Llama 4 Behemoth
Meta · moe · 2000B parameters · 1,048,576 context
Parameters
2.0T
Context Window
1024K tokens
Architecture
MoE
Best GPU
B200 SXM
Cheapest API
$16.00/M
Quality Score
93/100
Intelligence Brief
Llama 4 Behemoth is a 2000B parameter Mixture-of-Experts (256 experts, 16 active) model from Meta, featuring Grouped Query Attention (GQA) with 128 layers and 16,384 hidden dimensions. With a 1,048,576 token context window, it supports tools, vision, structured output, code, math, multilingual, reasoning. On standardized benchmarks, it achieves MMLU 92, HumanEval 74, GSM8K 97. The most cost-effective API deployment is via together at $16.00/M output tokens. For self-hosted inference, B200 SXM delivers optimal throughput at $136352/month.
Architecture Details
Memory Requirements
BF16 Weights
4000.0 GB
FP8 Weights
2000.0 GB
INT4 Weights
1000.0 GB
Fits on (multi-GPU with Tensor Parallelism)
Multi-GPU configurations use Tensor Parallelism (TP) to split model layers across GPUs. Requires NVLink or NVSwitch interconnect for optimal performance.
This model requires multi-GPU deployment. Minimum: 4x B200 NVL (pair) (360GB each) with Tensor Parallelism.
GPU Compatibility Matrix
Llama 4 Behemoth is compatible with 0% of GPU configurations across 41 GPUs at 3 precision levels.
GPU Recommendations
BF16 · 32 GPUs · tensorrt-llm
63/100
score
Throughput
140.0 tok/s
Latency (ITL)
7.1ms
Est. TTFT
1ms
Cost/Month
$136352
Cost/M Tokens
$370.60
BF16 · 32 GPUs · tensorrt-llm
63/100
score
Throughput
140.0 tok/s
Latency (ITL)
7.1ms
Est. TTFT
1ms
Cost/Month
$136656
Cost/M Tokens
$371.43
BF16 · 32 GPUs · tensorrt-llm
63/100
score
Throughput
140.0 tok/s
Latency (ITL)
7.1ms
Est. TTFT
1ms
Cost/Month
$197392
Cost/M Tokens
$536.51
Deployment Options
API Deployment
together
$16.00/M
output tokens
Single GPU
Requires multi-GPU setup (2000 GB VRAM needed)
Multi-GPU
B200 SXM x32
140.0 tok/s
TP· $136352/mo
API Pricing Comparison
| Provider | Input $/M | Output $/M | Badges |
|---|---|---|---|
| together | $5.00 | $16.00 | Cheapest |
Cost Analysis
| Provider | Input $/M | Output $/M | ~Monthly Cost |
|---|---|---|---|
| togetherBest Value | $5.00 | $16.00 | $105 |
Cost per 1,000 Requests
Short (500 tok)
$5.70
via together
Medium (2K tok)
$22.80
via together
Long (8K tok)
$72.00
via together
Performance Estimates
Throughput by GPU
VRAM Breakdown (B200 SXM, BF16)
Quality Benchmarks
Capabilities
Features
Supported Frameworks
Supported Precisions
Where to Deploy Llama 4 Behemoth
Self-Hosted Infrastructure
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Frequently Asked Questions
How much VRAM does Llama 4 Behemoth need for inference?
Llama 4 Behemoth requires approximately 4000.0 GB of VRAM at BF16 precision, 2000.0 GB at FP8, or 1000.0 GB at INT4 quantization. Additional VRAM is needed for KV-cache (4194304 bytes per token) and activations (~25.00 GB).
What is the best GPU for Llama 4 Behemoth?
The top recommended GPU for Llama 4 Behemoth is the B200 SXM (x32) using BF16 precision. It achieves approximately 140.0 tokens/sec at an estimated cost of $136352/month ($370.60/M tokens). Score: 63/100.
How much does Llama 4 Behemoth inference cost?
Llama 4 Behemoth API inference starts from $5.00/M input tokens and $16.00/M output tokens. Self-hosted inference costs depend on your GPU configuration — use our ROI calculator for a detailed breakdown.