How to Deploy Llama 4 Scout 17B 16E Locally
Llama 4 Scout 17B 16E is a MoE model with 108.6B total parameters and 17B active per token. At Q4 it needs 64GB of VRAM to sit entirely on the GPU; with experts offloaded to system RAM it needs only 10GB of VRAM plus 55GB of RAM. The cheapest card that runs it today is the CMP 170HX 64GB at about $2,089, at roughly 71.5 tokens/s. In offload mode the cheapest card that runs it is the Tesla V100 16GB, at roughly 12 tokens/s.
Llama 4 Scout 17B 16E details
How much VRAM does Llama 4 Scout 17B 16E need for local deployment?
MoE: all 108.6B parameters must stay resident in memory, while speed is set by the 17B active per token.
| Quantization | WeightsGB | Total at 8KGB | Total at 32KGB | Total at 128KGB |
|---|---|---|---|---|
| 1-bit | 35GGUF | 28 | 32.5 | 50.5 |
| 2-bit | 42.4GGUF | 42.9 | 47.4 | 65.4 |
| 3-bit | 49GGUF | 54.6 | 59.1 | 77.1 |
| 4-bit | 65.4GGUF | 63.1 | 67.6 | 85.6 |
| 5-bit | 76.5GGUF | 75.8 | 80.3 | 98.3 |
| 8-bit | 114.5GGUF | 115.1 | 119.6 | 137.6 |
| FP16 | 215.6GGUF | 215 | 219.5 | 237.5 |
Total = weights + KV cache (FP16) + 1GB runtime overhead. Weights marked GGUF are measured file sizes from the repository; the rest are estimated from bytes per parameter. Contexts beyond this model's 10M limit show a dash. Each tier takes whichever file of that bit width the repository has, preferring unsloth dynamic quants, so the exact file name varies by model.
Which GPUs can run Llama 4 Scout 17B 16E?
Xianyu
| Device | VRAMGB | Price | Est. t/s | Suggested context |
|---|---|---|---|---|
 CMP 170HX 64GB | 64 | $2,0898 listings | 71.5 | 8K |
 Instinct MI210 | 64 | $2,75112 listings | 59.7 | 8K |
| Mac Studio M4 Max 128GB | 128 | $4,78813 listings | 25.8 | 128K |
| DGX Spark 128GB | 128 | $4,83231 listings | 17.1 | 128K |
| MacBook Pro M4 Max 128GB | 128 | $5,2644 listings | 25.8 | 128K |
| Mac Studio M5 Max 128GB | 128 | $5,8816 listings | 28.6 | 128K |
| MacBook Pro M5 Max 128GB | 128 | $8,1487 listings | 28.6 | 128K |
| RTX PRO 5000 Blackwell 72GB | 72 | $9,66517 listings | 66.2 | 32K |
| RTX PRO 6000D | 84 | $10,11117 listings | 74 | 32K |
| RTX PRO 6000 Blackwell | 96 | $18,4226 listings | 81.2 | 128K |
| Mac Studio M3 Ultra 512GB | 512 | $19,3156 listings | 36.8 | 128K |
| Ryzen AI Max+ 395 128GB | 128 | —No listings | 11.7 | 128K |
Fits when weights + KV cache + runtime overhead is at or below usable memory, single card. "Offload" on MoE models means it runs with expert weights in system RAM (only attention layers and KV cache stay in VRAM); that speed assumes 70 GB/s RAM bandwidth. The context column is the largest tier that fits.
Recommended GPUs for Llama 4 Scout 17B 16E with CPU/GPU offloading
Xianyu
Expert weights live in system RAM (needs at least 55 GB); VRAM holds only attention layers, shared experts and the KV cache, at least 10 GB at Q4. Speed is bound by RAM bandwidth (estimated at 70 GB/s) and is far slower than a full-VRAM setup.
Deploy Llama 4 Scout 17B 16E locally with Ollama, llama.cpp or vLLM
Ollama pulls and runs it in one command; the quantization comes from the official tag.
ollama run llama4:scout
llama.cpp pulls the GGUF straight from Hugging Face; -c sets the context length.
llama-server -hf unsloth/Llama-4-Scout-17B-16E-Instruct-GGUF:Q4_K_M -c 8192
vLLM serves the original-precision weights, which needs far more memory than GGUF and usually more than one GPU.
vllm serve meta-llama/Llama-4-Scout-17B-16E-Instruct
File names follow that week's model-library snapshot; the definitions are in the methodology. Methodology
FAQ
How much VRAM does Llama 4 Scout 17B 16E need?
At Q4_K_M with an 8K context Llama 4 Scout 17B 16E needs about 64GB of VRAM; 76GB leaves comfortable headroom.
What is the cheapest GPU that runs Llama 4 Scout 17B 16E?
The CMP 170HX 64GB: 64GB of VRAM, currently about $2,089, at roughly 71.5 tokens/s.
Can you run Llama 4 Scout 17B 16E with Ollama?
Yes: ollama run llama4:scout.