Apple192 GB VRAM800 GB/s$0/hr

GPT-OSS 120B (MoE) on Mac M2 Ultra (192GB unified): Does It Fit, How Fast, What It Costs

GPT-OSS 120B (MoE) at AWQ 4-bit needs 64.3 GB for its weights and 66.7 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
66.7 GB needed / 192 GB available
Decode speed
163.1
tok/s per stream
Aggregate
1324
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 GB66.7 GB163.113246.23 s—✓
2× Mac384 GB66.7 GB277.352943.13 s—✓
4× Mac768 GB66.7 GB554.7211761.58 s—✓
8× Mac1536 GB66.7 GB110984706804.6 ms—✓
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?
4K820309.2 MB✓ fits
8K416618.5 MB✓ fits
33K1052.42 GB✓ fits
66K524.83 GB✓ fits
131K269.66 GB✓ fits
Other GPUs that run GPT-OSS 120B (MoE)
Same workload, same quantization — ranked by value (tokens per dollar-hour).
GPUGPUsAggregate tok/sCost / 1M tok
RTX 5090 32GB4×59638$0.009316
AMD MI300X 192GB1×12969$0.0535
L40S 48GB2×7188$0.0541
AMD MI325X 288GB1×14682$0.0662
A100 80GB SXM41×4894$0.0681
Intel Gaudi 3 128GB1×8075$0.0688
Frequently asked questions

Can GPT-OSS 120B (MoE) run on Mac M2 Ultra (192GB unified)?

Yes. GPT-OSS 120B (MoE) at AWQ 4-bit needs 66.7 GB including the KV cache, and Mac M2 Ultra (192GB unified) has 192 GB — so it fits on a single GPU.

How fast is GPT-OSS 120B (MoE) on Mac M2 Ultra (192GB unified)?

163.1 tokens/sec per stream and 1324 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 6.23 s, and inter-token latency is 6.1 ms.

What does GPT-OSS 120B (MoE) 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 GPT-OSS 120B (MoE) need on Mac M2 Ultra (192GB unified)?

64.3 GB for the weights at AWQ 4-bit, plus 2.5 GB of KV cache for 8 concurrent requests at 8K context — 66.7 GB total. Every extra concurrent user at long context costs more, so leave headroom.

Is Mac M2 Ultra (192GB unified) the right choice for GPT-OSS 120B (MoE)?

It works. Compare — per 1M tokens against the alternatives below — the cheapest option that fits the same workload is 8× RTX 3080 10GB at $0.004393, and the fastest is 2× Cerebras CS-3 (WSE-3) at 166400 tok/s.

Adjust the workload yourself

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

Open in calculator