout 17B 16E achieves approximately 9.2 tokens per second decode speed with a time-to-first-token of 21125ms using Q4_K_M quantization.
For coding workloads, Llama 4 Scout 17B 16E on NVIDIA A16 64GB receives a B grade with 9.2 tok/s and 4K context.
On NVIDIA A16 64GB, 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.
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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