Updated minutes ago
Gemma 3 27B
Google · dense · 27B parameters · 131,072 context
Quality76.0
Architecture Details
TypeDENSE
Total Parameters27B
Active Parameters27B
Layers62
Hidden Dimension3,584
Attention Heads32
KV Heads16
Head Dimension128
Vocab Size262,144
Memory Requirements
BF16 Weights
54.0 GB
FP8 Weights
27.0 GB
INT4 Weights
13.5 GB
KV-Cache per Token507904 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
H20optimal
FP8 · 1 GPU · tensorrt-llm
100/100
score
Throughput
1.1K tok/s
Cost/Month
$940
Cost/M Tokens
$0.34
H200 SXMoptimal
FP8 · 1 GPU · tensorrt-llm
95/100
score
Throughput
1.1K tok/s
Cost/Month
$2553
Cost/M Tokens
$0.93
H100 SXMoptimal
FP8 · 1 GPU · tensorrt-llm
95/100
score
Throughput
1.0K tok/s
Cost/Month
$1794
Cost/M Tokens
$0.66
API Pricing Comparison
| Provider | Input $/M | Output $/M | Badges |
|---|---|---|---|
| $0.10 | $0.20 | Cheapest | |
| together | $0.30 | $0.30 |
Quality Benchmarks
MMLU78.0
HumanEval48.0
GSM8K85.0
MT-Bench82.0
Capabilities
Features
✓ Tool Use✓ Vision✓ Code✓ Math✗ Reasoning✓ Multilingual✓ Structured Output
Supported Frameworks
vllmsglangtgitensorrt-llmollama
Supported Precisions
BF16 (default)FP8INT4