DeepSeek-Coder-V2-Lite-Instruct vs Qwen2.5-14B-Instruct
DeepSeek-Coder-V2-Lite-Instruct (15.7B parameters) and Qwen2.5-14B-Instruct (14.8B parameters) side by side: the memory each needs at every precision, what it costs to run on a live GPU, and the context window, KV cache and license where they are published. Numbers are computed from the models' published specs; this page does not rank quality.
Side by side
| Fact | DeepSeek-Coder-V2-Lite-Instruct | Qwen2.5-14B-Instruct |
|---|---|---|
| Parameters | 15.7B | 14.8B |
| Architecture | Multi-head latent attention; mixture of 64 experts, 6 active per token | Grouped-query attention |
| Context length | 163,840 tokens | 32K tokens (32,768) |
| License | – | Apache 2.0 |
| Published precision | BF16 | BF16 |
| VRAM needed, As published | 35.1 GB | 33.0 GB |
| VRAM needed, FP8 | 17.6 GB | 16.5 GB |
| VRAM needed, INT4 | 8.8 GB | 8.3 GB |
| Cheapest live fit, As published | RTX A6000 · $0.363/hr | RTX A6000 · $0.363/hr |
| Cheapest live fit, FP8 | RTX 4000 SFF Ada · $0.198/hr | RTX 4000 SFF Ada · $0.198/hr |
| Cheapest live fit, INT4 | RTX 4070 Super · $0.121/hr | RTX 4070 Super · $0.121/hr |
| KV cache per token (16-bit) | 30 KB | 192 KB |
| KV cache at 32k tokens | 0.95 GB | 6.00 GB |
| KV cache at 128k tokens | 3.80 GB | 24.0 GB |
VRAM is the weight size at each precision times a flat 1.2 overhead; see the methodology. The FP8 and INT4 rows need a quantized checkpoint or an engine that quantizes on load. The fit is the lowest-priced single GPU type that holds the model at that precision, or the lowest-priced multi-GPU set (up to 8) when none does. KV cache is for one sequence at 16-bit, computed from each model's config where the attention layout is known.
Which to pick
- Qwen2.5-14B-Instruct needs less VRAM at its published precision (33.0 GB against 35.1 GB), so it fits on a smaller GPU.
- DeepSeek-Coder-V2-Lite-Instruct lists the longer context window (163,840 tokens against 32,768).
- DeepSeek-Coder-V2-Lite-Instruct caches less per sequence at 32k tokens (0.9 GB against 6.0 GB), leaving more memory for batching.
These follow only from the facts in the table above. Whether either model does your task well is a separate question this page does not answer.
Keep reading
- DeepSeek-Coder-V2-Lite-Instruct: full VRAM table and live GPU fit
- Qwen2.5-14B-Instruct: full VRAM table and live GPU fit
- The DeepSeek model series
- The Qwen model series
- All models that fit in 48 GB
Other comparisons
- DeepSeek-Coder-V2-Lite-Instruct vs Qwen2.5-Coder-14B-Instruct
- DeepSeek-Coder-V2-Lite-Instruct vs Qwen3-14B
- DeepSeek-Coder-V2-Lite-Instruct vs Qwen3-14B-Base
- DeepSeek-Coder-V2-Lite-Instruct vs phi-4
- Qwen2.5-14B-Instruct vs phi-4
- Qwen2.5-14B-Instruct vs DeepSeek-V2-Lite-Chat
- Qwen2.5-14B-Instruct vs DeepSeek-V2-Lite
