Qwen 3.5 122B-A10B runs at 43 tok/s (42.9 as recorded) on a 128 GB M5 Max, a community run reported by github.com (llama.cpp, Q4_K_M, ~64-token prompt, 512 generated). It needs about 65 GB of unified memory. It is Alibaba's 122B mixture-of-experts model (10B active), released under the Apache 2.0 licence.
Want to run Qwen 3.5 122B-A10B faster — or step up to bigger models? Find your Mac →This is a community run: a real run, reported by its source and linked below. The index records it as given and never adjusts it.
| Metric | Value |
|---|---|
| Tokens per second | community43 tok/s |
| Provenance | community run · github.com |
| Quantization | Q4_K_M |
| Engine | llama.cpp |
| Context | ~64-token prompt, 512 generated |
| Figure dated | 2026-04-05 |
| Minimum RAM | 65 GB |
| Parameters | 122B MoE (10B active) |
| Chip bandwidth | 614 GB/s |
Each row as the index holds it, with its source. “Mac RAM” is the memory of the Mac a run used, not the model’s requirement (65 GB). The headline figure follows the reference rule: the lower-median measured plain-decode run at 5 bits or fewer (a run whose quant was not stated counts only when no stated-quant run qualifies), else the estimate; a run marked “not used to anchor estimates” is listed here but never becomes the headline.
| Speed | Quant | Engine | Mac RAM | Context | Source | Note |
|---|---|---|---|---|---|---|
| community42.9 tok/s | Q4_K_M | llama.cpp | 128 GB | ~64-token prompt, 512 generated | github.com | MacBook Pro 14, 40-core GPU. llama.cpp b8500 llama-server, unsloth Q4_K_M, median of 5; other sessions on the same machine gave 38.5–39.4. |
Ollama — install it (brew install ollama or the app from ollama.com), then pull the model by its tag, checked on ollama.com on 2026-10-24:
MLX — pick a build from Hugging Face’s MLX listings, then run it with its repo id:
Downloads: Search the Ollama library → · model card →
The 43 tok/s figure above came from llama.cpp at Q4_K_M; other runtimes and quants run at different speeds.
Prefer a GUI? LM Studio downloads GGUF and MLX builds in one click. Step by step: install Ollama on a Mac.
Alibaba's Apache 2.0 122B MoE with 10B active (Feb 2026): Gated DeltaNet hybrid, text/image/video input, 262K context. SWE-bench Verified 72.0, GPQA Diamond 86.6, MMLU-Pro 86.7 (model card). About 65 GB at 4-bit, so it needs a 96 GB+ Mac. A community llama.cpp run measured 42.9 tok/s on an M5 Max (Q4_K_M, median of 5); BGR's review measured ~80 on an M5 Ultra and ~60 on an M3 Ultra (quantization not stated).
Apache 2.0 licence · 122B MoE · 262K context · LLMCheck capability 38/50 · see its leaderboard rank →
Qwen 3.5 122B-A10B needs about 65 GB of unified memory. These current Apple Silicon Macs have the headroom to run it comfortably:
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