NVIDIA48 GB VRAM960 GB/s$0.8/hr

Qwen 2.5 14B on RTX 6000 Ada 48GB: Does It Fit, How Fast, What It Costs

Qwen 2.5 14B at FP16 needs 28.0 GB for its weights and 40.9 GB in total at 8K context with 8 concurrent users. That means it fits on a single RTX 6000 Ada 48GB. Every figure is computed by tokcalc's open-source capacity model.

Fits?
Yes, 1 GPU
40.9 GB needed / 48 GB available
Decode speed
22.29
tok/s per stream
Aggregate
267.4
tok/s @ 8 concurrent
Cost / 1M tokens
$0.8310
1× RTX 6000 Ada 48GB
What changes as you add RTX 6000 Ada 48GBs
Tensor-parallel degree vs VRAM, throughput, and cost. Cost scales linearly with GPU count; decode throughput scales near-linearly too, so cost per token stays roughly flat.
GPUsTotal VRAMVRAM neededDecode tok/sAggregate tok/sTTFTCost / 1M tokFits?
1× RTX48 GB40.9 GB22.29267.45.08 s$0.8310✓
2× RTX96 GB40.9 GB37.89909.32.56 s$0.4888✓
4× RTX192 GB40.9 GB75.7736371.30 s$0.2444✓
8× RTX384 GB40.9 GB151.514548662.6 ms$0.1222✓
Concurrency vs context length
On 1× RTX 6000 Ada 48GB at FP16. KV cache scales linearly with context, so this is the number of users your GPUs can actually serve.
ContextConcurrent usersKV per requestFits?
4K24824.6 MB✓ fits
8K121.61 GB✓ fits
33K36.44 GB✓ fits
66K112.88 GB✓ fits
131K025.77 GB✗ does not fit
Other GPUs that run Qwen 2.5 14B
Same workload, same quantization — ranked by value (tokens per dollar-hour).
GPUGPUsAggregate tok/sCost / 1M tok
RTX 5090 32GB2×1697$0.1637
AMD MI300X 192GB1×1476$0.4704
AMD MI325X 288GB1×1671$0.5817
A100 80GB SXM41×557.1$0.5983
Intel Gaudi 3 128GB1×919.3$0.6043
H100 SXM5 80GB1×933.2$0.7441
Frequently asked questions

Can Qwen 2.5 14B run on RTX 6000 Ada 48GB?

Yes. Qwen 2.5 14B at FP16 needs 40.9 GB including the KV cache, and RTX 6000 Ada 48GB has 48 GB — so it fits on a single GPU.

How fast is Qwen 2.5 14B on RTX 6000 Ada 48GB?

22.29 tokens/sec per stream and 267.4 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 5.08 s, and inter-token latency is 44.9 ms.

What does Qwen 2.5 14B on RTX 6000 Ada 48GB cost per million tokens?

$0.8310 per 1M output tokens at 1× RTX 6000 Ada 48GB ($0.8000/hr). That is the marginal hardware cost — add idle capacity, ops, and egress for the real figure.

How much VRAM does Qwen 2.5 14B need on RTX 6000 Ada 48GB?

28.0 GB for the weights at FP16, plus 12.9 GB of KV cache for 8 concurrent requests at 8K context — 40.9 GB total. Every extra concurrent user at long context costs more, so leave headroom.

Is RTX 6000 Ada 48GB the right choice for Qwen 2.5 14B?

It works. Compare $0.8310 per 1M tokens against the alternatives below — the cheapest option that fits the same workload is 8× RTX 3080 10GB at $0.0386, and the fastest is 8× RTX 3080 10GB at 11517 tok/s.

Adjust the workload yourself

Same formulas, your context length, concurrency, and quantisation.

Open in calculator