Apple192 GB VRAM800 GB/s$0/hr

SmolLM2 1.7B Instruct on Mac M2 Ultra (192GB): 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 Mac M2 Ultra (192GB). Every figure is computed by tokcalc's open-source capacity model.

Fits?
Yes, 1 GPU
16.3 GB needed / 192 GB available
Decode speed
152.9
tok/s per stream
Aggregate
1835
tok/s @ 8 concurrent
Cost / 1M tokens
—
1× Mac M2 Ultra (192GB) · quote required
What changes as you add Mac M2 Ultra (192GB)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× Mac192 GB16.3 GB152.918352.10 s—✓
2× Mac384 GB16.3 GB260.062401.07 s—✓
4× Mac768 GB16.3 GB520.024960547.7 ms—✓
8× Mac1536 GB16.3 GB104099840288.9 ms—✓
Concurrency vs context length
On 1× Mac M2 Ultra (192GB) at FP16. KV cache scales linearly with context, so this is the number of users your GPUs can actually serve.
ContextConcurrent usersKV per requestFits?
4K234824.6 MB✓ fits
8K1171.61 GB✓ fits
33K296.44 GB✓ fits
66K1412.88 GB✓ fits
131K725.77 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 Mac M2 Ultra (192GB)?

Yes. SmolLM2 1.7B Instruct at FP16 needs 16.3 GB including the KV cache, and Mac M2 Ultra (192GB) has 192 GB — so it fits on a single GPU.

How fast is SmolLM2 1.7B Instruct on Mac M2 Ultra (192GB)?

152.9 tokens/sec per stream and 1835 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 2.10 s, and inter-token latency is 6.5 ms.

What does SmolLM2 1.7B Instruct on Mac M2 Ultra (192GB) cost per million tokens?

— per 1M output tokens at 1× Mac M2 Ultra (192GB). 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 Mac M2 Ultra (192GB)?

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 Mac M2 Ultra (192GB) the right choice for SmolLM2 1.7B Instruct?

It works. Compare — 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