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

Phi-4 14B on RTX PRO 6000 Blackwell 96GB: Does It Fit, How Fast, What It Costs

Phi-4 14B at FP16 needs 28.0 GB for its weights and 38.7 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
38.7 GB needed / 96 GB available
Decode speed
41.60
tok/s per stream
Aggregate
499.2
tok/s @ 8 concurrent
Cost / 1M tokens
$0.8347
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 GB38.7 GB41.60499.23.72 s$0.8347✓
2× RTX192 GB38.7 GB70.7216971.88 s$0.4910✓
4× RTX384 GB38.7 GB141.46789954.6 ms$0.2455✓
8× RTX768 GB38.7 GB282.927156492.3 ms$0.1227✓
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?
4K101687.2 MB✓ fits
8K501.34 GB✓ fits
33K125.37 GB✓ fits
66K610.74 GB✓ fits
131K321.47 GB✓ fits
Other GPUs that run Phi-4 14B
Same workload, same quantization — ranked by value (tokens per dollar-hour).
GPUGPUsAggregate tok/sCost / 1M tok
RTX 5090 32GB2×1697$0.1637
AMD MI300X 192GB1×1476$0.4704
A100 40GB SXM41×431.8$0.5790
AMD MI325X 288GB1×1671$0.5817
A100 80GB SXM41×557.1$0.5983
Intel Gaudi 3 128GB1×919.3$0.6043
Frequently asked questions

Can Phi-4 14B run on RTX PRO 6000 Blackwell 96GB?

Yes. Phi-4 14B at FP16 needs 38.7 GB including the KV cache, and RTX PRO 6000 Blackwell 96GB has 96 GB — so it fits on a single GPU.

How fast is Phi-4 14B on RTX PRO 6000 Blackwell 96GB?

41.60 tokens/sec per stream and 499.2 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 3.72 s, and inter-token latency is 24.0 ms.

What does Phi-4 14B on RTX PRO 6000 Blackwell 96GB cost per million tokens?

$0.8347 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 Phi-4 14B need on RTX PRO 6000 Blackwell 96GB?

28.0 GB for the weights at FP16, plus 10.7 GB of KV cache for 8 concurrent requests at 8K context — 38.7 GB total. Every extra concurrent user at long context costs more, so leave headroom.

Is RTX PRO 6000 Blackwell 96GB the right choice for Phi-4 14B?

It works. Compare $0.8347 per 1M tokens against the alternatives below — the cheapest option that fits the same workload is 4× RTX 3080 10GB at $0.0772, and the fastest is 1× Cerebras CS-3 (WSE-3) at 5571 tok/s.

Adjust the workload yourself

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

Open in calculator