NVIDIA80 GB VRAM3,350 GB/s$2.5/hr

Phi-3 Mini 3.8B on H100 SXM5 80GB: 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 H100 SXM5 80GB. Every figure is computed by tokcalc's open-source capacity model.

Fits?
Yes, 1 GPU
33.4 GB needed / 80 GB available
Decode speed
286.5
tok/s per stream
Aggregate
3438
tok/s @ 8 concurrent
Cost / 1M tokens
$0.2020
1× H100 SXM5 80GB
What changes as you add H100 SXM5 80GBs
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× H10080 GB33.4 GB286.53438159.3 ms$0.2020✓
2× H100160 GB33.4 GB487.11169094.9 ms$0.1188✓
4× H100320 GB33.4 GB974.14675962.5 ms$0.0594✓
8× H100640 GB33.4 GB194818703646.2 ms$0.0297✓
Concurrency vs context length
On 1× H100 SXM5 80GB at FP16. KV cache scales linearly with context, so this is the number of users your GPUs can actually serve.
ContextConcurrent usersKV per requestFits?
4K441.61 GB✓ fits
8K223.22 GB✓ fits
33K512.88 GB✓ fits
66K225.77 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 H100 SXM5 80GB?

Yes. Phi-3 Mini 3.8B at FP16 needs 33.4 GB including the KV cache, and H100 SXM5 80GB has 80 GB — so it fits on a single GPU.

How fast is Phi-3 Mini 3.8B on H100 SXM5 80GB?

286.5 tokens/sec per stream and 3438 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 159.3 ms, and inter-token latency is 3.5 ms.

What does Phi-3 Mini 3.8B on H100 SXM5 80GB cost per million tokens?

$0.2020 per 1M output tokens at 1× H100 SXM5 80GB ($2.50/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 H100 SXM5 80GB?

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 H100 SXM5 80GB the right choice for Phi-3 Mini 3.8B?

It works. Compare $0.2020 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