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DeepSeek
DeepSeek V2 Lite
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Llama-3.1 70B
Analysis By: The WhichLLMModel Editorial Team|Updated: June 2026
Live Hardware Compatibility Simulator
GPU Dedicated VRAM16 GB
System Host RAM32 GB
DeepSeek V2 Lite100% GPU VRAM (Safe)
VRAM Footprint (Q4):10.50 GB
Active GPU Load:10.50 GB
Llama-3.1 70BPartial CPU Offload
VRAM Footprint (Q4):42.11 GB
Active GPU Load:14.18 GB
Hardware & Architecture Shootout Matrix
| Metric / Spec | DeepSeek V2 Lite | Llama-3.1 70B |
|---|---|---|
| Parameter Scale | 16B Parameters | 70B Parameters |
| Attention Architecture | MLA Low-Rank Latent | Standard GQA / MHA |
| Max Context Window | 163.84k | 131.07k |
| Available GGUF Quants | 0 Quant Formats | 2 Quant Formats |
| Full Model Page | Hardware Details | Hardware Details |
Intelligence & Benchmark Head-to-Head
Coding Benchmark (SWE-bench / HumanEval)
DeepSeek V2 Lite29.9%
Llama-3.1 70B80.5%
Reasoning Benchmark (GPQA / MMLU)
DeepSeek V2 LiteN/A%
Llama-3.1 70B46.7%
Frequently Asked Questions about DeepSeek V2 Lite vs Llama-3.1 70B
Llama-3.1 70B is cheaper than DeepSeek V2 Lite. Llama-3.1 70B has a blended cost of $NaN/1M tokens, which is about 1.0x cheaper than DeepSeek V2 Lite at $NaN/1M tokens.
Llama-3.1 70B is better for coding tasks on this benchmark. It scores 80.5% on human-eval (basic standalone code completion and simple functions) compared to DeepSeek V2 Lite which scores 29.9%.
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