Is unified memory on Mac Studio M1 Ultra 128GB as fast as VRAM for Qwen3.5 122B A10B?

Not always. Mac Studio M1 Ultra 128GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.

Can Qwen3.5 122B A10B run on Mac Studio M1 Ultra 128GB?

Q: What should I upgrade first if Qwen3.5 122B A10B feels slow on Mac Studio M1 Ultra 128GB?

Prioritize bandwidth, not only capacity. If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

YES — With Offload

C46Usable

Estimated from fit model

Qwen3.5 122B A10B needs ~88.8 GB VRAM. Mac Studio M1 Ultra 128GB has 92.2 GB. With Q3_K_M quantization, expect ~7 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: HighStack: StandardBottleneck: Memory bandwidth

Operating mode

Choose the run profile you care about

Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.

Current mode

Balanced

Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.

Capabilities:

Select quantization to explore

F16 (Maximum quality) — 279.1 GB, exceeds 92.2 GB available

279.1 GB required92.2 GB available

303% VRAM needed

186.9 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.0 tok/s

TTFT

96800 ms

Safe context

Memory

279.1 GB / 92.2 GB

Offload

70%

Memory breakdown

Weights250.1 GB

KV Cache14.3 GB

Runtime0.9 GB

Headroom13.8 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsQwen3.5 122B A10B on Mac Studio M1 Ultra 128GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.

When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.

Key benefits of running locally:
  - Full privacy: your prompts never leave your machine
  - No per-token cost or rate limits
  - Works offline once the model is downloaded
  - Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:

  Model size     Q4 (4-bit)    Q8 (8-bit)    FP16
  7B params      ~4.3 GB       ~7.5 GB       ~14 GB
  13B params     ~7.9 GB       ~13.9 GB      ~26 GB
  70B params     ~42.7 GB      ~74.9 GB      ~140 GB

Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 2.0 tok/s decode · 96.8s TTFT (warm) · 5 tok/s prefill

What limits this setup

The model fits in shared memory, but shared-memory bandwidth is now the real limiter.

Fit does not mean dedicated-VRAM speed

Unified or shared memory can make a model technically fit, but sustained tokens per second may still trail a discrete high-bandwidth GPU with less total memory.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Shared-memory contention still exists

The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.

Best improvement path

Prioritize bandwidth, not only capacity

If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Tight fit	6.8 tok/s	15427 ms	20K
Coding	C	Runs with offload	6.8 tok/s	28283 ms	20K
Agentic Coding	D	Very compromised (needs ~6.3 GB host RAM)	5.7 tok/s	49337 ms	20K
Reasoning	C	Runs with offload	6.8 tok/s	33425 ms	20K
RAG	D	Very compromised (needs ~6.3 GB host RAM)	5.7 tok/s	61671 ms

Quantization options

How Qwen3.5 122B A10B (122B params) fits at each quantization level on Mac Studio M1 Ultra 128GB (92.2 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	47.6 GB	Low	C48
Q3_K_S	3	59.8 GB	Low	C48
NVFP4	4

Get started

Copy-paste commands to run Qwen3.5 122B A10B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "unsloth/Qwen3.5-122B-A10B-GGUF" \
  --hf-file "Qwen3.5-122B-A10B-GGUF-Q3_K_M.gguf" \
  -c 4096 -ngl 99

Upgrade options

Hardware that runs Qwen3.5 122B A10B well

Mac Studio M3 Ultra 256GBBudget pick

256 GB Unified (+128)819 GB/s (+19)

Raises estimated decode speed by about 28%.8.7 tok/s decode

Raises estimated decode speed by about 28%.

Adds memory headroom for longer context windows and future model growth.

~$6,999 MSRP

AMD Instinct MI350X 288GBBest value

288 GB VRAM (+160)8000 GB/s (+7200)

Raises estimated decode speed by about 1237%.90.9 tok/s decode

Raises estimated decode speed by about 1237%.

Adds memory headroom for longer context windows and future model growth.

~$8,000 MSRP

AMD Instinct MI300A 128GBBiggest leap

5300 GB/s (+4500)

Raises estimated decode speed by about 749%.57.7 tok/s decode

Raises estimated decode speed by about 749%.

Adds memory headroom for longer context windows and future model growth.

~$12,000 MSRP

Frequently asked questions

Can Mac Studio M1 Ultra 128GB run Qwen3.5 122B A10B?

Yes, Mac Studio M1 Ultra 128GB can run Qwen3.5 122B A10B with a C grade (Runs with offload). Expected decode speed: 6.8 tok/s.

How much VRAM does Qwen3.5 122B A10B need?

Qwen3.5 122B A10B (122B parameters) requires approximately 88.8 GB of memory with Q3_K_M quantization.

What is the best quantization for Qwen3.5 122B A10B?

The recommended quantization for Qwen3.5 122B A10B is Q3_K_M, which balances quality and memory efficiency.

What speed will Qwen3.5 122B A10B run at on Mac Studio M1 Ultra 128GB?

On Mac Studio M1 Ultra 128GB, Qwen3.5 122B A10B achieves approximately 6.8 tokens per second decode speed with a time-to-first-token of 28283ms using Q3_K_M quantization.

Can Mac Studio M1 Ultra 128GB run Qwen3.5 122B A10B for coding?

For coding workloads, Qwen3.5 122B A10B on Mac Studio M1 Ultra 128GB receives a C grade with 6.8 tok/s and 20K context.

What context window can Qwen3.5 122B A10B use on Mac Studio M1 Ultra 128GB?

On Mac Studio M1 Ultra 128GB, Qwen3.5 122B A10B can safely use up to 20K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Mac Studio M1 Ultra 128GB See all hardware for Qwen3.5 122B A10B

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<iframe src="https://willitrunai.com/embed/hf-unsloth--qwen3-5-122b-a10b-gguf-on-m1-ultra-128gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

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