Can I Run DeepSeek Coder V2 236B MoE on NVIDIA GeForce RTX 5070 Ti (16GB GDDR7)?
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 GeForce RTX 5070 Ti (16GB GDDR7) (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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Best price-to-VRAM homelab rig for 70B Q4_K_M & Qwen 3.8 27B Q8
Apple Mac Studio M4 Max (128GB Unified Memory)
Running Llama 4 Scout 109B & 70B Q8 locally with 128K+ context
NVIDIA GeForce RTX 5070 Ti (16GB GDDR7)
Next-gen 16GB Blackwell GPU for fast 14B–32B Q4_K_M coding
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Out of VRAMSpin up an on-demand cloud GPU instance in 60 seconds with pre-installed Ollama and vLLM. (DeepSeek Coder V2 236B MoE @ 151.34 GB VRAM)
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 GeForce RTX 5070 Ti (16GB GDDR7)
How much VRAM does DeepSeek Coder V2 236B MoE need on NVIDIA GeForce RTX 5070 Ti (16GB GDDR7)?
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 GeForce RTX 5070 Ti (16GB GDDR7)?
Based on NVIDIA GeForce RTX 5070 Ti (16GB GDDR7)'s 896 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.