Can I Run DeepSeek Coder V2 236B MoE on NVIDIA GeForce RTX 4090 (24GB GDDR6X)?
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 4090 (24GB GDDR6X) (24 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
⚠️ Out of VRAM: High-Capacity Hardware UpgradesNVIDIA GeForce RTX 4090 (24GB GDDR6X)
The 24GB CUDA enthusiast standard for 14B–32B Q4_K_M models
2x NVIDIA RTX 3090 Dual Rig (48GB Total VRAM)
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
⚠️ Out of VRAM? Don't downgrade model quality.
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 (2055%) | 0 t/s | OOM |
| Q8_08.5 bpw | 250.16 GB | 4.03 GB | 263.7 GB (1099%) | 0 t/s | OOM |
| Q6_K6.56 bpw | 193.52 GB | 4.03 GB | 205.07 GB (854%) | 0 t/s | OOM |
| Q5_K_M5.68 bpw | 167.56 GB | 4.03 GB | 178.2 GB (743%) | 0 t/s | OOM |
| Q4_K_M4.8 bpw | 141.6 GB | 4.03 GB | 151.34 GB (631%) | 0 t/s | OOM |
| FP44.25 bpw | 125.08 GB | 4.03 GB | 134.24 GB (559%) | 0 t/s | OOM |
| IQ3_XS3.3 bpw | 96.76 GB | 4.03 GB | 104.93 GB (437%) | 0 t/s | OOM |
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Frequently Asked Questions: DeepSeek Coder V2 236B MoE on NVIDIA GeForce RTX 4090 (24GB GDDR6X)
How much VRAM does DeepSeek Coder V2 236B MoE need on NVIDIA GeForce RTX 4090 (24GB GDDR6X)?
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 4090 (24GB GDDR6X)?
Based on NVIDIA GeForce RTX 4090 (24GB GDDR6X)'s 1008 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.