NVIDIA141 GB VRAM4,800 GB/s$4/hr

SmolLM2 1.7B Instruct on H200 SXM5 141GB: 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 H200 SXM5 141GB. Every figure is computed by tokcalc's open-source capacity model.

Fits?
Yes, 1 GPU
16.3 GB needed / 141 GB available
Decode speed
917.6
tok/s per stream
Aggregate
11012
tok/s @ 8 concurrent
Cost / 1M tokens
$0.1009
1× H200 SXM5 141GB
What changes as you add H200 SXM5 141GBs
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× H200141 GB16.3 GB917.61101287.4 ms$0.1009✓
2× H200282 GB16.3 GB15603744058.8 ms$0.0594✓
4× H200564 GB16.3 GB312014976044.4 ms$0.0297✓
8× H2001128 GB16.3 GB624059904037.2 ms$0.0148✓
Concurrency vs context length
On 1× H200 SXM5 141GB at FP16. KV cache scales linearly with context, so this is the number of users your GPUs can actually serve.
ContextConcurrent usersKV per requestFits?
4K170824.6 MB✓ fits
8K851.61 GB✓ fits
33K216.44 GB✓ fits
66K1012.88 GB✓ fits
131K525.77 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 H200 SXM5 141GB?

Yes. SmolLM2 1.7B Instruct at FP16 needs 16.3 GB including the KV cache, and H200 SXM5 141GB has 141 GB — so it fits on a single GPU.

How fast is SmolLM2 1.7B Instruct on H200 SXM5 141GB?

917.6 tokens/sec per stream and 11012 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 87.4 ms, and inter-token latency is 1.1 ms.

What does SmolLM2 1.7B Instruct on H200 SXM5 141GB cost per million tokens?

$0.1009 per 1M output tokens at 1× H200 SXM5 141GB ($4.00/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 H200 SXM5 141GB?

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 H200 SXM5 141GB the right choice for SmolLM2 1.7B Instruct?

It works. Compare $0.1009 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