Llama 4 Scout
Meta · moe · 109B parameters · 10,485,760 context
Parameters
109B
Context Window
10240K tokens
Architecture
MoE
Best GPU
B200 SXM
Cheapest API
$0.30/M
Quality Score
73/100
Intelligence Brief
Llama 4 Scout is a 109B parameter Mixture-of-Experts (16 experts, 1 active) model from Meta, featuring Grouped Query Attention (GQA) with 48 layers and 5,120 hidden dimensions. With a 10,485,760 token context window, it supports tools, vision, structured output, code, math, multilingual. On standardized benchmarks, it achieves MMLU 79, HumanEval 55, GSM8K 85. The most cost-effective API deployment is via together at $0.30/M output tokens. For self-hosted inference, B200 SXM delivers optimal throughput at $4261/month.
Provider pricing
5 providers · canonical: together| Provider | Input $/M | Output $/M ▲ | Notes |
|---|---|---|---|
| togethercanonical | $0.180 | $0.300 | cheapest output |
| openrouter | $0.080 | $0.300 | cheapest input · cheapest output |
| groq | $0.110 | $0.340 | — |
| fireworks | $0.200 | $0.350 | — |
| novita | $0.180 | $0.590 | — |
Prices update via the nightly pricing cron + admin approvals at /admin/ingest-queue. The leaderboard's Input/Output cells show the canonical rate above; this table shows the full spread.
Recent changes
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Picks: same family first, then same vendor within ±2× params, then top tag-overlap matches. Price shown is the cheapest Output $/M across providers — the row's page shows the canonical anchor.
Architecture Details
Memory Requirements
BF16 Weights
218.0 GB
FP8 Weights
109.0 GB
INT4 Weights
54.5 GB
GPU Compatibility Matrix
Llama 4 Scout is compatible with 21% of GPU configurations across 41 GPUs at 3 precision levels.
GPU Recommendations
FP8 · 1 GPU · tensorrt-llm
100/100
score
Throughput
280.0 tok/s
Latency (ITL)
3.6ms
Est. TTFT
1ms
Cost/Month
$4261
Cost/M Tokens
$5.79
FP8 · 1 GPU · tensorrt-llm
100/100
score
Throughput
280.0 tok/s
Latency (ITL)
3.6ms
Est. TTFT
1ms
Cost/Month
$4271
Cost/M Tokens
$5.80
FP8 · 1 GPU · tensorrt-llm
100/100
score
Throughput
280.0 tok/s
Latency (ITL)
3.6ms
Est. TTFT
1ms
Cost/Month
$6169
Cost/M Tokens
$8.38
Deployment Options
API Deployment
together
$0.30/M
output tokens
Single GPU
B200 SXM
$4261/mo
Min VRAM: 109 GB
Multi-GPU
H100 SXM x2
280.0 tok/s
TP· $3587/mo
API Pricing Comparison
| Provider | Input $/M | Output $/M | Badges |
|---|---|---|---|
| together | $0.18 | $0.30 | Cheapest |
| openrouter | $0.08 | $0.30 | Low Input |
| groq | $0.11 | $0.34 | |
| fireworks | $0.20 | $0.35 | |
| novita | $0.18 | $0.59 |
Cost Analysis
| Provider | Input $/M | Output $/M | ~Monthly Cost |
|---|---|---|---|
| togetherBest Value | $0.18 | $0.30 | $2 |
| openrouter | $0.08 | $0.30 | $2 |
| groq | $0.11 | $0.34 | $2 |
| fireworks | $0.20 | $0.35 | $3 |
| novita | $0.18 | $0.59 | $4 |
Cost per 1,000 Requests
Short (500 tok)
$0.15
via together
Medium (2K tok)
$0.60
via together
Long (8K tok)
$2.04
via together
Performance Estimates
Throughput by GPU
VRAM Breakdown (B200 SXM, FP8)
Precision Impact
bf16
218.0 GB
weights/GPU
fp8
109.0 GB
weights/GPU
~280.0 tok/s
int4
54.5 GB
weights/GPU
Quality Benchmarks
Capabilities
Features
Supported Frameworks
Supported Precisions
Where to Deploy Llama 4 Scout
Self-Hosted Infrastructure
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Frequently Asked Questions
How much VRAM does Llama 4 Scout need for inference?
Llama 4 Scout requires approximately 218.0 GB of VRAM at BF16 precision, 109.0 GB at FP8, or 54.5 GB at INT4 quantization. Additional VRAM is needed for KV-cache (196608 bytes per token) and activations (~2.00 GB).
What is the best GPU for Llama 4 Scout?
The top recommended GPU for Llama 4 Scout is the B200 SXM using FP8 precision. It achieves approximately 280.0 tokens/sec at an estimated cost of $4261/month ($5.79/M tokens). Score: 100/100.
How much does Llama 4 Scout inference cost?
Llama 4 Scout API inference starts from $0.18/M input tokens and $0.30/M output tokens. Self-hosted inference costs depend on your GPU configuration — use our ROI calculator for a detailed breakdown.