Llama 4 Scout 17B 16E needs ~98.0 GB VRAM. Mac Studio M3 Ultra 256GB has 184.3 GB. With Q4_K_M quantization, expect ~21 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
21.3 tok/s
TTFT
9091 ms
Safe context
488K
Memory
98.0 GB / 184.3 GB
This setup is broadly balanced for this model.
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs well | 21.3 tok/s | 4959 ms | 488K |
| Coding | A | Runs well | 21.3 tok/s | 9091 ms | 488K |
| Agentic Coding | A | Runs well | 19.7 tok/s | 14281 ms | 488K |
| Reasoning | A | Runs well | 21.3 tok/s | 10744 ms | 488K |
| RAG | A | Runs well | 21.3 tok/s | 16529 ms | 488K |
How Llama 4 Scout 17B 16E (109B params) fits at each quantization level on Mac Studio M3 Ultra 256GB (184.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 42.5 GB | Low | B69 |
Q3_K_S | 3 | 53.4 GB | Low | B70 |
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 | 8.1 tok/s | |
 Qwen 3.5 122B A10B | 122B | S |
Yes, Mac Studio M3 Ultra 256GB can run Llama 4 Scout 17B 16E with a A grade (Runs well). Expected decode speed: 21.3 tok/s.
Llama 4 Scout 17B 16E (109B parameters) requires approximately 98.0 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 Mac Studio M3 Ultra 256GB, Llama 4 Scout 17B 16E achieves approximately 21.3 tokens per second decode speed with a time-to-first-token of 9091ms using Q4_K_M quantization.
For coding workloads, Llama 4 Scout 17B 16E on Mac Studio M3 Ultra 256GB receives a A grade with 21.3 tok/s and 488K context.
On Mac Studio M3 Ultra 256GB, Llama 4 Scout 17B 16E can safely use up to 488K 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-m3-ultra-256gb" 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 |
| A71 |
Q4_K_M | 4 | 66.5 GB | Medium | A71 |
Q5_K_M | 5 | 78.5 GB | High | A73 |
Q6_K | 6 | 89.4 GB | High | A74 |
Q8_0Best for your GPU | 8 | 116.6 GB | Very High | A76 |
F16 | 16 | 223.5 GB | Maximum | F0 |
| 34.7 tok/s |
 DeepSeek V4 Flash | 284B | S | 17.8 tok/s |
| Mistral Small 4 119B | 119B | S | 37.6 tok/s |
 GPT-OSS 120B | 117B | S | 8.5 tok/s |
Not always. Mac Studio M3 Ultra 256GB 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.