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Can the RTX 3080 10GB Run DeepSeek Coder V2 33B? (❌ 19.5GB VRAM Needed, 2026)

Can the RTX 3080 10GB run DeepSeek Coder V2 33B locally? No — not enough VRAM without CPU offloading. VRAM requirements, expected tokens/sec, the right quantization, and the cheapest GPU that fits if it doesn’t.

← Can It Run? — GPU compatibility matrix · Local LLMs guide

❌ No — not enough VRAM without CPU offloading
DeepSeek Coder V2 33B needs 19.5 GB VRAM at Q4. The RTX 3080 10GB has 10 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 3080 10GB’s 10 GB of VRAM can hold it — here’s the exact math.

VRAM requirements

QuantizationVRAM neededRTX 3080 10GB
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 3080 10GB has only 10 GB — you’re 9.5 GB short. You can offload layers to system RAM, but expect single-digit tokens/sec (painfully slow for chat).

💡 Cheapest GPU that runs DeepSeek Coder V2 33B: the RTX 3090 24GB (24 GB, ~$800 used). Check price on Amazon →

About the RTX 3080 10GB

Architecture: Ampere · VRAM: 10 GB · Price: ~$400 used.

Good: Fast memory bandwidth, strong tokens/sec for its tier
Watch out: Only 10GB — awkward middle ground, limits model size

Check current RTX 3080 10GB price on Amazon →

Related checks

Other models on the RTX 3080 10GB

DeepSeek Coder V2 33B on other GPUs

🎯 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 3080 10GB 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 3080 10GB has 10 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 3080 10GB get on DeepSeek Coder V2 33B?

It won't fit in VRAM; with CPU offloading expect very slow single-digit tokens/sec.

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.

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