✅Runs wellfp16 · 4K ctx · batch 1
Run Qwen3-8B on RTX 5090
At fp16 precision, Qwen3-8B (8.0 B parameters) needs roughly 20.2 GB of VRAM. RTX 5090 has 32 GB, leaving 11.8 GB of headroom — plenty of room for longer contexts and larger batch sizes. Expected throughput ≈ 105 tokens/sec on a single card.
63%
VRAM
Runs well
Estimated VRAM (fp16)
20.2 GB
of 32 GB on RTX 5090· 11.8 GB free
Tok/sec
105
TFTT
637ms
Power
220W
Rent on GPUniq marketplace
RTX 5090
Live prices from verified providers · billed hourly, no commitment.
Memory breakdown (fp16)
Model Weights
16.00 GBKV Cache
0.63 GBActivations
2.50 GBFramework Overhead
1.04 GBQuantization 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) | 20.2 GB | 63% | ✅Runs well |
| INT8 (8-bit) | 10.9 GB | 34% | ✅Runs easily |
| INT4 / Q4 (4-bit) | 6.3 GB | 20% | ✅Runs easily |
Frequently asked
- Can RTX 5090 run Qwen3-8B?
- Yes. Qwen3-8B needs ≈ 20.2 GB VRAM at fp16 and RTX 5090 provides 32 GB. Expected throughput is 105 tokens/sec per GPU.
- How much VRAM does Qwen3-8B use?
- About 20.2 GB at fp16, 6.3 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 Qwen3-8B 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 Qwen3-8B
Try it live
Chat with Qwen3-8B — no setup
Send a prompt and see how Qwen3-8B 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 →