Intel128 GB VRAM3,300 GB/s$2/hr

SmolLM2 1.7B Instruct on Intel Gaudi 3 128GB: 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 Intel Gaudi 3 128GB. Every figure is computed by tokcalc's open-source capacity model.

Fits?
Yes, 1 GPU
16.3 GB needed / 128 GB available
Decode speed
630.9
tok/s per stream
Aggregate
7571
tok/s @ 8 concurrent
Cost / 1M tokens
$0.0734
1× Intel Gaudi 3 128GB
What changes as you add Intel Gaudi 3 128GBs
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× Intel128 GB16.3 GB630.9757161.9 ms$0.0734✓
2× Intel256 GB16.3 GB10732574046.1 ms$0.0432✓
4× Intel512 GB16.3 GB214510296038.1 ms$0.0216✓
8× Intel1024 GB16.3 GB429041184034.0 ms$0.0108✓
Concurrency vs context length
On 1× Intel Gaudi 3 128GB at FP16. KV cache scales linearly with context, so this is the number of users your GPUs can actually serve.
ContextConcurrent usersKV per requestFits?
4K154824.6 MB✓ fits
8K771.61 GB✓ fits
33K196.44 GB✓ fits
66K912.88 GB✓ fits
131K425.77 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 Intel Gaudi 3 128GB?

Yes. SmolLM2 1.7B Instruct at FP16 needs 16.3 GB including the KV cache, and Intel Gaudi 3 128GB has 128 GB — so it fits on a single GPU.

How fast is SmolLM2 1.7B Instruct on Intel Gaudi 3 128GB?

630.9 tokens/sec per stream and 7571 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 61.9 ms, and inter-token latency is 1.6 ms.

What does SmolLM2 1.7B Instruct on Intel Gaudi 3 128GB cost per million tokens?

$0.0734 per 1M output tokens at 1× Intel Gaudi 3 128GB ($2.00/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 Intel Gaudi 3 128GB?

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 Intel Gaudi 3 128GB the right choice for SmolLM2 1.7B Instruct?

It works. Compare $0.0734 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