✅Runs easilyfp16 · 4K ctx · batch 1
Run Qwen2.5-7B on RTX 5090
At fp16 precision, Qwen2.5-7B (7.0 B parameters) needs roughly 15.4 GB of VRAM. RTX 5090 has 32 GB, leaving 16.6 GB of headroom — plenty of room for longer contexts and larger batch sizes. Expected throughput ≈ 88 tokens/sec on a single card.
48%
VRAM
Runs easily
Estimated VRAM (fp16)
15.4 GB
of 32 GB on RTX 5090· 16.6 GB free
Tok/sec
88
TFTT
410ms
Power
270W
Rent on GPUniq marketplace
RTX 5090
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 5090? |
|---|---|---|---|
| FP16 (full precision) | 15.4 GB | 48% | ✅Runs easily |
| INT8 (8-bit) | 9.6 GB | 30% | ✅Runs easily |
| INT4 / Q4 (4-bit) | 6.7 GB | 21% | ✅Runs easily |
Frequently asked
- Can RTX 5090 run Qwen2.5-7B?
- Yes. Qwen2.5-7B needs ≈ 15.4 GB VRAM at fp16 and RTX 5090 provides 32 GB. Expected throughput is 88 tokens/sec per GPU.
- How much VRAM does Qwen2.5-7B use?
- About 15.4 GB at fp16, 6.7 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 Qwen2.5-7B on RTX 5090?
- 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 5090, enable Flash Attention 2.
- Where can I rent a RTX 5090?
- GPUniq aggregates live RTX 5090 offers from verified providers. You can deploy an instance in about a minute and pay hourly with no commitment.
Other GPUs for Qwen2.5-7B
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Chat with Qwen2.5-7B — no setup
Send a prompt and see how Qwen2.5-7B responds directly in our chat. No installation, no GPU required to test.
Want to tweak sequence length, batch size, or fine-tuning? Open the full calculator →