Can solar finalised finetuned Model 10.7B i1 run on Mac Studio M2 Ultra 64GB?

YES — Runs Great

C48Usable

○Estimated from fit model

solar finalised finetuned Model 10.7B i1 needs ~15.6 GB VRAM. Mac Studio M2 Ultra 64GB has 46.1 GB. With Q4_K_M quantization, expect ~71 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) — 15.6 GB, 71.1 tok/s, Runs well

15.6 GB required46.1 GB available

34% VRAM used

Fit status

Runs well

Decode

71.1 tok/s

TTFT

2723 ms

Safe context

405K

Memory

15.6 GB / 46.1 GB

Memory breakdown

Weights6.5 GB

KV Cache1.3 GB

Runtime0.9 GB

Headroom6.9 GB

See how fast it feels

See how fast it feelssolar finalised finetuned Model 10.7B i1 on Mac Studio M2 Ultra 64GB
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: 71.1 tok/s decode · 2.7s TTFT (warm) · 178 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	71.1 tok/s	1485 ms	405K
Coding	C	Runs well	71.1 tok/s	2723 ms	405K
Agentic Coding	C	Runs well	71.1 tok/s	3961 ms	405K
Reasoning	C	Runs well	71.1 tok/s	3218 ms	405K
RAG	C	Runs well	71.1 tok/s	4952 ms	405K

Quantization options

How solar finalised finetuned Model 10.7B i1 (10.699999809265137B params) fits at each quantization level on Mac Studio M2 Ultra 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.2 GB	Low	C41
Q3_K_S	3	5.2 GB	Low	C42
NVFP4	4

Get started

Copy-paste commands to run solar finalised finetuned Model 10.7B i1 on your machine.

Run

lms load hf-mradermacher--solar-finalised-finetuned-model-10-7b-i1-gguf && lms server start

Frequently asked questions

See all results for Mac Studio M2 Ultra 64GB See all hardware for solar finalised finetuned Model 10.7B i1

URL: https://willitrunai.com/can-run/hf-mradermacher--solar-finalised-finetuned-model-10-7b-i1-gguf-on-m2-ultra-64gb

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