Apple192 GB VRAM800 GB/s$0/hr

BGE-M3 (embed, 1024-dim) on Mac M2 Ultra (192GB unified): Does It Fit, How Fast, What It Costs

BGE-M3 (embed, 1024-dim) at FP16 needs 1.1 GB for its weights and 7.6 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
7.6 GB needed / 192 GB available
Decode speed
457.7
tok/s per stream
Aggregate
5493
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 GB7.6 GB457.75493721.5 ms—✓
2× Mac384 GB7.6 GB778.218676376.0 ms—✓
4× Mac768 GB7.6 GB155674704203.0 ms—✓
8× Mac1536 GB7.6 GB3113298817116.5 ms—✓
Concurrency vs context length
On 1× Mac M2 Ultra (192GB unified) at FP16. KV cache scales linearly with context, so this is the number of users your GPUs can actually serve.
ContextConcurrent usersKV per requestFits?
4K474412.3 MB✓ fits
8K237824.6 MB✓ fits
33K593.22 GB✓ fits
66K296.44 GB✓ fits
131K1412.88 GB✓ fits
Other GPUs that run BGE-M3 (embed, 1024-dim)
Same workload, same quantization — ranked by value (tokens per dollar-hour).
GPUGPUsAggregate tok/sCost / 1M tok
RTX 3080 10GB1×5218$0.0106
RTX 3090 24GB1×6427$0.0108
RTX 5090 32GB1×12304$0.0113
RTX 4090 24GB1×6921$0.0161
RTX 4080 16GB1×4923$0.0169
AMD MI300X 192GB1×36391$0.0191
Frequently asked questions

Can BGE-M3 (embed, 1024-dim) run on Mac M2 Ultra (192GB unified)?

Yes. BGE-M3 (embed, 1024-dim) at FP16 needs 7.6 GB including the KV cache, and Mac M2 Ultra (192GB unified) has 192 GB — so it fits on a single GPU.

How fast is BGE-M3 (embed, 1024-dim) on Mac M2 Ultra (192GB unified)?

457.7 tokens/sec per stream and 5493 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 721.5 ms, and inter-token latency is 2.2 ms.

What does BGE-M3 (embed, 1024-dim) 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 BGE-M3 (embed, 1024-dim) need on Mac M2 Ultra (192GB unified)?

1.1 GB for the weights at FP16, plus 6.4 GB of KV cache for 8 concurrent requests at 8K context — 7.6 GB total. Every extra concurrent user at long context costs more, so leave headroom.

Is Mac M2 Ultra (192GB unified) the right choice for BGE-M3 (embed, 1024-dim)?

It works. Compare — per 1M tokens against the alternatives below — the cheapest option that fits the same workload is 1× RTX 3080 10GB at $0.0106, and the fastest is 1× Cerebras CS-3 (WSE-3) at 137324 tok/s.

Adjust the workload yourself

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

Open in calculator