Google32 GB VRAM819 GB/s$0.8/hr

SmolLM2 1.7B Instruct on TPU v6e Trillium (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 TPU v6e Trillium (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
156.6
tok/s per stream
Aggregate
1879
tok/s @ 8 concurrent
Cost / 1M tokens
$0.1183
1× TPU v6e Trillium (32GB)
What changes as you add TPU v6e Trillium (32GB)s
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× TPU32 GB16.3 GB156.6187997.1 ms$0.1183✓
2× TPU64 GB16.3 GB266.2638864.1 ms$0.0696✓
4× TPU128 GB16.3 GB532.42555347.0 ms$0.0348✓
8× TPU256 GB16.3 GB106510221138.5 ms$0.0174✓
Concurrency vs context length
On 1× TPU v6e Trillium (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 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 TPU v6e Trillium (32GB)?

Yes. SmolLM2 1.7B Instruct at FP16 needs 16.3 GB including the KV cache, and TPU v6e Trillium (32GB) has 32 GB — so it fits on a single GPU.

How fast is SmolLM2 1.7B Instruct on TPU v6e Trillium (32GB)?

156.6 tokens/sec per stream and 1879 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 97.1 ms, and inter-token latency is 6.4 ms.

What does SmolLM2 1.7B Instruct on TPU v6e Trillium (32GB) cost per million tokens?

$0.1183 per 1M output tokens at 1× TPU v6e Trillium (32GB) ($0.8000/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 TPU v6e Trillium (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 TPU v6e Trillium (32GB) the right choice for SmolLM2 1.7B Instruct?

It works. Compare $0.1183 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