Will It Run AI

Can solar finalised finetuned Model 10.7B i1 run on MacBook Pro M4 Max 96GB?

YES — Runs Great

C46Usable
Estimated from fit model

solar finalised finetuned Model 10.7B i1 needs ~19.0 GB VRAM. MacBook Pro M4 Max 96GB has 69.1 GB. With Q4_K_M quantization, expect ~53 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: StandardBottleneck: Balanced
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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) 19.0 GB, 46.4 tok/s, Runs well
19.0 GB required69.1 GB available
27% VRAM used

Fit status

Runs well

Decode

46.4 tok/s

TTFT

4174 ms

Safe context

655K

Memory

19.0 GB / 69.1 GB

Memory breakdown

Weights6.5 GB
KV Cache1.3 GB
Runtime0.9 GB
Headroom10.4 GB

See how fast it feels

See how fast it feelssolar finalised finetuned Model 10.7B i1 on MacBook Pro M4 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: 46.4 tok/s decode · 4.2s TTFT (warm) · 116 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

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well46.4 tok/s2277 ms655K
CodingCRuns well52.7 tok/s3674 ms655K
Agentic CodingCRuns well46.4 tok/s6072 ms655K
ReasoningCRuns well46.4 tok/s4933 ms655K
RAGCRuns well46.4 tok/s7590 ms655K

Quantization options

How solar finalised finetuned Model 10.7B i1 (10.699999809265137B params) fits at each quantization level on MacBook Pro M4 Max 96GB (69.1 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
4.2 GB
LowD40
Q3_K_S
3
5.2 GB
LowD40
NVFP4
4
6.0 GB
MediumD40
Q4_K_M
4
6.5 GB
MediumC40
Q5_K_M
5
7.7 GB
HighC40
Q6_K
6
8.8 GB
HighC40
Q8_0
8
11.4 GB
Very HighC41
F16Best for your GPU
16
21.9 GB
MaximumC43

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

升级选项

能流畅运行 solar finalised finetuned Model 10.7B i1 的硬件

Frequently asked questions

Can MacBook Pro M4 Max 96GB run solar finalised finetuned Model 10.7B i1?

Yes, MacBook Pro M4 Max 96GB can run solar finalised finetuned Model 10.7B i1 with a C grade (Runs well). Expected decode speed: 52.7 tok/s.

How much VRAM does solar finalised finetuned Model 10.7B i1 need?

solar finalised finetuned Model 10.7B i1 (10.699999809265137B parameters) requires approximately 19.0 GB of memory with Q4_K_M quantization.

What is the best quantization for solar finalised finetuned Model 10.7B i1?

The recommended quantization for solar finalised finetuned Model 10.7B i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will solar finalised finetuned Model 10.7B i1 run at on MacBook Pro M4 Max 96GB?

On MacBook Pro M4 Max 96GB, solar finalised finetuned Model 10.7B i1 achieves approximately 52.7 tokens per second decode speed with a time-to-first-token of 3674ms using Q4_K_M quantization.

Can MacBook Pro M4 Max 96GB run solar finalised finetuned Model 10.7B i1 for coding?

For coding workloads, solar finalised finetuned Model 10.7B i1 on MacBook Pro M4 Max 96GB receives a C grade with 52.7 tok/s and 655K context.

What context window can solar finalised finetuned Model 10.7B i1 use on MacBook Pro M4 Max 96GB?

On MacBook Pro M4 Max 96GB, solar finalised finetuned Model 10.7B i1 can safely use up to 655K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M4 Max 96GB as fast as VRAM for solar finalised finetuned Model 10.7B i1?

Not always. MacBook Pro M4 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 M4 Max 96GBSee all hardware for solar finalised finetuned Model 10.7B i1
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