Gemma 3 4B
Google · dense · 4.3B parameters · 131,072 context
Parameters
4.3B
Context Window
128K tokens
Architecture
Dense
Best GPU
A4000
Cheapest API
$0.10/M
Quality Score
54/100
Intelligence Brief
Gemma 3 4B is a 4.3B parameter DENSE model from Google, featuring Grouped Query Attention (GQA) with 34 layers and 2,560 hidden dimensions. With a 131,072 token context window, it supports tools, vision, structured output, code, math, multilingual. On standardized benchmarks, it achieves MMLU 60, HumanEval 32, GSM8K 58. The most cost-effective API deployment is via google at $0.10/M output tokens. For self-hosted inference, A4000 delivers optimal throughput at $161/month.
Architecture Details
Memory Requirements
BF16 Weights
8.6 GB
FP8 Weights
4.3 GB
INT4 Weights
2.1 GB
GPU Compatibility Matrix
Gemma 3 4B is compatible with 98% of GPU configurations across 41 GPUs at 3 precision levels.
GPU Recommendations
BF16 · 1 GPU · vllm
100/100
score
Throughput
281.3 tok/s
Latency (ITL)
3.6ms
Est. TTFT
1ms
Cost/Month
$161
Cost/M Tokens
$0.22
BF16 · 1 GPU · vllm
100/100
score
Throughput
450.2 tok/s
Latency (ITL)
2.2ms
Est. TTFT
0ms
Cost/Month
$304
Cost/M Tokens
$0.26
BF16 · 1 GPU · vllm
100/100
score
Throughput
316.4 tok/s
Latency (ITL)
3.2ms
Est. TTFT
1ms
Cost/Month
$237
Cost/M Tokens
$0.29
Deployment Options
API Deployment
$0.10/M
output tokens
Single GPU
A4000
$161/mo
Min VRAM: 4 GB
Multi-GPU
RTX 3070 x2
408.6 tok/s
TP· $171/mo
API Pricing Comparison
| Provider | Input $/M | Output $/M | Badges |
|---|---|---|---|
| $0.05 | $0.10 | Cheapest |
Cost Analysis
| Provider | Input $/M | Output $/M | ~Monthly Cost |
|---|---|---|---|
| googleBest Value | $0.05 | $0.10 | $1 |
Cost per 1,000 Requests
Short (500 tok)
$0.05
via google
Medium (2K tok)
$0.18
via google
Long (8K tok)
$0.60
via google
Performance Estimates
Throughput by GPU
VRAM Breakdown (A4000, BF16)
Precision Impact
bf16
8.6 GB
weights/GPU
~281.3 tok/s
fp8
4.3 GB
weights/GPU
int4
2.1 GB
weights/GPU
Quality Benchmarks
Capabilities
Features
Supported Frameworks
Supported Precisions
Where to Deploy Gemma 3 4B
Self-Hosted Infrastructure
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Frequently Asked Questions
How much VRAM does Gemma 3 4B need for inference?
Gemma 3 4B requires approximately 8.6 GB of VRAM at BF16 precision, 4.3 GB at FP8, or 2.1 GB at INT4 quantization. Additional VRAM is needed for KV-cache (139264 bytes per token) and activations (~0.50 GB).
What is the best GPU for Gemma 3 4B?
The top recommended GPU for Gemma 3 4B is the A4000 using BF16 precision. It achieves approximately 281.3 tokens/sec at an estimated cost of $161/month ($0.22/M tokens). Score: 100/100.
How much does Gemma 3 4B inference cost?
Gemma 3 4B API inference starts from $0.05/M input tokens and $0.10/M output tokens. Self-hosted inference costs depend on your GPU configuration — use our ROI calculator for a detailed breakdown.