NVIDIA24 GB VRAM936 GB/s$0.25/hr

Multilingual E5 Large (1024-dim) on RTX 3090 24GB: Does It Fit, How Fast, What It Costs

Multilingual E5 Large (1024-dim) at FP16 needs 1.1 GB for its weights and 7.6 GB in total at 8K context with 8 concurrent users. That means it fits on a single RTX 3090 24GB. Every figure is computed by tokcalc's open-source capacity model.

Fits?
Yes, 1 GPU
7.6 GB needed / 24 GB available
Decode speed
543.2
tok/s per stream
Aggregate
6519
tok/s @ 8 concurrent
Cost / 1M tokens
$0.0107
1× RTX 3090 24GB
What changes as you add RTX 3090 24GBs
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× RTX24 GB7.6 GB543.26519547.3 ms$0.0107✓
2× RTX48 GB7.6 GB923.522163288.8 ms$0.006267✓
4× RTX96 GB7.6 GB184788653159.4 ms$0.003133✓
8× RTX192 GB7.6 GB369435461094.7 ms$0.001567✓
Concurrency vs context length
On 1× RTX 3090 24GB at FP16. KV cache scales linearly with context, so this is the number of users your GPUs can actually serve.
ContextConcurrent usersKV per requestFits?
4K56412.3 MB✓ fits
8K28824.6 MB✓ fits
33K73.22 GB✓ fits
66K36.44 GB✓ fits
131K112.88 GB✓ fits
Other GPUs that run Multilingual E5 Large (1024-dim)
Same workload, same quantization — ranked by value (tokens per dollar-hour).
GPUGPUsAggregate tok/sCost / 1M tok
RTX 3080 10GB1×5293$0.0105
RTX 5090 32GB1×12480$0.0111
RTX 4090 24GB1×7020$0.0158
RTX 4080 16GB1×4993$0.0167
AMD MI300X 192GB1×36911$0.0188
V100 SXM2 32GB1×6268$0.0222
Frequently asked questions

Can Multilingual E5 Large (1024-dim) run on RTX 3090 24GB?

Yes. Multilingual E5 Large (1024-dim) at FP16 needs 7.6 GB including the KV cache, and RTX 3090 24GB has 24 GB — so it fits on a single GPU.

How fast is Multilingual E5 Large (1024-dim) on RTX 3090 24GB?

543.2 tokens/sec per stream and 6519 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 547.3 ms, and inter-token latency is 1.8 ms.

What does Multilingual E5 Large (1024-dim) on RTX 3090 24GB cost per million tokens?

$0.0107 per 1M output tokens at 1× RTX 3090 24GB ($0.2500/hr). That is the marginal hardware cost — add idle capacity, ops, and egress for the real figure.

How much VRAM does Multilingual E5 Large (1024-dim) need on RTX 3090 24GB?

1.1 GB for the weights at FP16, plus 6.4 GB of KV cache for 8 concurrent requests at 8K context — 7.6 GB total. Every extra concurrent user at long context costs more, so leave headroom.

Is RTX 3090 24GB the right choice for Multilingual E5 Large (1024-dim)?

It works. Compare $0.0107 per 1M tokens against the alternatives below — the cheapest option that fits the same workload is 1× RTX 3080 10GB at $0.0105, and the fastest is 1× Cerebras CS-3 (WSE-3) at 139286 tok/s.

Adjust the workload yourself

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

Open in calculator