Can the RTX 5090 Run DeepSeek?
Yes for DeepSeek-V2 16B (Lite) — fits the 32 GB at FP16. DeepSeek-V2 236B and V3 671B do not fit any single GPU and require a multi-GPU cluster.
Yes — tightThe RTX 5090 (32 GB) just fits DeepSeek-V2 16B at FP16 (32 GB). For comfortable production use, drop to FP8 / INT8 quantisation.
Detailed Breakdown
DeepSeek-V2 16B Lite at FP16 needs ~32 GB. The RTX 5090 has exactly 32 GB. The fit is razor-tight but works for short-context inference. For comfortable production deployment, use FP8 (~16 GB) or AWQ-INT4 (~10 GB).
- DeepSeek-V2 16B Lite FP16 — 32 GB. Tight, no KV cache room. FP8 is the practical path.
- DeepSeek-V2 16B Lite FP8 — 16 GB. Comfortable, 32K context fits.
- DeepSeek-Coder 6.7B FP16 — 13 GB. Trivial fit.
- DeepSeek-Coder 33B FP16 — 66 GB, doesn’t fit.
- DeepSeek-V2 236B — 470 GB FP16, requires multi-GPU cluster.
- DeepSeek-V3 671B — frontier-class, multi-node H100 only.
For most teams the practical DeepSeek deployment on a 5090 is V2 16B Lite at FP8. It outperforms Llama 3 8B on reasoning at similar throughput.
Frequently Asked Questions
The questions buyers actually ask before committing to a GPU server.
DeepSeek-V2 16B Lite vs Llama 3 8B on a 5090?
DeepSeek wins on reasoning, Llama on general knowledge. Throughput is similar at FP8.
DeepSeek-V3 on a 5090?
No. V3 has 671B parameters; impossible single-GPU. Cluster needed.
DeepSeek-Coder for IDE integration?
Yes — works with Continue.dev, Cursor (via OpenAI-compatible endpoint), Cody.
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