Gemma 2 9B
Google · dense · 9.2B parameters · 8,192 context
Parameters
9.2B
Context Window
8K tokens
Architecture
Dense
Best GPU
RTX 5090
Cheapest API
$0.10/M
Quality Score
68/100
Intelligence Brief
Gemma 2 9B is a 9.2B parameter DENSE model from Google, featuring Grouped Query Attention (GQA) with 42 layers and 3,584 hidden dimensions. With a 8,192 token context window, it supports structured output, code, math, multilingual. On standardized benchmarks, it achieves MMLU 71.3, HumanEval 40, GSM8K 76. The most cost-effective API deployment is via deepinfra at $0.10/M output tokens. For self-hosted inference, RTX 5090 delivers optimal throughput at $845/month.
Architecture Details
Memory Requirements
BF16 Weights
18.4 GB
FP8 Weights
9.2 GB
INT4 Weights
4.6 GB
GPU Compatibility Matrix
Gemma 2 9B is compatible with 90% of GPU configurations across 41 GPUs at 3 precision levels.
GPU Recommendations
BF16 · 1 GPU · vllm
95/100
score
Throughput
473.3 tok/s
Latency (ITL)
2.1ms
Est. TTFT
0ms
Cost/Month
$845
Cost/M Tokens
$0.68
BF16 · 1 GPU · vllm
95/100
score
Throughput
91.7 tok/s
Latency (ITL)
10.9ms
Est. TTFT
2ms
Cost/Month
$180
Cost/M Tokens
$0.75
BF16 · 1 GPU · vllm
95/100
score
Throughput
110.8 tok/s
Latency (ITL)
9.0ms
Est. TTFT
2ms
Cost/Month
$380
Cost/M Tokens
$1.30
Deployment Options
API Deployment
deepinfra
$0.10/M
output tokens
Single GPU
RTX 5090
$845/mo
Min VRAM: 9 GB
Multi-GPU
A4000 x2
188.2 tok/s
TP· $323/mo
API Pricing Comparison
| Provider | Input $/M | Output $/M | Badges |
|---|---|---|---|
| deepinfra | $0.10 | $0.10 | Cheapest |
| together | $0.20 | $0.20 |
Cost Analysis
| Provider | Input $/M | Output $/M | ~Monthly Cost |
|---|---|---|---|
| deepinfraBest Value | $0.10 | $0.10 | $1 |
| together | $0.20 | $0.20 | $2 |
Cost per 1,000 Requests
Short (500 tok)
$0.07
via deepinfra
Medium (2K tok)
$0.28
via deepinfra
Long (8K tok)
$1.00
via deepinfra
Performance Estimates
Throughput by GPU
VRAM Breakdown (RTX 5090, BF16)
Precision Impact
bf16
18.4 GB
weights/GPU
~473.3 tok/s
fp8
9.2 GB
weights/GPU
int4
4.6 GB
weights/GPU
Quality Benchmarks
Capabilities
Features
Supported Frameworks
Supported Precisions
Where to Deploy Gemma 2 9B
Self-Hosted Infrastructure
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Frequently Asked Questions
How much VRAM does Gemma 2 9B need for inference?
Gemma 2 9B requires approximately 18.4 GB of VRAM at BF16 precision, 9.2 GB at FP8, or 4.6 GB at INT4 quantization. Additional VRAM is needed for KV-cache (344064 bytes per token) and activations (~1.00 GB).
What is the best GPU for Gemma 2 9B?
The top recommended GPU for Gemma 2 9B is the RTX 5090 using BF16 precision. It achieves approximately 473.3 tokens/sec at an estimated cost of $845/month ($0.68/M tokens). Score: 95/100.
How much does Gemma 2 9B inference cost?
Gemma 2 9B API inference starts from $0.10/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.