NVIDIA32 GB VRAM1,792 GB/s$0.5/hr

Qwen 2.5 14B on RTX 5090 32GB: 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 needs 2× RTX 5090 32GB. Every figure is computed by tokcalc's open-source capacity model.

Fits?
Yes, 2×
40.9 GB needed / 64 GB available
Decode speed
70.72
tok/s per stream
Aggregate
1697
tok/s @ 8 concurrent
Cost / 1M tokens
$0.1637
2× RTX 5090 32GB

RTX 5090 32GB has no NVLink, so 2-way tensor parallelism runs over PCIe. tokcalc applies a flat 0.85× multi-GPU efficiency factor for this, which is realistic for NVLink-equipped datacenter cards but optimistic for PCIe scale-out — treat the throughput and cost figures for this configuration as an upper bound, and measure before committing.

What changes as you add RTX 5090 32GBs
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× RTX32 GB40.9 GB0.0000.000——✗
2× RTX64 GB40.9 GB70.7216972.23 s$0.1637✓
4× RTX128 GB40.9 GB141.467891.13 s$0.0818✓
8× RTX256 GB40.9 GB282.927156579.7 ms$0.0409✓
Concurrency vs context length
On 2× RTX 5090 32GB at FP16. KV cache scales linearly with context, so this is the number of users your GPUs can actually serve.
ContextConcurrent usersKV per requestFits?
4K44824.6 MB✓ fits
8K221.61 GB✓ fits
33K56.44 GB✓ fits
66K212.88 GB✓ fits
131K125.77 GB✓ fits
Other GPUs that run Qwen 2.5 14B
Same workload, same quantization — ranked by value (tokens per dollar-hour).
GPUGPUsAggregate tok/sCost / 1M tok
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
B200 SXM 192GB1×2229$0.7479
Frequently asked questions

Can Qwen 2.5 14B run on RTX 5090 32GB?

Yes, but it needs 2× RTX 5090 32GB. At FP16 the total footprint is 40.9 GB (28.0 GB of weights plus 12.9 GB of KV cache at 8K × 8 concurrent users), against 64 GB of available VRAM across 2 GPUs.

How fast is Qwen 2.5 14B on RTX 5090 32GB?

70.72 tokens/sec per stream and 1697 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 2.23 s, and inter-token latency is 14.1 ms.

What does Qwen 2.5 14B on RTX 5090 32GB cost per million tokens?

$0.1637 per 1M output tokens at 2× RTX 5090 32GB ($0.5000/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 5090 32GB?

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 5090 32GB the right choice for Qwen 2.5 14B?

It works. Compare $0.1637 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