Apple128 GB VRAM546 GB/s$0/hr

SmolLM2 1.7B Instruct on Mac M4 Max (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 Mac M4 Max (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
104.4
tok/s per stream
Aggregate
1253
tok/s @ 8 concurrent
Cost / 1M tokens
—
1× Mac M4 Max (128GB) · quote required
What changes as you add Mac M4 Max (128GB)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× Mac128 GB16.3 GB104.412533.32 s—✓
2× Mac256 GB16.3 GB177.542591.67 s—✓
4× Mac512 GB16.3 GB354.917035852.0 ms—✓
8× Mac1024 GB16.3 GB709.868141441.0 ms—✓
Concurrency vs context length
On 1× Mac M4 Max (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 Mac M4 Max (128GB)?

Yes. SmolLM2 1.7B Instruct at FP16 needs 16.3 GB including the KV cache, and Mac M4 Max (128GB) has 128 GB — so it fits on a single GPU.

How fast is SmolLM2 1.7B Instruct on Mac M4 Max (128GB)?

104.4 tokens/sec per stream and 1253 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 3.32 s, and inter-token latency is 9.6 ms.

What does SmolLM2 1.7B Instruct on Mac M4 Max (128GB) cost per million tokens?

— per 1M output tokens at 1× Mac M4 Max (128GB). 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 M4 Max (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 Mac M4 Max (128GB) 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