The RTX 4090 is the best GPU for running DeepSeek models locally. Its 24GB VRAM fits DeepSeek-R1 32B and DeepSeek Coder V2 Lite at Q4_K_M with room for context, and it delivers ~65 tok/s on 7B variants. For tighter budgets, the RTX 4060 Ti 16GB handles 7B and smaller distilled models well at $425.
NVIDIA GeForce RTX 4090
24GB GDDR6X24GB VRAM fits DeepSeek-R1 32B at Q4_K_M — the strongest local reasoning setup you can build.
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Who this is for
You want to run DeepSeek models on your own hardware instead of relying on the DeepSeek API. Maybe you need privacy for proprietary code, want zero-latency inference, or the API rate limits are slowing you down. This guide covers every DeepSeek model worth running locally and the GPU each one needs.
DeepSeek models and their VRAM requirements
If you’re running DeepSeek through Ollama specifically, our Ollama VRAM Requirements guide cross-references these numbers with Ollama’s exact memory overhead per model.
| Model | Parameters | Q4_K_M Size | Minimum VRAM | Use Case |
|---|---|---|---|---|
| DeepSeek-R1 1.5B | 1.5B | ~1GB | 6GB | Light reasoning tasks |
| DeepSeek-R1 7B | 7B | ~4.5GB | 8GB | General reasoning |
| DeepSeek-R1 14B | 14B | ~8.5GB | 12GB | Balanced quality/speed |
| DeepSeek-R1 32B | 32B | ~19GB | 24GB | Best local reasoning |
| DeepSeek Coder V2 Lite (16B) | 16B | ~9.5GB | 12GB | Code generation |
| DeepSeek V3 (671B MoE) | 671B | ~380GB | Multi-GPU | Research only |
DeepSeek-R1 32B is the sweet spot for local deployment. It rivals GPT-4 on reasoning benchmarks while fitting on a single 24GB card.
VRAM capacity memory bandwidth Specs are manufacturer figures. Bar lengths are scaled independently per metric.
GPU benchmarks for DeepSeek models
Ollama at Q4_K_M. Figures are modelled from each card’s memory bandwidth, not measured — see methodology:
| GPU | R1 7B | R1 14B | R1 32B | Price |
|---|---|---|---|---|
| RTX 5090 (32GB) | ~95 tok/s | ~50 tok/s | ~28 tok/s | ~$4,900 |
| RTX 4090 (24GB) | ~65 tok/s | ~38 tok/s | ~20 tok/s | ~$2,200 |
| RTX 5080 (16GB) | ~55 tok/s | ~32 tok/s | Won’t fit | ~$1,400 |
| RTX 4060 Ti 16GB | ~35 tok/s | ~20 tok/s | Won’t fit | ~$425 |
| RTX 3090 (24GB, used) | ~55 tok/s | ~32 tok/s | ~18 tok/s | ~$820 |
| RTX 3060 12GB (used) | ~25 tok/s | ~15 tok/s | Won’t fit | ~$250 |
Which GPU should you buy for DeepSeek?
If you want DeepSeek-R1 32B for serious reasoning work, the RTX 4090 ($2,200) is the clear winner — 24GB VRAM fits the model at Q4_K_M with headroom for 8K context. If you mostly run 7B distilled models for quick tasks and chat, the RTX 4060 Ti 16GB ($425) delivers 35 tok/s, which feels responsive for interactive use. If budget allows and you want top speed across all sizes, the RTX 5090 ($4,900) handles everything up to 32B with the fastest throughput available.
Common mistakes to avoid
- Buying a 16GB card expecting to run DeepSeek-R1 32B. The model needs ~19GB at Q4_K_M before you add context. 16GB cards cannot fit it at any usable quantization level.
- Running DeepSeek V3 671B locally. This is a 671B MoE model requiring 380GB+ of VRAM. It is a cloud-only model for individual users. Use the API instead. (The newer DeepSeek V4 family has its own hardware guide — V4-Flash is the first variant with a semi-realistic prosumer path at roughly 81-96GB.)
- Ignoring the R1 distilled variants. DeepSeek-R1 7B and 14B are distilled from the full model and perform surprisingly well. You do not always need the 32B version.
- Skipping quantization to preserve quality. FP16 doubles your VRAM needs with marginal quality improvement on reasoning tasks. Q4_K_M is the practical sweet spot.
Our recommendation
| Your goal | Best GPU | Price |
|---|---|---|
| DeepSeek-R1 7B daily driver | RTX 4060 Ti 16GB | ~$425 |
| DeepSeek-R1 32B reasoning | RTX 4090 | ~$2,200 |
| DeepSeek-R1 32B + Coder | RTX 5090 | ~$4,900 |
| Budget DeepSeek setup | RTX 3060 12GB (used) | ~$250 |
The RTX 4090 running DeepSeek-R1 32B is the strongest local reasoning setup you can build in 2026. For coding-focused workflows, pair it with DeepSeek Coder V2 Lite and you have both reasoning and code generation covered on one card.
Check NVIDIA GeForce RTX 4060 Ti 16GB on Amazon→Buy on Shopee SG→ Check NVIDIA GeForce RTX 4090 on Amazon→Buy on Shopee SG→NVIDIA GeForce RTX 5090
32GB GDDR7Handles all DeepSeek variants up to R1 32B at top speed — and runs Coder V2 simultaneously.
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VRAM is the gatekeeper for DeepSeek models. Get the 24GB card and unlock the 32B model, or save money on a 16GB card and stick with the distilled variants — both are valid paths.
For coding-specific GPU advice, see our best GPU for code LLMs guide. If you plan to run DeepSeek through Ollama, our Ollama GPU guide covers setup and optimization tips.