Llama 3.1 8B vs Gemma 2 9B
Architecture Comparison
SpecLlama 3.1 8BGemma 2 9B
TypeDENSEDENSE
Total Parameters8.03B9.2B
Active Parameters8.03B9.2B
Layers3242
Hidden Dimension4,0963,584
Attention Heads3216
KV Heads88
Context Length131,0728,192
Precision (default)BF16BF16
Memory Requirements
PrecisionLlama 3.1 8BGemma 2 9B
BF16 Weights16.1 GB18.4 GB
FP8 Weights8.0 GB9.2 GB
INT4 Weights4.0 GB4.6 GB
KV-Cache / Token131072 B344064 B
Activation Estimate1.00 GB1.00 GB
Minimum GPUs Needed (BF16)
H100 SXM1 GPU1 GPU
L40S1 GPU1 GPU
Quality Benchmarks
BenchmarkLlama 3.1 8BGemma 2 9B
Overall6568
MMLU69.471.3
HumanEval40.240.0
GSM8K79.676.0
MT-Bench78.078.0
Llama 3.1 8B
MMLU
69.4
HumanEval
40.2
GSM8K
79.6
MT-Bench
78.0
Gemma 2 9B
MMLU
71.3
HumanEval
40.0
GSM8K
76.0
MT-Bench
78.0
Capabilities
FeatureLlama 3.1 8BGemma 2 9B
Tool Use✓ Yes✗ No
Vision✗ No✗ No
Code✓ Yes✓ Yes
Math✓ Yes✓ Yes
Reasoning✗ No✗ No
Multilingual✓ Yes✓ Yes
Structured Output✓ Yes✓ Yes
API Pricing Comparison
Cheapest Output (Llama 3.1 8B)
$0.08/M
Input: $0.05/M
Cheapest Output (Gemma 2 9B)
$0.10/M
Input: $0.10/M
| Provider | Llama 3.1 8B In $/M | Out $/M | Gemma 2 9B In $/M | Out $/M |
|---|---|---|---|---|
| groq | $0.05 | $0.08 | — | — |
| deepinfra | — | — | $0.10 | $0.10 |
| together | $0.18 | $0.18 | $0.20 | $0.20 |
| fireworks | $0.20 | $0.20 | — | — |
Recommendation Summary
- ‣Gemma 2 9B scores higher on overall quality (68 vs 65).
- ‣Llama 3.1 8B is cheaper per output token ($0.08/M vs $0.10/M).
- ‣Llama 3.1 8B has a smaller memory footprint (16.1 GB vs 18.4 GB BF16), making it easier to deploy on fewer GPUs.
- ‣Llama 3.1 8B supports a longer context window (131,072 vs 8,192 tokens).
- ‣Llama 3.1 8B is stronger at code generation (HumanEval: 40.2 vs 40.0).
- ‣Llama 3.1 8B is better at math reasoning (GSM8K: 79.6 vs 76.0).