Local AI

Can RTX 4090 24GB Run DeepSeek Coder V2 33B? (Tested, 19.5GB VRAM Needed)

Can the RTX 4090 24GB run DeepSeek Coder V2 33B locally? Yes — runs at Q4 (recommended quantization). See VRAM requirements, performance estimates, and the best quantization level for your setup.

📍 Part of the Local LLMs in 2026 guide

✅ Yes — runs at Q4 (recommended quantization)
DeepSeek Coder V2 33B needs 19.5 GB VRAM at Q4. The RTX 4090 24GB has 24 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 4090 24GB
Q4_K_M (recommended)	19.5 GB	✅
Q8_0 (high quality)	34 GB	❌

Expected Performance

Running DeepSeek Coder V2 33B at Q4 on the RTX 4090 24GB, expect approximately ~24 tokens/sec with Ollama or llama.cpp. That’s fast enough for interactive chat — you’ll see responses streaming in real-time.

Headroom: With 19.5 GB used out of 24 GB, you have 4.5 GB free for KV cache (context window). At 4K context, this is plenty. At 32K+ context you may need to reduce batch size.

About the RTX 4090 24GB

Pros: Fastest consumer GPU, excellent for real-time inference

Cons: Expensive, still only 24GB limits 70B models

Price: ~$1,800 — Check current price on Amazon →

Try It Yourself

🎯 LLM Hardware Checker

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💾 VRAM Calculator

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