NVIDIA80 GB VRAM2,000 GB/s$1.2/hr

Phi-3 Mini 3.8B on A100 80GB SXM4: 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 A100 80GB SXM4. Every figure is computed by tokcalc's open-source capacity model.

Fits?
Yes, 1 GPU
33.4 GB needed / 80 GB available
Decode speed
171.1
tok/s per stream
Aggregate
2053
tok/s @ 8 concurrent
Cost / 1M tokens
$0.1624
1× A100 80GB SXM4
What changes as you add A100 80GB SXM4s
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× A10080 GB33.4 GB171.12053434.9 ms$0.1624✓
2× A100160 GB33.4 GB290.86979233.0 ms$0.0955✓
4× A100320 GB33.4 GB581.627916131.5 ms$0.0478✓
8× A100640 GB33.4 GB116311166380.7 ms$0.0239✓
Concurrency vs context length
On 1× A100 80GB SXM4 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
Intel Gaudi 3 128GB1×3387$0.1640
H100 SXM5 80GB1×3438$0.2020
Frequently asked questions

Can Phi-3 Mini 3.8B run on A100 80GB SXM4?

Yes. Phi-3 Mini 3.8B at FP16 needs 33.4 GB including the KV cache, and A100 80GB SXM4 has 80 GB — so it fits on a single GPU.

How fast is Phi-3 Mini 3.8B on A100 80GB SXM4?

171.1 tokens/sec per stream and 2053 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 434.9 ms, and inter-token latency is 5.8 ms.

What does Phi-3 Mini 3.8B on A100 80GB SXM4 cost per million tokens?

$0.1624 per 1M output tokens at 1× A100 80GB SXM4 ($1.20/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 A100 80GB SXM4?

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 A100 80GB SXM4 the right choice for Phi-3 Mini 3.8B?

It works. Compare $0.1624 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