Updated minutes ago
InternLM 2.5 20B
Shanghai AI Lab · dense · 19.9B parameters · 262,144 context
Quality50.0
Architecture Details
TypeDENSE
Total Parameters19.9B
Active Parameters19.9B
Layers48
Hidden Dimension6,144
Attention Heads48
KV Heads8
Head Dimension128
Vocab Size92,544
Memory Requirements
BF16 Weights
39.8 GB
FP8 Weights
19.9 GB
INT4 Weights
9.9 GB
KV-Cache per Token196608 bytes
Activation Estimate1.50 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
H100 SXMoptimal
FP8 · 1 GPU · tensorrt-llm
100/100
score
Throughput
1.1K tok/s
Cost/Month
$1794
Cost/M Tokens
$0.65
H20optimal
FP8 · 1 GPU · tensorrt-llm
100/100
score
Throughput
1.1K tok/s
Cost/Month
$940
Cost/M Tokens
$0.34
H100 PCIeoptimal
FP8 · 1 GPU · tensorrt-llm
95/100
score
Throughput
840.8 tok/s
Cost/Month
$1794
Cost/M Tokens
$0.81
API Pricing Comparison
| Provider | Input $/M | Output $/M | Badges |
|---|---|---|---|
| internlm | $0.50 | $0.50 | Cheapest |
Capabilities
Features
✓ Tool Use✗ Vision✓ Code✓ Math✓ Reasoning✓ Multilingual✓ Structured Output
Supported Frameworks
vllmsglangtgi
Supported Precisions
BF16 (default)FP8INT4