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

OLMo 2 13B on RTX PRO 6000 Blackwell 96GB: Does It Fit, How Fast, What It Costs

OLMo 2 13B at FP16 needs 26.0 GB for its weights and 36.7 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
36.7 GB needed / 96 GB available
Decode speed
44.80
tok/s per stream
Aggregate
537.6
tok/s @ 8 concurrent
Cost / 1M tokens
$0.7750
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 GB36.7 GB44.80537.63.46 s$0.7750✓
2× RTX192 GB36.7 GB76.1618281.75 s$0.4559✓
4× RTX384 GB36.7 GB152.37311888.5 ms$0.2280✓
8× RTX768 GB36.7 GB304.629245459.3 ms$0.1140✓
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?
4K104687.2 MB✓ fits
8K521.34 GB✓ fits
33K135.37 GB✓ fits
66K610.74 GB✓ fits
131K321.47 GB✓ fits
Other GPUs that run OLMo 2 13B
Same workload, same quantization — ranked by value (tokens per dollar-hour).
GPUGPUsAggregate tok/sCost / 1M tok
RTX 5090 32GB2×1828$0.1520
AMD MI300X 192GB1×1590$0.4368
A100 40GB SXM41×465.0$0.5376
AMD MI325X 288GB1×1800$0.5401
A100 80GB SXM41×600.0$0.5556
Intel Gaudi 3 128GB1×990.0$0.5612
Frequently asked questions

Can OLMo 2 13B run on RTX PRO 6000 Blackwell 96GB?

Yes. OLMo 2 13B at FP16 needs 36.7 GB including the KV cache, and RTX PRO 6000 Blackwell 96GB has 96 GB — so it fits on a single GPU.

How fast is OLMo 2 13B on RTX PRO 6000 Blackwell 96GB?

44.80 tokens/sec per stream and 537.6 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 3.46 s, and inter-token latency is 22.3 ms.

What does OLMo 2 13B on RTX PRO 6000 Blackwell 96GB cost per million tokens?

$0.7750 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 OLMo 2 13B need on RTX PRO 6000 Blackwell 96GB?

26.0 GB for the weights at FP16, plus 10.7 GB of KV cache for 8 concurrent requests at 8K context — 36.7 GB total. Every extra concurrent user at long context costs more, so leave headroom.

Is RTX PRO 6000 Blackwell 96GB the right choice for OLMo 2 13B?

It works. Compare $0.7750 per 1M tokens against the alternatives below — the cheapest option that fits the same workload is 4× RTX 3080 10GB at $0.0717, and the fastest is 1× Cerebras CS-3 (WSE-3) at 6000 tok/s.

Adjust the workload yourself

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

Open in calculator