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

Phi-3 Mini 3.8B on RTX PRO 6000 Blackwell 96GB: Does It Fit, How Fast, What It Costs

Phi-3 Mini 3.8B at FP16 needs 7.6 GB for its weights and 33.4 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
33.4 GB needed / 96 GB available
Decode speed
153.3
tok/s per stream
Aggregate
1839
tok/s @ 8 concurrent
Cost / 1M tokens
$0.2266
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 GB33.4 GB153.318391.03 s$0.2266✓
2× RTX192 GB33.4 GB260.56253531.9 ms$0.1333✓
4× RTX384 GB33.4 GB521.125013281.0 ms$0.0666✓
8× RTX768 GB33.4 GB1042100050155.5 ms$0.0333✓
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?
4K541.61 GB✓ fits
8K273.22 GB✓ fits
33K612.88 GB✓ fits
66K325.77 GB✓ fits
131K151.54 GB✓ fits
Other GPUs that run Phi-3 Mini 3.8B
Same workload, same quantization — ranked by value (tokens per dollar-hour).
GPUGPUsAggregate tok/sCost / 1M tok
RTX 5090 32GB2×6253$0.0444
AMD MI300X 192GB1×5439$0.1277
A100 40GB SXM41×1591$0.1572
AMD MI325X 288GB1×6158$0.1579
A100 80GB SXM41×2053$0.1624
Intel Gaudi 3 128GB1×3387$0.1640
Frequently asked questions

Can Phi-3 Mini 3.8B run on RTX PRO 6000 Blackwell 96GB?

Yes. Phi-3 Mini 3.8B at FP16 needs 33.4 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-3 Mini 3.8B on RTX PRO 6000 Blackwell 96GB?

153.3 tokens/sec per stream and 1839 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 1.03 s, and inter-token latency is 6.5 ms.

What does Phi-3 Mini 3.8B on RTX PRO 6000 Blackwell 96GB cost per million tokens?

$0.2266 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-3 Mini 3.8B need on RTX PRO 6000 Blackwell 96GB?

7.6 GB for the weights at FP16, plus 25.8 GB of KV cache for 8 concurrent requests at 8K context — 33.4 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-3 Mini 3.8B?

It works. Compare $0.2266 per 1M tokens against the alternatives below — the cheapest option that fits the same workload is 4× RTX 3080 10GB at $0.0209, and the fastest is 1× Cerebras CS-3 (WSE-3) at 20526 tok/s.

Adjust the workload yourself

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

Open in calculator