Apple192 GB VRAM800 GB/s$0/hr

Llama 4 Scout 17B-16E (MoE, VLM) on Mac M2 Ultra (192GB unified): Does It Fit, How Fast, What It Costs

Llama 4 Scout 17B-16E (MoE, VLM) at AWQ 4-bit needs 60.0 GB for its weights and 72.8 GB in total at 8K context with 8 concurrent users. That means it fits on a single Mac M2 Ultra (192GB unified). Every figure is computed by tokcalc's open-source capacity model.

Fits?
Yes, 1 GPU
72.8 GB needed / 192 GB available
Decode speed
48.94
tok/s per stream
Aggregate
397.1
tok/s @ 8 concurrent
Cost / 1M tokens
—
1× Mac M2 Ultra (192GB unified) · quote required
What changes as you add Mac M2 Ultra (192GB unified)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 GB72.8 GB48.94397.120.68 s—✓
2× Mac384 GB72.8 GB83.20158810.36 s—✓
4× Mac768 GB72.8 GB166.463535.19 s—✓
8× Mac1536 GB72.8 GB332.8254122.61 s—✓
Concurrency vs context length
On 1× Mac M2 Ultra (192GB unified) at AWQ 4-bit. KV cache scales linearly with context, so this is the number of users your GPUs can actually serve.
ContextConcurrent usersKV per requestFits?
4K163824.6 MB✓ fits
8K811.61 GB✓ fits
33K206.44 GB✓ fits
66K1012.88 GB✓ fits
131K525.77 GB✓ fits
Other GPUs that run Llama 4 Scout 17B-16E (MoE, VLM)
Same workload, same quantization — ranked by value (tokens per dollar-hour).
GPUGPUsAggregate tok/sCost / 1M tok
RTX 5090 32GB4×17891$0.0311
AMD MI300X 192GB1×3891$0.1785
L40S 48GB2×2157$0.1803
AMD MI325X 288GB1×4405$0.2207
A100 80GB SXM41×1468$0.2270
Intel Gaudi 3 128GB1×2423$0.2293
Frequently asked questions

Can Llama 4 Scout 17B-16E (MoE, VLM) run on Mac M2 Ultra (192GB unified)?

Yes. Llama 4 Scout 17B-16E (MoE, VLM) at AWQ 4-bit needs 72.8 GB including the KV cache, and Mac M2 Ultra (192GB unified) has 192 GB — so it fits on a single GPU.

How fast is Llama 4 Scout 17B-16E (MoE, VLM) on Mac M2 Ultra (192GB unified)?

48.94 tokens/sec per stream and 397.1 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 20.68 s, and inter-token latency is 20.4 ms.

What does Llama 4 Scout 17B-16E (MoE, VLM) on Mac M2 Ultra (192GB unified) cost per million tokens?

— per 1M output tokens at 1× Mac M2 Ultra (192GB unified). That is the marginal hardware cost — add idle capacity, ops, and egress for the real figure.

How much VRAM does Llama 4 Scout 17B-16E (MoE, VLM) need on Mac M2 Ultra (192GB unified)?

60.0 GB for the weights at AWQ 4-bit, plus 12.9 GB of KV cache for 8 concurrent requests at 8K context — 72.8 GB total. Every extra concurrent user at long context costs more, so leave headroom.

Is Mac M2 Ultra (192GB unified) the right choice for Llama 4 Scout 17B-16E (MoE, VLM)?

It works. Compare — per 1M tokens against the alternatives below — the cheapest option that fits the same workload is 8× RTX 3080 10GB at $0.0146, and the fastest is 2× Cerebras CS-3 (WSE-3) at 49920 tok/s.

Adjust the workload yourself

Same formulas, your context length, concurrency, and quantisation.

Open in calculator