Is unified memory on Mac Studio M2 Ultra 128GB as fast as VRAM for openchat 3.6 8b 20240522 IMat?

Not always. Mac Studio M2 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 openchat 3.6 8b 20240522 IMat run on Mac Studio M2 Ultra 128GB?

YES — Runs Great

C46Usable

Estimated from fit model

openchat 3.6 8b 20240522 IMat needs ~20.5 GB VRAM. Mac Studio M2 Ultra 128GB has 92.2 GB. With Q4_K_M quantization, expect ~95 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: Balanced

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

Q4_K_M (Medium quality) — 20.5 GB, 95.1 tok/s, Runs well

20.5 GB required92.2 GB available

22% VRAM used

Fit status

Runs well

Decode

95.1 tok/s

TTFT

2036 ms

Safe context

1.2M

Memory

20.5 GB / 92.2 GB

Memory breakdown

Weights4.9 GB

KV Cache0.9 GB

Runtime0.9 GB

Headroom13.8 GB

See how fast it feels

See how fast it feelsopenchat 3.6 8b 20240522 IMat on Mac Studio M2 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: 95.1 tok/s decode · 2.0s TTFT (warm) · 238 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

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

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	95.1 tok/s	1111 ms	1.2M
Coding	C	Runs well	95.1 tok/s	2036 ms	1.2M
Agentic Coding	C	Runs well	95.1 tok/s	2962 ms	1.2M
Reasoning	C	Runs well	95.1 tok/s	2406 ms	1.2M
RAG	C	Runs well	95.1 tok/s	3702 ms	1.2M

Inference speed

openchat 3.6 8b 20240522 IMat inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for openchat 3.6 8b 20240522 IMat at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~112 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	112.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	112.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	112.0

Quantization options

How openchat 3.6 8b 20240522 IMat (8B params) fits at each quantization level on Mac Studio M2 Ultra 128GB (92.2 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	D39
Q3_K_S	3	3.9 GB	Low	D39
NVFP4	4

Get started

Copy-paste commands to run openchat 3.6 8b 20240522 IMat on your machine.

Run

lms load hf-legraphista--openchat-3-6-8b-20240522-imat-gguf && lms server start

Frequently asked questions

Can Mac Studio M2 Ultra 128GB run openchat 3.6 8b 20240522 IMat?

Yes, Mac Studio M2 Ultra 128GB can run openchat 3.6 8b 20240522 IMat with a C grade (Runs well). Expected decode speed: 95.1 tok/s.

How much VRAM does openchat 3.6 8b 20240522 IMat need?

openchat 3.6 8b 20240522 IMat (8B parameters) requires approximately 20.5 GB of memory with Q4_K_M quantization.

What is the best quantization for openchat 3.6 8b 20240522 IMat?

The recommended quantization for openchat 3.6 8b 20240522 IMat is Q4_K_M, which balances quality and memory efficiency.

What speed will openchat 3.6 8b 20240522 IMat run at on Mac Studio M2 Ultra 128GB?

On Mac Studio M2 Ultra 128GB, openchat 3.6 8b 20240522 IMat achieves approximately 95.1 tokens per second decode speed with a time-to-first-token of 2036ms using Q4_K_M quantization.

Can Mac Studio M2 Ultra 128GB run openchat 3.6 8b 20240522 IMat for coding?

For coding workloads, openchat 3.6 8b 20240522 IMat on Mac Studio M2 Ultra 128GB receives a C grade with 95.1 tok/s and 1.2M context.

What context window can openchat 3.6 8b 20240522 IMat use on Mac Studio M2 Ultra 128GB?

On Mac Studio M2 Ultra 128GB, openchat 3.6 8b 20240522 IMat can safely use up to 1.2M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Mac Studio M2 Ultra 128GB See all hardware for openchat 3.6 8b 20240522 IMat

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<iframe src="https://willitrunai.com/embed/hf-legraphista--openchat-3-6-8b-20240522-imat-gguf-on-m2-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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