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Llama 3.3 70B on Apple Silicon

Llama 3.3 70B is Meta's highest-capability open-weight model in the 70B class — and one of the most requested on Apple Silicon. This guide covers RAM requirements, measured performance, and which Mac configurations can run it without swapping to disk.

⚠ Limited data note: We currently have benchmark data for one M4 Max configuration on Llama 3.3 70B. Broader chip coverage is in the research queue. Check back as we expand the dataset, or view the benchmarks table →

RAM requirements

A 70B parameter model needs significantly more RAM than 8B or 14B models:

RAM figures are model weights only. Add 4–6 GB for OS/runtime overhead. Long-context workloads add more for KV cache (~128 KB per token per layer). With 70B at Q4, plan for ~42–44 GB total with normal context lengths.

The 64 GB tier — available on M2 Max, M4 Max, M1 Ultra, M2 Ultra, M3 Ultra, and Mac Studio models — is the minimum comfortable configuration for Q4 70B. Anything under 64 GB will use slow disk swap for 70B inference, which reduces performance dramatically.

Measured performance: M4 Max

Our single Llama 3.3 70B benchmark covers an M4 Max configuration at Q5_K_M:

7.1 tok/s at Q5 is solid for a 70B model. This feels conversational for reading responses — about one sentence every 2–3 seconds. Not fast enough for rapid iteration, but fine for thorough research or complex reasoning tasks where output quality matters more than speed.

Note: Q4_K_M (slightly smaller than Q5) would run marginally faster — estimate ~8–9 tok/s on the same M4 Max hardware based on the lower memory bandwidth requirement. Q8 would roughly halve this to ~4 tok/s.

Expected performance by chip tier

While we're actively collecting more 70B benchmark data, the following speed estimates are derived from the bandwidth-to-throughput relationship observed across 8B and 14B measurements. These are estimates, not measured benchmarks — actual results will vary.

These estimates scale the 8B-to-70B throughput ratio (~8× slower than 8B) adjusted for each chip's bandwidth. They are directionally accurate but should not be taken as precise benchmarks. We'll replace these with measured data as it becomes available.

Is Llama 3.3 70B worth running locally?

At 7–18 tok/s depending on chip, Llama 3.3 70B is usable but not fast for local inference. Here's when it makes sense:

Good use cases for local 70B

• Long-form writing and editing — you read the output at 3–4 words/second anyway; 7 tok/s keeps up
• Privacy-sensitive tasks — code review, document analysis, anything that shouldn't leave your machine
• Complex single-shot tasks — you send one prompt, get a long response, then review. Speed matters less than quality.
• Overnight batch tasks — summarizing documents, generating reports. Speed is less important.

Better alternatives if speed matters

• Qwen 3 30B A3B (MoE): 92 tok/s on M4 Max 64 GB at Q4 — ~13× faster than 70B dense, similar intelligence for many tasks. See Qwen 3 guide →
• Llama 3.1 8B Q8: Very fast (40–55 tok/s on M4 Max), smaller but capable for most daily tasks.
• Qwen 3 32B dense: 22 tok/s on M4 Max 64 GB Q4 — a good middle ground between quality and speed.

Which Mac to buy for 70B

• Minimum usable: Any Mac with 64 GB RAM — M1 Max 64 GB, M2 Max 96 GB, M4 Pro 64 GB, M4 Max 64 GB+, M1/M2/M3 Ultra. You'll get Q4 at ~5–9 tok/s.
• Best value for 70B: M4 Max 64 GB (Mac Studio or MacBook Pro) — fast enough for Q4 70B at ~9–12 tok/s, and the 128 GB option unlocks Q8 70B.
• Best performance: M2 Ultra or M3 Ultra — 128–192 GB RAM, ~13–18 tok/s on Q4 70B. The 192 GB M2 Ultra runs Q8 70B (~74 GB) with context headroom.
• Future-proof: M4 Max 128 GB — runs Q8 70B today, will run whatever 70B-class models appear in the next 2–3 years.

See the full guide on RAM requirements: Minimum RAM for 70B models →

See also