Llama 4 Scout 17B 16E needs ~83.7 GB VRAM. NVIDIA DGX Spark 128GB has 108.8 GB. With Q4_K_M quantization, expect ~6 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 well
Decode
6.3 tok/s
TTFT
30909 ms
Safe context
153K
Memory
83.7 GB / 108.8 GB
The model fits in shared memory, but shared-memory bandwidth is now the real limiter.
Fit does not mean dedicated-VRAM speed
Unified or shared memory can make a model technically fit, but sustained tokens per second may still trail a discrete high-bandwidth GPU with less total memory.
Shared-memory contention still exists
The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.
Prioritize bandwidth, not only capacity
If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs well | 6.3 tok/s | 16859 ms | 153K |
| Coding | A | Runs well | 6.3 tok/s | 30909 ms | 153K |
| Agentic Coding | A | Runs well | 6.3 tok/s | 44958 ms | 153K |
| Reasoning | A | Runs well | 6.3 tok/s | 36528 ms | 153K |
| RAG | A | Runs well | 6.3 tok/s | 56197 ms | 153K |
How Llama 4 Scout 17B 16E (109B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 42.5 GB | Low | A74 |
Q3_K_S | 3 | 53.4 GB | Low | A76 |
NVFP4 | 4 |
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 | S | 2.4 tok/s | |
 Qwen 3.5 122B A10B | 122B | S |
Yes, NVIDIA DGX Spark 128GB can run Llama 4 Scout 17B 16E with a A grade (Runs well). Expected decode speed: 6.3 tok/s.
Llama 4 Scout 17B 16E (109B parameters) requires approximately 83.7 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 DGX Spark 128GB, Llama 4 Scout 17B 16E achieves approximately 6.3 tokens per second decode speed with a time-to-first-token of 30909ms using Q4_K_M quantization.
For coding workloads, Llama 4 Scout 17B 16E on NVIDIA DGX Spark 128GB receives a A grade with 6.3 tok/s and 153K context.
On NVIDIA DGX Spark 128GB, Llama 4 Scout 17B 16E can safely use up to 153K 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-dgx-spark-128gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
61.0 GB |
| Medium |
| A76 |
Q4_K_MBest for your GPU | 4 | 66.5 GB | Medium | A76 |
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 |
| 6.6 tok/s |
| Mistral Small 4 119B | 119B | S | 7.1 tok/s |
 GPT-OSS 120B | 117B | A | 2.5 tok/s |
 Command A 111B | 111B | S | 2.6 tok/s |
Prioritize bandwidth, not only capacity. If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.
Not always. NVIDIA DGX Spark 128GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.