NVIDIA96 GB VRAM1,792 GB/s$1.5/hr

Multilingual E5 Large (1024-dim) on RTX PRO 6000 Blackwell 96GB: 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 PRO 6000 Blackwell 96GB. Every figure is computed by tokcalc's open-source capacity model.

Fits?
Yes, 1 GPU
7.6 GB needed / 96 GB available
Decode speed
1040
tok/s per stream
Aggregate
12480
tok/s @ 8 concurrent
Cost / 1M tokens
$0.0334
1× RTX PRO 6000 Blackwell 96GB
What changes as you add RTX PRO 6000 Blackwell 96GBs
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× RTX96 GB7.6 GB104012480177.8 ms$0.0334✓
2× RTX192 GB7.6 GB176842432104.0 ms$0.0196✓
4× RTX384 GB7.6 GB353616972867.0 ms$0.009820✓
8× RTX768 GB7.6 GB707267891248.5 ms$0.004910✓
Concurrency vs context length
On 1× RTX PRO 6000 Blackwell 96GB at FP16. KV cache scales linearly with context, so this is the number of users your GPUs can actually serve.
ContextConcurrent usersKV per requestFits?
4K235412.3 MB✓ fits
8K117824.6 MB✓ fits
33K293.22 GB✓ fits
66K146.44 GB✓ fits
131K712.88 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 3090 24GB1×6519$0.0107
RTX 5090 32GB1×12480$0.0111
RTX 4090 24GB1×7020$0.0158
RTX 4080 16GB1×4993$0.0167
AMD MI300X 192GB1×36911$0.0188
Frequently asked questions

Can Multilingual E5 Large (1024-dim) run on RTX PRO 6000 Blackwell 96GB?

Yes. Multilingual E5 Large (1024-dim) at FP16 needs 7.6 GB including the KV cache, and RTX PRO 6000 Blackwell 96GB has 96 GB — so it fits on a single GPU.

How fast is Multilingual E5 Large (1024-dim) on RTX PRO 6000 Blackwell 96GB?

1040 tokens/sec per stream and 12480 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 177.8 ms, and inter-token latency is 961.5 µs.

What does Multilingual E5 Large (1024-dim) on RTX PRO 6000 Blackwell 96GB cost per million tokens?

$0.0334 per 1M output tokens at 1× RTX PRO 6000 Blackwell 96GB ($1.50/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 PRO 6000 Blackwell 96GB?

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 PRO 6000 Blackwell 96GB the right choice for Multilingual E5 Large (1024-dim)?

It works. Compare $0.0334 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