📍 Part of the Local LLMs in 2026 guide
DeepSeek Coder V2 33B needs 19.5 GB VRAM at Q4. The RTX 4060 Ti 16GB has 16 GB.
● DeepSeek Coder V2 33B (DeepSeek) is a 33B parameter model used for Code generation and analysis. Top-tier code model, competitive with GPT-4 for programming.
VRAM Requirements
| Quantization | VRAM Needed | RTX 4060 Ti 16GB |
|---|---|---|
| Q4_K_M (recommended) | 19.5 GB | ❌ |
| Q8_0 (high quality) | 34 GB | ❌ |
Why It Won’t Fit
DeepSeek Coder V2 33B needs 19.5 GB VRAM at Q4 quantization, but the RTX 4060 Ti 16GB only has 16 GB. You’re 3.5 GB short.
Options: You can run it with CPU offloading (expect ~16 tok/s — very slow), or upgrade to a GPU with 19.5+ GB VRAM.
About the RTX 4060 Ti 16GB
Pros: 16GB unlocks 14B models, efficient power draw, DLSS 3
Cons: Limited to 128-bit bus, not ideal for batch inference
Price: ~$450 — Check current price on Amazon →
Try It Yourself
🎯 LLM Hardware Checker
Select your exact GPU + RAM and see ALL models you can run.
💾 VRAM Calculator
Pick any model, see exact VRAM at Q4/Q5/Q8/FP16 with context scaling.
About the speed figure. The tokens/sec number above is an estimate, not a measured benchmark. Real throughput depends on your runtime and backend (Ollama, llama.cpp, vLLM), the exact quantization you download, context length, memory bandwidth, and whether any layers are offloaded to CPU. Treat it as a rough guide to the tier of performance, not a promised result.