Apple192 GB VRAM800 GB/s$0/hr

Llama 3 8B on Mac M2 Ultra (192GB): Does It Fit, How Fast, What It Costs

Llama 3 8B at FP16 needs 16.1 GB for its weights and 24.6 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
24.6 GB needed / 192 GB available
Decode speed
32.38
tok/s per stream
Aggregate
388.5
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 GB24.6 GB32.38388.59.81 s—✓
2× Mac384 GB24.6 GB55.0413214.92 s—✓
4× Mac768 GB24.6 GB110.152842.48 s—✓
8× Mac1536 GB24.6 GB220.2211371.25 s—✓
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?
4K327549.8 MB✓ fits
8K1631.07 GB✓ fits
33K404.29 GB✓ fits
66K208.59 GB✓ fits
131K1017.18 GB✓ fits
Other GPUs that run Llama 3 8B
Same workload, same quantization — ranked by value (tokens per dollar-hour).
GPUGPUsAggregate tok/sCost / 1M tok
RTX 5090 32GB1×870.3$0.1596
AMD MI300X 192GB1×2574$0.2698
V100 SXM2 32GB1×437.1$0.3177
A100 40GB SXM41×752.8$0.3321
AMD MI325X 288GB1×2914$0.3336
A100 80GB SXM41×971.4$0.3432
Frequently asked questions

Can Llama 3 8B run on Mac M2 Ultra (192GB)?

Yes. Llama 3 8B at FP16 needs 24.6 GB including the KV cache, and Mac M2 Ultra (192GB) has 192 GB — so it fits on a single GPU.

How fast is Llama 3 8B on Mac M2 Ultra (192GB)?

32.38 tokens/sec per stream and 388.5 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 9.81 s, and inter-token latency is 30.9 ms.

What does Llama 3 8B 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 Llama 3 8B need on Mac M2 Ultra (192GB)?

16.1 GB for the weights at FP16, plus 8.6 GB of KV cache for 8 concurrent requests at 8K context — 24.6 GB total. Every extra concurrent user at long context costs more, so leave headroom.

Is Mac M2 Ultra (192GB) the right choice for Llama 3 8B?

It works. Compare — per 1M tokens against the alternatives below — the cheapest option that fits the same workload is 4× RTX 3080 10GB at $0.0443, and the fastest is 1× Cerebras CS-3 (WSE-3) at 9714 tok/s.

Adjust the workload yourself

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

Open in calculator