Llama 3.1 8B vs Llama 3.1 70B
Architecture Comparison
SpecLlama 3.1 8BLlama 3.1 70B
TypeDENSEDENSE
Total Parameters8.03B70.6B
Active Parameters8.03B70.6B
Layers3280
Hidden Dimension4,0968,192
Attention Heads3264
KV Heads88
Context Length131,072131,072
Precision (default)BF16BF16
Memory Requirements
PrecisionLlama 3.1 8BLlama 3.1 70B
BF16 Weights16.1 GB141.2 GB
FP8 Weights8.0 GB70.6 GB
INT4 Weights4.0 GB35.3 GB
KV-Cache / Token131072 B327680 B
Activation Estimate1.00 GB2.50 GB
Minimum GPUs Needed (BF16)
H100 SXM1 GPU3 GPUs
L40S1 GPU4 GPUs
Quality Benchmarks
BenchmarkLlama 3.1 8BLlama 3.1 70B
Overall6582
MMLU69.483.6
HumanEval40.258.5
GSM8K79.693.0
MT-Bench78.085.0
Llama 3.1 8B
MMLU
69.4
HumanEval
40.2
GSM8K
79.6
MT-Bench
78.0
Llama 3.1 70B
MMLU
83.6
HumanEval
58.5
GSM8K
93.0
MT-Bench
85.0
Capabilities
FeatureLlama 3.1 8BLlama 3.1 70B
Tool Use✓ Yes✓ Yes
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 (Llama 3.1 70B)
$0.79/M
Input: $0.59/M
| Provider | Llama 3.1 8B In $/M | Out $/M | Llama 3.1 70B In $/M | Out $/M |
|---|---|---|---|---|
| groq | $0.05 | $0.08 | $0.59 | $0.79 |
| together | $0.18 | $0.18 | $0.88 | $0.88 |
| fireworks | $0.20 | $0.20 | $0.90 | $0.90 |
Recommendation Summary
- ‣Llama 3.1 70B scores higher on overall quality (82 vs 65).
- ‣Llama 3.1 8B is cheaper per output token ($0.08/M vs $0.79/M).
- ‣Llama 3.1 8B has a smaller memory footprint (16.1 GB vs 141.2 GB BF16), making it easier to deploy on fewer GPUs.
- ‣Llama 3.1 70B is stronger at code generation (HumanEval: 58.5 vs 40.2).
- ‣Llama 3.1 70B is better at math reasoning (GSM8K: 93.0 vs 79.6).