Apple128 GB VRAM546 GB/s$0/hr

Llama 3 8B on Mac M4 Max (128GB): 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 M4 Max (128GB). Every figure is computed by tokcalc's open-source capacity model.

Fits?
Yes, 1 GPU
24.6 GB needed / 128 GB available
Decode speed
22.10
tok/s per stream
Aggregate
265.2
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 GB24.6 GB22.10265.215.55 s—✓
2× Mac256 GB24.6 GB37.57901.67.80 s—✓
4× Mac512 GB24.6 GB75.1336063.91 s—✓
8× Mac1024 GB24.6 GB150.3144261.97 s—✓
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?
4K208549.8 MB✓ fits
8K1041.07 GB✓ fits
33K264.29 GB✓ fits
66K138.59 GB✓ fits
131K617.18 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 M4 Max (128GB)?

Yes. Llama 3 8B at FP16 needs 24.6 GB including the KV cache, and Mac M4 Max (128GB) has 128 GB — so it fits on a single GPU.

How fast is Llama 3 8B on Mac M4 Max (128GB)?

22.10 tokens/sec per stream and 265.2 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 15.55 s, and inter-token latency is 45.3 ms.

What does Llama 3 8B 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 Llama 3 8B need on Mac M4 Max (128GB)?

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 M4 Max (128GB) 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