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

SmolLM2 1.7B Instruct on H100 SXM5 80GB: Does It Fit, How Fast, What It Costs

SmolLM2 1.7B Instruct at FP16 needs 3.4 GB for its weights and 16.3 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
16.3 GB needed / 80 GB available
Decode speed
640.4
tok/s per stream
Aggregate
7685
tok/s @ 8 concurrent
Cost / 1M tokens
$0.0904
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 GB16.3 GB640.4768587.8 ms$0.0904✓
2× H100160 GB16.3 GB10892613059.1 ms$0.0532✓
4× H100320 GB16.3 GB217810452044.5 ms$0.0266✓
8× H100640 GB16.3 GB435541808037.3 ms$0.0133✓
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?
4K95824.6 MB✓ fits
8K471.61 GB✓ fits
33K116.44 GB✓ fits
66K512.88 GB✓ fits
131K225.77 GB✓ fits
Other GPUs that run SmolLM2 1.7B Instruct
Same workload, same quantization — ranked by value (tokens per dollar-hour).
GPUGPUsAggregate tok/sCost / 1M tok
RTX 3090 24GB1×2147$0.0323
RTX 5090 32GB1×4111$0.0338
RTX 4090 24GB1×2312$0.0480
AMD MI300X 192GB1×12159$0.0571
V100 SXM2 32GB1×2065$0.0673
A100 40GB SXM41×3556$0.0703
Frequently asked questions

Can SmolLM2 1.7B Instruct run on H100 SXM5 80GB?

Yes. SmolLM2 1.7B Instruct at FP16 needs 16.3 GB including the KV cache, and H100 SXM5 80GB has 80 GB — so it fits on a single GPU.

How fast is SmolLM2 1.7B Instruct on H100 SXM5 80GB?

640.4 tokens/sec per stream and 7685 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 87.8 ms, and inter-token latency is 1.6 ms.

What does SmolLM2 1.7B Instruct on H100 SXM5 80GB cost per million tokens?

$0.0904 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 SmolLM2 1.7B Instruct need on H100 SXM5 80GB?

3.4 GB for the weights at FP16, plus 12.9 GB of KV cache for 8 concurrent requests at 8K context — 16.3 GB total. Every extra concurrent user at long context costs more, so leave headroom.

Is H100 SXM5 80GB the right choice for SmolLM2 1.7B Instruct?

It works. Compare $0.0904 per 1M tokens against the alternatives below — the cheapest option that fits the same workload is 2× RTX 3080 10GB at $0.0187, and the fastest is 1× Cerebras CS-3 (WSE-3) at 45882 tok/s.

Adjust the workload yourself

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

Open in calculator