✅Runs easilyfp16 · 4K ctx · batch 1

Run Qwen3-8B on RTX A6000

At fp16 precision, Qwen3-8B (8.0 B parameters) needs roughly 20.2 GB of VRAM. RTX A6000 has 48 GB, leaving 27.8 GB of headroom — plenty of room for longer contexts and larger batch sizes. Expected throughput ≈ 82 tokens/sec on a single card.

42%
VRAM
Runs easily
Estimated VRAM (fp16)
20.2 GB
of 48 GB on RTX A6000· 27.8 GB free
Tok/sec
82
TFTT
818ms
Power
115W
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RTX A6000
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Memory breakdown (fp16)

Model Weights
16.00 GB
KV Cache
0.63 GB
Activations
2.50 GB
Framework Overhead
1.04 GB

Quantization comparison

Lower precision = less VRAM with a small quality trade-off. Quality order: FP16 > INT8 > INT4.

PrecisionVRAMUtilisationFits on RTX A6000?
FP16 (full precision)20.2 GB42%✅Runs easily
INT8 (8-bit)10.9 GB23%✅Runs easily
INT4 / Q4 (4-bit)6.3 GB13%✅Runs easily

Frequently asked

Can RTX A6000 run Qwen3-8B?
Yes. Qwen3-8B needs ≈ 20.2 GB VRAM at fp16 and RTX A6000 provides 48 GB. Expected throughput is 82 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 A6000?
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 A6000, enable Flash Attention 2.
Where can I rent a RTX A6000?
GPUniq aggregates live RTX A6000 offers from verified providers. You can deploy an instance in about a minute and pay hourly with no commitment.

Other GPUs for Qwen3-8B

Other models on RTX A6000

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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.

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Want to tweak sequence length, batch size, or fine-tuning? Open the full calculator →