Qwen3 8B vs Llama 3.1 8B
These older 8B-class models remain useful baseline profiles for a deployment experiment. They are not presented as the latest releases. Similar parameter counts make the arithmetic easy to compare, while runtime and workload differences still matter.
By BenchGrid editorial · Reviewed · Performance not yet measured
| SIDE BY SIDESame questions. Different models. | AlibabaQwen3 8B | MetaLlama 3.1 8B |
|---|---|---|
| Weight memory estimate16-bit · weights only · decimal GB | 16.4 GB | 16 GB |
| Parameters | 8.2B | ~8B |
| Active parameters | 8.2B | ~8B |
| Context window | 32K native | 128K |
| Architecture | Dense | Dense |
| License | Apache 2.0 | Llama 3.1 |
| BenchGrid performance test | Not yet measured | Not yet measured |
| Deployment perspective | A useful small-model baseline. Start with a short context and measure the additional latency of thinking mode separately. | Start with the workload you actually serve. A 128K model context does not mean a small GPU can serve 128K at your target concurrency. |
| Primary source | Model card | Model card |
Lower weight memory does not mean better quality or faster inference. These calculations exclude serving overhead and do not confirm a working quantized checkpoint. Read the methodology.
An 8B label is not an identical memory footprint
The table uses the parameter counts recorded in each sourced profile. At 16 bits, each billion parameters contributes roughly 2 decimal GB of raw weight storage; at 4 bits it contributes 0.5 GB. These values exclude cache, temporary allocations, and quantization metadata. They are useful for comparing weight-storage scale, not for certifying that a particular GPU will serve the model.
Record the exact context and generation configuration
Read each model card before copying a launch command. Context extensions, chat templates, and reasoning settings can change the experiment. Keep input and output lengths bounded, record sampling settings, and score the completed task. An older baseline can still be informative if its role and configuration are explicit.
Use a baseline to measure progress
Our suggested workflow saves a reproducible run of the baseline, then tests a newer candidate against the same application workload. Keep the baseline result even when the candidate performs better: it gives later changes a reference point. BenchGrid has not run that experiment yet. This page offers specifications and transparent arithmetic, with no claimed performance ranking.