Llama 4 Scout 17B 16E needs ~76.7 GB VRAM. AMD Instinct MI210 64GB has 64.0 GB. With Q4_K_M quantization, expect ~22 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
21.8 tok/s
TTFT
8876 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) | 22.7 tok/s | 4649 ms | 4K |
| Coding | B | Very compromised (needs ~11 GB host RAM) | 21.8 tok/s | 8876 ms | 4K |
| Agentic Coding | F | Too heavy | 20.2 tok/s | 13971 ms | 4K |
| Reasoning | B | Very compromised (needs ~11 GB host RAM) | 21.8 tok/s | 10490 ms | 4K |
| RAG | F | Too heavy | 20.2 tok/s | 17463 ms | 4K |
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 AMD Instinct MI210 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 startUpgrade options
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 924%.
~$8,000 MSRP
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 551%.
~$12,000 MSRP
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 338%.
~$15,000 MSRP
Yes, AMD Instinct MI210 64GB can run Llama 4 Scout 17B 16E with a B grade (Very compromised (needs ~11 GB host RAM)). Expected decode speed: 21.8 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 AMD Instinct MI210 64GB, Llama 4 Scout 17B 16E achieves approximately 21.8 tokens per second decode speed with a time-to-first-token of 8876ms using Q4_K_M quantization.
For coding workloads, Llama 4 Scout 17B 16E on AMD Instinct MI210 64GB receives a B grade with 21.8 tok/s and 4K context.
On AMD Instinct MI210 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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