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

Gemma 2 9B on RTX 5090 32GB: Does It Fit, How Fast, What It Costs

Gemma 2 9B at FP16 needs 18.0 GB for its weights and 40.5 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×
40.5 GB needed / 64 GB available
Decode speed
110.0
tok/s per stream
Aggregate
2640
tok/s @ 8 concurrent
Cost / 1M tokens
$0.1052
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 GB40.5 GB0.0000.000——✗
2× RTX64 GB40.5 GB110.026401.44 s$0.1052✓
4× RTX128 GB40.5 GB220.010561736.7 ms$0.0526✓
8× RTX256 GB40.5 GB440.042243383.4 ms$0.0263✓
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?
4K321.41 GB✓ fits
8K162.82 GB✓ fits
33K411.27 GB✓ fits
66K222.55 GB✓ fits
131K145.10 GB✓ fits
Other GPUs that run Gemma 2 9B
Same workload, same quantization — ranked by value (tokens per dollar-hour).
GPUGPUsAggregate tok/sCost / 1M tok
AMD MI300X 192GB1×2297$0.3024
AMD MI325X 288GB1×2600$0.3739
A100 80GB SXM41×866.7$0.3846
Intel Gaudi 3 128GB1×1430$0.3885
H100 SXM5 80GB1×1452$0.4784
B200 SXM 192GB1×3467$0.4808
Frequently asked questions

Can Gemma 2 9B run on RTX 5090 32GB?

Yes, but it needs 2× RTX 5090 32GB. At FP16 the total footprint is 40.5 GB (18.0 GB of weights plus 22.5 GB of KV cache at 8K × 8 concurrent users), against 64 GB of available VRAM across 2 GPUs.

How fast is Gemma 2 9B on RTX 5090 32GB?

110.0 tokens/sec per stream and 2640 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 1.44 s, and inter-token latency is 9.1 ms.

What does Gemma 2 9B on RTX 5090 32GB cost per million tokens?

$0.1052 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 Gemma 2 9B need on RTX 5090 32GB?

18.0 GB for the weights at FP16, plus 22.5 GB of KV cache for 8 concurrent requests at 8K context — 40.5 GB total. Every extra concurrent user at long context costs more, so leave headroom.

Is RTX 5090 32GB the right choice for Gemma 2 9B?

It works. Compare $0.1052 per 1M tokens against the alternatives below — the cheapest option that fits the same workload is 8× RTX 3080 10GB at $0.0248, and the fastest is 8× RTX 3080 10GB at 17916 tok/s.

Adjust the workload yourself

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

Open in calculator