Updated minutes ago
Llama 4 Scout
Meta · moe · 109B parameters · 10,485,760 context
Quality76.0
Architecture Details
TypeMOE
Total Parameters109B
Active Parameters17B
Layers48
Hidden Dimension5,120
Attention Heads40
KV Heads8
Head Dimension128
Vocab Size202,048
Total Experts16
Active Experts1
Memory Requirements
BF16 Weights
218.0 GB
FP8 Weights
109.0 GB
INT4 Weights
54.5 GB
KV-Cache per Token196608 bytes
Activation Estimate2.00 GB
Fits on (single-node)
B200 SXM FP8B100 SXM FP8GB200 NVL72 (per GPU) FP8GB300 NVL72 (per GPU) FP8H200 SXM FP8H100 SXM INT4H100 PCIe INT4H100 NVL INT4
GPU Recommendations
B200 SXMoptimal
FP8 · 1 GPU · tensorrt-llm
100/100
score
Throughput
280.0 tok/s
Cost/Month
$4261
Cost/M Tokens
$5.79
B100 SXMoptimal
FP8 · 1 GPU · tensorrt-llm
100/100
score
Throughput
280.0 tok/s
Cost/Month
$4271
Cost/M Tokens
$5.80
GB200 NVL72 (per GPU)optimal
FP8 · 1 GPU · tensorrt-llm
100/100
score
Throughput
280.0 tok/s
Cost/Month
$6169
Cost/M Tokens
$8.38
API Pricing Comparison
| Provider | Input $/M | Output $/M | Badges |
|---|---|---|---|
| together | $0.18 | $0.30 | Cheapest |
| fireworks | $0.20 | $0.35 |
Quality Benchmarks
MMLU79.0
HumanEval55.0
GSM8K85.0
MT-Bench81.0
Capabilities
Features
✓ Tool Use✓ Vision✓ Code✓ Math✗ Reasoning✓ Multilingual✓ Structured Output
Supported Frameworks
vllmsglangtensorrt-llm
Supported Precisions
BF16 (default)FP8INT4