Can solar finalised finetuned Model 10.7B i1 run on MacBook Pro M4 Pro 64GB?

YES — Runs Great

C45Usable

▲Estimated — low-sample bucket· few comparable runs

solar finalised finetuned Model 10.7B i1 needs ~15.6 GB VRAM. MacBook Pro M4 Pro 64GB has 46.1 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) — 15.6 GB, 28.3 tok/s, Runs well

15.6 GB required46.1 GB available

34% VRAM used

Fit status

Runs well

Decode

28.3 tok/s

TTFT

6831 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 MacBook Pro M4 Pro 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: 28.3 tok/s decode · 6.8s TTFT (warm) · 71 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	28.3 tok/s	3726 ms	405K
Coding	C	Runs well	28.3 tok/s	6831 ms	405K
Agentic Coding	C	Runs well	28.3 tok/s	9936 ms	405K
Reasoning	C	Runs well	28.3 tok/s	8073 ms	405K
RAG	C	Runs well	28.3 tok/s	12420 ms	405K

Quantization options

How solar finalised finetuned Model 10.7B i1 (10.699999809265137B params) fits at each quantization level on MacBook Pro M4 Pro 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	6.0 GB	Medium	C42
Q4_K_M	4	6.5 GB	Medium	C42
Q5_K_M	5	7.7 GB	High	C42
Q6_K	6	8.8 GB	High	C42
Q8_0	8	11.4 GB	Very High	C43
F16Best for your GPU	16	21.9 GB	Maximum	C47