Compare
DeepSeek-R1-Distill-Qwen-14B vs Llama 3.1 8B Instruct
Same hardware, same arithmetic, side by side. Every memory figure is calculated from published model geometry rather than quoted from a marketing page.
Estimated
Side by side
On GeForce RTX 4090
| Property | DeepSeek-R1-Distill-Qwen-14B | Llama 3.1 8B Instruct |
|---|---|---|
| Organization | DeepSeek | Meta |
| Parameters | 14.8B | 8B |
| Architecture | Qwen2.5 · dense | Llama · dense |
| Context | 128k | 128k |
| Layers | 48 | 32 |
| Hidden size | 5,120 | 4,096 |
| KV heads | 8 of 40 | 8 of 32 |
| Licence | MIT | Llama 3.1 Community License |
| Commercial use | Yes | Yes |
| Modalities | text | text |
| Released | 2025-01-20 | 2024-07-23 |
| Recommended quantization | Q6_K | Q8_0 |
| Memory needed | 14 GB | 9.5 GB |
| Estimated tok/s | 64 | 92 |
| Fit | Excellent fit | Excellent fit |
| Fine-tune here | Yes | Yes |
| MMLU (reported) | — | 69.4% |
| HUMANEVAL (reported) | — | 72.6% |
Benchmark rows are figures the model's authors published, not ModelLM measurements, and the two models may not have been evaluated under identical conditions.
Trade-offs
DeepSeek-R1-Distill-Qwen-14B
Qwen2.5 14B distilled on R1 reasoning traces. Writes out its thinking before answering, which costs tokens but wins on hard problems.
Strengths
- Outstanding maths and logic for 14B
- MIT licensed
- Runs on a 12 GB card at Q4_K_M
Limitations
- Long chains of thought inflate latency
- Poor at short conversational replies
Llama 3.1 8B Instruct
The most widely supported open model there is. If a tool, adapter or tutorial exists, it was written for this one first.
Strengths
- Unmatched ecosystem support
- 128k context
- Very stable fine-tuning behaviour
Limitations
- Benchmarks now behind newer 7–9B models
- Community licence with an acceptable-use policy
Common comparisons