❌Won't fitfp16 · 4K ctx · batch 1
Does RTX 3090 run DeepSeek-V3 671B? No at fp16.
At fp16 precision, DeepSeek-V3 671B (671 B MoE parameters) needs roughly 1347.0 GB of VRAM. RTX 3090 only has 24 GB, short by 1323.0 GB. INT4 quantization brings it down to 343.2 GB.
>999%
VRAM
Won't fit
Estimated VRAM (fp16)
1347.0 GB
of 24 GB on RTX 3090· 1323.0 GB short
Tok/sec
12
TFTT
6222ms
Power
270W
Rent on GPUniq marketplace
RTX 3090
Live prices from verified providers · billed hourly, no commitment.
Memory breakdown (fp16)
Quantization comparison
Lower precision = less VRAM with a small quality trade-off. Quality order: FP16 > INT8 > INT4.
| Precision | VRAM | Utilisation | Fits on RTX 3090? |
|---|---|---|---|
| FP16 (full precision) | 1347.0 GB | 5613% | ❌Won't fit |
| INT8 (8-bit) | 677.8 GB | 2824% | ❌Won't fit |
| INT4 / Q4 (4-bit) | 343.2 GB | 1430% | ❌Won't fit |
Frequently asked
- Can RTX 3090 run DeepSeek-V3 671B?
- Not at fp16 — DeepSeek-V3 671B needs about 1347.0 GB while RTX 3090 has 24 GB. It fits at INT4 quantization (343.2 GB) with some accuracy trade-off.
- How much VRAM does DeepSeek-V3 671B use?
- About 1347.0 GB at fp16, 343.2 GB at INT4 (for a 4K context, batch size 1). Longer contexts add to KV cache size; larger batches increase activations.
- What's the fastest way to run DeepSeek-V3 671B on RTX 3090?
- Use a production inference engine like vLLM, SGLang, or TensorRT-LLM with paged attention — they cut KV cache 2–3× vs the naive estimate and batch multiple requests efficiently. For RTX 3090, enable Flash Attention 2.
- Where can I rent a RTX 3090?
- GPUniq aggregates live RTX 3090 offers from verified providers. You can deploy an instance in about a minute and pay hourly with no commitment.
Other GPUs for DeepSeek-V3 671B
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None of the common quantization levels fit this model on RTX 3090. Consider multi-GPU deployment or a larger card.