Llama 4 Scout 17B 16E needs ~80.2 GB VRAM. NVIDIA H20 96GB has 96.0 GB. With Q4_K_M quantization, expect ~124 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
Tight fit
Decode
123.9 tok/s
TTFT
1563 ms
Safe context
102K
Memory
80.2 GB / 96.0 GB
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Tight fit | 123.9 tok/s | 852 ms | 102K |
| Coding | A | Tight fit | 123.9 tok/s | 1563 ms | 102K |
| Agentic Coding | A | Tight fit | 123.9 tok/s | 2273 ms | 102K |
| Reasoning | A | Tight fit | 123.9 tok/s | 1847 ms | 102K |
| RAG | A | Tight fit | 123.9 tok/s | 2841 ms | 102K |
Inference speed
Estimated decode speed (tokens/sec) for Llama 4 Scout 17B 16E at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is Mac Studio M3 Ultra 256GB at ~21 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 21.3 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 17.7 | Tight |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 16.8 | Tight |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 13.2 | Tight |
2× RX 7900 XTX 24GB | 48 GB | Q4_K_M | 13.1 | Too big |
 2× RTX 4090 24GB | 48 GB | Q4_K_M | 8.7 | Too big |
| 2× RTX 3090 24GB | 48 GB | Q4_K_M | 7.5 | Too big |
| RTX 5090 32GB | 32 GB | Q4_K_M | 6.9 | Too big |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 6.8 | Too big |
| 4× RTX 3060 12GB | 48 GB | Q4_K_M | 6.6 | Too big |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 4.7 | Too big |
| RTX 4090 24GB | 24 GB | Q4_K_M | 4.4 | Too big |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 4.3 | Too big |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 4.0 | Too big |
| RTX 3090 24GB | 24 GB | Q4_K_M | 3.8 | Too big |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 3.6 | Too big |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 3.5 | Too big |
| RTX 4070 12GB | 12 GB | Q4_K_M | 2.2 | Too big |
| RTX 3060 12GB | 12 GB | Q4_K_M | 2.0 | Too big |
| RTX 4060 8GB | 8 GB | Q4_K_M | 2.0 | Too big |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
How Llama 4 Scout 17B 16E (109B params) fits at each quantization level on NVIDIA H20 96GB (96.0 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 | 61.0 GB | Medium | A76 |
Q4_K_M | 4 | 66.5 GB | Medium | A76 |
Q5_K_MBest for your GPU | 5 | 78.5 GB | High | A76 |
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 | S | 47 tok/s | |
 Qwen 3.5 122B A10B | 122B | S | 130.3 tok/s | |
| Mistral Small 4 119B | 119B | S | 141.2 tok/s | |
 GPT-OSS 120B | 117B | S | 49.4 tok/s | |
 Command A 111B | 111B | S | 52.2 tok/s |
Yes, NVIDIA H20 96GB can run Llama 4 Scout 17B 16E with a A grade (Tight fit). Expected decode speed: 123.9 tok/s.
Llama 4 Scout 17B 16E (109B parameters) requires approximately 80.2 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 H20 96GB, Llama 4 Scout 17B 16E achieves approximately 123.9 tokens per second decode speed with a time-to-first-token of 1563ms using Q4_K_M quantization.
For coding workloads, Llama 4 Scout 17B 16E on NVIDIA H20 96GB receives a A grade with 123.9 tok/s and 102K context.
On NVIDIA H20 96GB, Llama 4 Scout 17B 16E can safely use up to 102K 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-h20-96gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: