NVIDIA32 GB VRAM1,792 GB/s$0.5/hr

SmolLM2 1.7B Instruct on RTX 5090 32GB: 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 RTX 5090 32GB. Every figure is computed by tokcalc's open-source capacity model.

Fits?
Yes, 1 GPU
16.3 GB needed / 32 GB available
Decode speed
342.6
tok/s per stream
Aggregate
4111
tok/s @ 8 concurrent
Cost / 1M tokens
$0.0338
1× RTX 5090 32GB
What changes as you add RTX 5090 32GBs
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× RTX32 GB16.3 GB342.64111563.4 ms$0.0338✓
2× RTX64 GB16.3 GB582.413978297.0 ms$0.0199✓
4× RTX128 GB16.3 GB116555910163.5 ms$0.009937✓
8× RTX256 GB16.3 GB233022364296.7 ms$0.004968✓
Concurrency vs context length
On 1× RTX 5090 32GB at FP16. KV cache scales linearly with context, so this is the number of users your GPUs can actually serve.
ContextConcurrent usersKV per requestFits?
4K35824.6 MB✓ fits
8K171.61 GB✓ fits
33K46.44 GB✓ fits
66K212.88 GB✓ fits
131K125.77 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 4090 24GB1×2312$0.0480
AMD MI300X 192GB1×12159$0.0571
V100 SXM2 32GB1×2065$0.0673
A100 40GB SXM41×3556$0.0703
AMD MI325X 288GB1×13765$0.0706
Frequently asked questions

Can SmolLM2 1.7B Instruct run on RTX 5090 32GB?

Yes. SmolLM2 1.7B Instruct at FP16 needs 16.3 GB including the KV cache, and RTX 5090 32GB has 32 GB — so it fits on a single GPU.

How fast is SmolLM2 1.7B Instruct on RTX 5090 32GB?

342.6 tokens/sec per stream and 4111 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 563.4 ms, and inter-token latency is 2.9 ms.

What does SmolLM2 1.7B Instruct on RTX 5090 32GB cost per million tokens?

$0.0338 per 1M output tokens at 1× RTX 5090 32GB ($0.5000/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 RTX 5090 32GB?

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 RTX 5090 32GB the right choice for SmolLM2 1.7B Instruct?

It works. Compare $0.0338 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