Llama 4 Scout 17B 16E needs ~80.2 GB VRAM. RTX PRO 6000 Blackwell Server Edition 96GB has 96.0 GB. With Q4_K_M quantization, expect ~51 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
51.3 tok/s
TTFT
3774 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 | 51.3 tok/s | 2059 ms | 102K |
| Coding | A | Tight fit | 51.3 tok/s | 3774 ms | 102K |
| Agentic Coding | A | Tight fit | 51.3 tok/s | 5490 ms | 102K |
| Reasoning | A | Tight fit | 51.3 tok/s | 4460 ms | 102K |
| RAG | A | Tight fit | 51.3 tok/s | 6862 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 RTX PRO 6000 Blackwell Server Edition 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 | 19.4 tok/s | |
 Qwen 3.5 122B A10B | 122B | S | 53.9 tok/s | |
| Mistral Small 4 119B | 119B | S | 58.5 tok/s | |
 GPT-OSS 120B | 117B | S | 20.4 tok/s | |
 Command A 111B | 111B | S | 21.6 tok/s |
Yes, RTX PRO 6000 Blackwell Server Edition 96GB can run Llama 4 Scout 17B 16E with a A grade (Tight fit). Expected decode speed: 51.3 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 RTX PRO 6000 Blackwell Server Edition 96GB, Llama 4 Scout 17B 16E achieves approximately 51.3 tokens per second decode speed with a time-to-first-token of 3774ms using Q4_K_M quantization.
For coding workloads, Llama 4 Scout 17B 16E on RTX PRO 6000 Blackwell Server Edition 96GB receives a A grade with 51.3 tok/s and 102K context.
On RTX PRO 6000 Blackwell Server Edition 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.
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