Updated minutes ago
BGE M3
BAAI · dense · 0.568B parameters · 8,192 context
Quality50.0
Architecture Details
TypeDENSE
Total Parameters0.568B
Active Parameters0.568B
Layers24
Hidden Dimension1,024
Attention Heads16
KV Heads16
Head Dimension64
Vocab Size250,002
Memory Requirements
BF16 Weights
1.1 GB
FP8 Weights
0.6 GB
INT4 Weights
0.3 GB
KV-Cache per Token98304 bytes
Activation Estimate0.10 GB
Fits on (single-node)
B200 SXM BF16B100 SXM BF16GB200 NVL72 (per GPU) BF16GB300 NVL72 (per GPU) BF16H200 SXM BF16H100 SXM BF16H100 PCIe BF16H100 NVL BF16
GPU Recommendations
B200 SXMoptimal
FP8 · 1 GPU · tensorrt-llm
83/100
score
Throughput
3.5K tok/s
Cost/Month
$4261
Cost/M Tokens
$0.46
B100 SXMoptimal
FP8 · 1 GPU · tensorrt-llm
83/100
score
Throughput
3.5K tok/s
Cost/Month
$4271
Cost/M Tokens
$0.46
GB200 NVL72 (per GPU)optimal
FP8 · 1 GPU · tensorrt-llm
83/100
score
Throughput
3.5K tok/s
Cost/Month
$6169
Cost/M Tokens
$0.67
API Pricing Comparison
| Provider | Input $/M | Output $/M | Badges |
|---|---|---|---|
| together | $0.01 | $0.01 | Cheapest |
Capabilities
Features
✗ Tool Use✗ Vision✗ Code✗ Math✗ Reasoning✓ Multilingual✗ Structured Output
Supported Frameworks
vllmtgitensorrt-llm
Supported Precisions
BF16 (default)FP8