❌Won't fitfp16 · 4K ctx · batch 1
Does RTX 3090 run Qwen2.5-72B? No at fp16.
At fp16 precision, Qwen2.5-72B (72 B parameters) needs roughly 164.1 GB of VRAM. RTX 3090 only has 24 GB, short by 140.1 GB. INT4 quantization brings it down to 44.6 GB.
684%
VRAM
Won't fit
Estimated VRAM (fp16)
164.1 GB
of 24 GB on RTX 3090· 140.1 GB short
Tok/sec
17
TFTT
3906ms
Power
223W
Rent on GPUniq marketplace
RTX 3090
Live prices from verified providers · billed hourly, no commitment.
Memory breakdown (fp16)
Model Weights
144.00 GBKV Cache
3.75 GBActivations
15.00 GBFramework Overhead
1.36 GBQuantization 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) | 164.1 GB | 684% | ❌Won't fit |
| INT8 (8-bit) | 84.4 GB | 352% | ❌Won't fit |
| INT4 / Q4 (4-bit) | 44.6 GB | 186% | ❌Won't fit |
Frequently asked
- Can RTX 3090 run Qwen2.5-72B?
- Not at fp16 — Qwen2.5-72B needs about 164.1 GB while RTX 3090 has 24 GB. It fits at INT4 quantization (44.6 GB) with some accuracy trade-off.
- How much VRAM does Qwen2.5-72B use?
- About 164.1 GB at fp16, 44.6 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 Qwen2.5-72B 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 Qwen2.5-72B
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None of the common quantization levels fit this model on RTX 3090. Consider multi-GPU deployment or a larger card.