Llama 3.1 8B
An established instruction-tuned baseline for a practical deployment comparison.
- Published size
- ~8B
- Active parameters
- ~8B
- Context window
- 128K
- Architecture
- Dense
- License
- Llama 3.1
Deployment considerations
Start with the workload you actually serve. A 128K model context does not mean a small GPU can serve 128K at your target concurrency.
This profile covers the instruction-tuned 8B release, using a rounded parameter count for the memory illustration.
Weights are distributed under the Llama 3.1 Community License. Review the upstream access and usage requirements.
The advertised context limit is a model capability. KV cache, concurrency, and runtime overhead determine the memory needed to use it.
Give your model
some breathing room.
This is not a fit guarantee. KV cache, activations, quantization metadata, and the runtime still need memory.
Decimal GB; nominal parameter counts where marked ~. Bit widths illustrate weight storage, not validated quantizations. Understand the estimate
No invented leaderboards.
Latency, throughput, and cost per token will appear here after a reproducible run. Until then, this page helps you understand the model—not predict its performance.
Llama 3.1 8B deployment FAQ
How much GPU memory does Llama 3.1 8B need?
At 16-bit precision, the estimated weight storage is 16 GB. At 8-bit it is 8 GB, and at 4-bit it is 4 GB. These are theoretical weight-only estimates, excluding KV cache, runtime allocations, and quantization metadata. A working deployment needs additional memory and a supported checkpoint.
Has BenchGrid benchmarked Llama 3.1 8B?
Not yet. This profile contains publisher specifications and calculated weight-memory estimates. We do not currently publish measured latency, throughput, or cost per token for this model.
Where do these specifications come from?
The specifications are based on the official Meta model card linked on this page. Memory estimates use the stated total parameter count, including inactive experts for MoE models. Nominal model sizes are labeled with ~.
Explore your compute options.
Check available hardware, quotas, and current pricing with the provider. These links are not verified deployments or performance recommendations.
