Updated minutes ago
Gemma 3 4B
Google · dense · 4.3B parameters · 131,072 context
Quality54.0
Architecture Details
TypeDENSE
Total Parameters4.3B
Active Parameters4.3B
Layers34
Hidden Dimension2,560
Attention Heads32
KV Heads8
Head Dimension128
Vocab Size262,144
Memory Requirements
BF16 Weights
8.6 GB
FP8 Weights
4.3 GB
INT4 Weights
2.1 GB
KV-Cache per Token139264 bytes
Activation Estimate0.50 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
A4000optimal
BF16 · 1 GPU · vllm
100/100
score
Throughput
281.3 tok/s
Cost/Month
$161
Cost/M Tokens
$0.22
RTX 4080optimal
BF16 · 1 GPU · vllm
100/100
score
Throughput
450.2 tok/s
Cost/Month
$304
Cost/M Tokens
$0.26
RTX 4070 Tioptimal
BF16 · 1 GPU · vllm
100/100
score
Throughput
316.4 tok/s
Cost/Month
$237
Cost/M Tokens
$0.29
API Pricing Comparison
| Provider | Input $/M | Output $/M | Badges |
|---|---|---|---|
| $0.05 | $0.10 | Cheapest |
Quality Benchmarks
MMLU60.0
HumanEval32.0
GSM8K58.0
MT-Bench72.0
Capabilities
Features
✓ Tool Use✓ Vision✓ Code✓ Math✗ Reasoning✓ Multilingual✓ Structured Output
Supported Frameworks
vllmsglangtgitensorrt-llmollama
Supported Precisions
BF16 (default)FP8INT4