DeepSeek-Coder-V2-Lite-Instruct
We present DeepSeek-Coder-V2, an open-source Mixture-of-Experts (MoE) code language model that achieves performance comparable to GPT4-Turbo in code-specific tasks. Specifically, DeepSeek-Coder-V2 is further pre-trained from an intermediate checkpoint of DeepSeek-V2 with additional 6 trillion tokens. Through this continued pre-training, DeepSeek-Coder-V2 substantially enhances the coding and mathematical reasoning ca...
Params
15.7 B
Context
163,840
Downloads 30d
557 K
Likes
628
Download history
daily snapshots · 10 days557 K534 K
Jul 28Jul 31Aug 3Aug 6
Can you run it?
Estimated VRAM at 8K context unless noted. Pick your hardware to see the verdict per quantization.
| File | Quant | Size | Est. VRAM | Verdict on RTX 4090 · 24 GB |
|---|---|---|---|---|
| model.safetensors | bf16 | 31.4 GB | 37.4 GB | ❌ Won’t fit |
| model.safetensors (bf16, full) | bf16 + 160K ctx | 31.4 GB | 82.2 GB | ❌ Won’t fit |
Estimate: file size × 1.1 + KV cache at 8K + 0.5 GB overhead. Not a benchmark — how we calculate this.
Run it
copy-paste, exact tags checked against the Hub$ curl -s https://aimodelscomparison.com/api/v1/models/deepseek-coder-v2-lite-instruct
{
"hf_id": "deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct",
"params_b": 15.71,
"context_length": 163840,
"license": { "id": "other", "commercial": "unknown" },
"downloads_30d": 556971,
"vram_estimates": [
{ "quant": "bf16", "gb": 37.4 }
],
"updated_at": "2026-07-28T18:04:59Z"
}
Specifications
- Architecture
- DeepseekV2ForCausalLM
- Parameters
- 15.7 B
- Tensor type
- BF16
- Context length
- 163,840
- Vocabulary
- 102,400
- Layers / heads
- 27 / 16
- Licence
- other
- First seen on the Hub
- 2024-06-14
- Training datasets
- undisclosed
- Added to our catalog
- 2026-07-28
Compare with
Sponsored · GPU cloud
Not enough VRAM?
Spin up a 24 GB L4 instance in 40 seconds. $0.44/hr.