📍 Part of the Local LLMs in 2026 guide
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
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.