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

SmolLM2 1.7B Instruct on A100 80GB SXM4: 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 A100 80GB SXM4. Every figure is computed by tokcalc's open-source capacity model.

Fits?
Yes, 1 GPU
16.3 GB needed / 80 GB available
Decode speed
382.4
tok/s per stream
Aggregate
4588
tok/s @ 8 concurrent
Cost / 1M tokens
$0.0726
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 GB16.3 GB382.44588211.2 ms$0.0726✓
2× A100160 GB16.3 GB650.015600120.8 ms$0.0427✓
4× A100320 GB16.3 GB13006240075.4 ms$0.0214✓
8× A100640 GB16.3 GB260024960052.7 ms$0.0107✓
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?
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 A100 80GB SXM4?

Yes. SmolLM2 1.7B Instruct at FP16 needs 16.3 GB including the KV cache, and A100 80GB SXM4 has 80 GB — so it fits on a single GPU.

How fast is SmolLM2 1.7B Instruct on A100 80GB SXM4?

382.4 tokens/sec per stream and 4588 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 211.2 ms, and inter-token latency is 2.6 ms.

What does SmolLM2 1.7B Instruct on A100 80GB SXM4 cost per million tokens?

$0.0726 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 SmolLM2 1.7B Instruct need on A100 80GB SXM4?

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 A100 80GB SXM4 the right choice for SmolLM2 1.7B Instruct?

It works. Compare $0.0726 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