Can Gemmasutra Mini 2B v1 run on MacBook Pro M2 Max 96GB?

YES — Runs Great

C42Usable

Estimated from fit model

Gemmasutra Mini 2B v1 needs ~12.7 GB VRAM. MacBook Pro M2 Max 96GB has 69.1 GB. With Q4_K_M quantization, expect ~28 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: 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) — 12.7 GB, 28.0 tok/s, Runs well

12.7 GB required69.1 GB available

18% VRAM used

Fit status

Runs well

Decode

28.0 tok/s

TTFT

6914 ms

Safe context

3.9M

Memory

12.7 GB / 69.1 GB

Memory breakdown

Weights1.2 GB

KV Cache0.2 GB

Runtime0.9 GB

Headroom10.4 GB

See how fast it feels

See how fast it feelsGemmasutra Mini 2B v1 on MacBook Pro M2 Max 96GB
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: 28.0 tok/s decode · 6.9s TTFT (warm) · 70 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	28.0 tok/s	3771 ms	3.9M
Coding	C	Runs well	28.0 tok/s	6914 ms	3.9M
Agentic Coding	C	Runs well	28.0 tok/s	10057 ms	3.9M
Reasoning	C	Runs well	28.0 tok/s	8171 ms	3.9M
RAG	C	Runs well	28.0 tok/s	12571 ms	3.9M

Quantization options

How Gemmasutra Mini 2B v1 (2B params) fits at each quantization level on MacBook Pro M2 Max 96GB (69.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.8 GB	Low	C40
Q3_K_S	3	1.0 GB	Low	C40
NVFP4	4	1.1 GB	Medium	C40
Q4_K_M	4	1.2 GB	Medium	C40
Q5_K_M	5	1.4 GB	High	C40
Q6_K	6	1.6 GB	High	C40
Q8_0	8	2.1 GB	Very High	C40
F16Best for your GPU	16	4.1 GB	Maximum	C40

Get started

Copy-paste commands to run Gemmasutra Mini 2B v1 on your machine.

Run

lms load hf-thedrummer--gemmasutra-mini-2b-v1-gguf && lms server start

Frequently asked questions

Can MacBook Pro M2 Max 96GB run Gemmasutra Mini 2B v1?

Yes, MacBook Pro M2 Max 96GB can run Gemmasutra Mini 2B v1 with a C grade (Runs well). Expected decode speed: 28.0 tok/s.

How much VRAM does Gemmasutra Mini 2B v1 need?

Gemmasutra Mini 2B v1 (2B parameters) requires approximately 12.7 GB of memory with Q4_K_M quantization.

What is the best quantization for Gemmasutra Mini 2B v1?

The recommended quantization for Gemmasutra Mini 2B v1 is Q4_K_M, which balances quality and memory efficiency.

What speed will Gemmasutra Mini 2B v1 run at on MacBook Pro M2 Max 96GB?

On MacBook Pro M2 Max 96GB, Gemmasutra Mini 2B v1 achieves approximately 28.0 tokens per second decode speed with a time-to-first-token of 6914ms using Q4_K_M quantization.

Can MacBook Pro M2 Max 96GB run Gemmasutra Mini 2B v1 for coding?

For coding workloads, Gemmasutra Mini 2B v1 on MacBook Pro M2 Max 96GB receives a C grade with 28.0 tok/s and 3.9M context.

What context window can Gemmasutra Mini 2B v1 use on MacBook Pro M2 Max 96GB?

On MacBook Pro M2 Max 96GB, Gemmasutra Mini 2B v1 can safely use up to 3.9M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M2 Max 96GB as fast as VRAM for Gemmasutra Mini 2B v1?

Not always. MacBook Pro M2 Max 96GB 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.

See all results for MacBook Pro M2 Max 96GB See all hardware for Gemmasutra Mini 2B v1

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