Can I Run DeepSeek Coder V2 236B MoE on NVIDIA RTX 4070 Ti Super (16GB)?
Full VRAM footprint analysis, tokens/sec generation benchmark, and copy-paste Ollama & vLLM runtime commands for running DeepSeek Coder V2 236B MoE (236B) on NVIDIA RTX 4070 Ti Super (16GB) (16 GB).
✕ Out of Memory — Exceeds VRAM Capacity
OLLAMA_FLASH_ATTENTION=1 OLLAMA_KV_CACHE_TYPE=q8_0 ollama run deepseek-coder-v2:236bLocal Hardware Configurations & Pricing
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Quantization Precision Matrix
32K ctx • KV: Q8_0| Quantization | Weights | KV Cache | Total VRAM | Est. Speed | Fit Status |
|---|---|---|---|---|---|
| FP1616 bpw | 472 GB | 4.03 GB | 493.3 GB (3083%) | 0 t/s | OOM |
| Q8_08.5 bpw | 250.16 GB | 4.03 GB | 263.7 GB (1648%) | 0 t/s | OOM |
| Q6_K6.56 bpw | 193.52 GB | 4.03 GB | 205.07 GB (1282%) | 0 t/s | OOM |
| Q5_K_M5.68 bpw | 167.56 GB | 4.03 GB | 178.2 GB (1114%) | 0 t/s | OOM |
| Q4_K_M4.8 bpw | 141.6 GB | 4.03 GB | 151.34 GB (946%) | 0 t/s | OOM |
| FP44.25 bpw | 125.08 GB | 4.03 GB | 134.24 GB (839%) | 0 t/s | OOM |
| IQ3_XS3.3 bpw | 96.76 GB | 4.03 GB | 104.93 GB (656%) | 0 t/s | OOM |
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Frequently Asked Questions: DeepSeek Coder V2 236B MoE on NVIDIA RTX 4070 Ti Super (16GB)
How much VRAM does DeepSeek Coder V2 236B MoE need on NVIDIA RTX 4070 Ti Super (16GB)?
At standard 4-bit quantization (Q4_K_M) with 32K context, DeepSeek Coder V2 236B MoE requires 151.34 GB of total VRAM (weights + KV cache + CUDA runtime overhead).
What inference speed will DeepSeek Coder V2 236B MoE achieve on NVIDIA RTX 4070 Ti Super (16GB)?
Based on NVIDIA RTX 4070 Ti Super (16GB)'s 672 GB/s memory bandwidth, expected decode speed is approximately 0 tokens per second.
Can I expand context beyond 32K tokens?
Each additional 32K tokens of context requires additional KV cache memory. For longer context windows, consider 8-bit or 4-bit KV cache quantization.