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DeepSeek Coder V2 33B needs 19.5 GB VRAM at Q4. The RTX 4060 8GB has 8 GB.
🔍 DeepSeek Coder V2 33B (DeepSeek) is a 33B-parameter model for code generation and analysis. Top-tier code model, competitive with GPT-4 for programming. The question is whether the RTX 4060 8GB’s 8 GB of VRAM can hold it — here’s the exact math.
VRAM requirements
| Quantization | VRAM needed | RTX 4060 8GB |
|---|---|---|
| Q4_K_M (recommended) | 19.5 GB | ❌ |
| Q8_0 (near-lossless) | 34 GB | ❌ |
Why it won’t fit (and what to do)
DeepSeek Coder V2 33B needs 19.5 GB at Q4 but the RTX 4060 8GB has only 8 GB — you’re 11.5 GB short. You can offload layers to system RAM, but expect single-digit tokens/sec (painfully slow for chat).
About the RTX 4060 8GB
Architecture: Ada · VRAM: 8 GB · Price: ~$300.
Good: Cheap, efficient Ada card, DLSS 3, low power draw
Watch out: Only 8GB VRAM — caps you at 7-8B models
Check current RTX 4060 8GB price on Amazon →
Related checks
Other models on the RTX 4060 8GB
🎯 LLM Hardware Checker
Enter your exact GPU + RAM, see every model you can run.
💾 VRAM Requirements
Exact VRAM per model at Q4/Q5/Q8/FP16 with context scaling.
Frequently asked questions
Can the RTX 4060 8GB run DeepSeek Coder V2 33B?
No — not enough VRAM without CPU offloading. DeepSeek Coder V2 33B needs 19.5 GB VRAM at Q4_K_M; the RTX 4060 8GB has 8 GB.
How much VRAM does DeepSeek Coder V2 33B need?
DeepSeek Coder V2 33B needs about 19.5 GB at Q4_K_M (recommended) and 34 GB at Q8_0.
How many tokens per second will the RTX 4060 8GB get on DeepSeek Coder V2 33B?
It won't fit in VRAM; with CPU offloading expect very slow single-digit tokens/sec.