Can SOLAR 10.7B Instruct v1.0 uncensored run on MacBook Pro M4 Max 48GB?

YES — Runs Great

C48Usable
Estimated from fit model

SOLAR 10.7B Instruct v1.0 uncensored needs ~13.9 GB VRAM. MacBook Pro M4 Max 48GB has 34.6 GB. With Q4_K_M quantization, expect ~46 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) 13.9 GB, 46.4 tok/s, Runs well
13.9 GB required34.6 GB available
40% VRAM used

Fit status

Runs well

Decode

46.4 tok/s

TTFT

4174 ms

Safe context

280K

Memory

13.9 GB / 34.6 GB

Memory breakdown

Weights6.5 GB
KV Cache1.3 GB
Runtime0.9 GB
Headroom5.2 GB

See how fast it feels

See how fast it feelsSOLAR 10.7B Instruct v1.0 uncensored on MacBook Pro M4 Max 48GB
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 ms280K
CodingCRuns well46.4 tok/s4174 ms280K
Agentic CodingCRuns well46.4 tok/s6072 ms280K
ReasoningCRuns well46.4 tok/s4933 ms280K
RAGCRuns well46.4 tok/s7590 ms280K

Quantization options

How SOLAR 10.7B Instruct v1.0 uncensored (10.699999809265137B params) fits at each quantization level on MacBook Pro M4 Max 48GB (34.6 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
4.2 GB
LowC43
Q3_K_S
3
5.2 GB
LowC43
NVFP4
4
6.0 GB
MediumC44
Q4_K_M
4
6.5 GB
MediumC44
Q5_K_M
5
7.7 GB
HighC44
Q6_K
6
8.8 GB
HighC45
Q8_0
8
11.4 GB
Very HighC46
F16Best for your GPU
16
21.9 GB
MaximumC49

Get started

Copy-paste commands to run SOLAR 10.7B Instruct v1.0 uncensored on your machine.

Run

lms load hf-thebloke--solar-10-7b-instruct-v1-0-uncensored-gguf && lms server start

Upgrade-Optionen

Hardware, die SOLAR 10.7B Instruct v1.0 uncensored gut ausführt

Frequently asked questions

Can MacBook Pro M4 Max 48GB run SOLAR 10.7B Instruct v1.0 uncensored?

Yes, MacBook Pro M4 Max 48GB can run SOLAR 10.7B Instruct v1.0 uncensored with a C grade (Runs well). Expected decode speed: 46.4 tok/s.

How much VRAM does SOLAR 10.7B Instruct v1.0 uncensored need?

SOLAR 10.7B Instruct v1.0 uncensored (10.699999809265137B parameters) requires approximately 13.9 GB of memory with Q4_K_M quantization.

What is the best quantization for SOLAR 10.7B Instruct v1.0 uncensored?

The recommended quantization for SOLAR 10.7B Instruct v1.0 uncensored is Q4_K_M, which balances quality and memory efficiency.

What speed will SOLAR 10.7B Instruct v1.0 uncensored run at on MacBook Pro M4 Max 48GB?

On MacBook Pro M4 Max 48GB, SOLAR 10.7B Instruct v1.0 uncensored achieves approximately 46.4 tokens per second decode speed with a time-to-first-token of 4174ms using Q4_K_M quantization.

Can MacBook Pro M4 Max 48GB run SOLAR 10.7B Instruct v1.0 uncensored for coding?

For coding workloads, SOLAR 10.7B Instruct v1.0 uncensored on MacBook Pro M4 Max 48GB receives a C grade with 46.4 tok/s and 280K context.

What context window can SOLAR 10.7B Instruct v1.0 uncensored use on MacBook Pro M4 Max 48GB?

On MacBook Pro M4 Max 48GB, SOLAR 10.7B Instruct v1.0 uncensored can safely use up to 280K 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 48GB as fast as VRAM for SOLAR 10.7B Instruct v1.0 uncensored?

Not always. MacBook Pro M4 Max 48GB 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 48GBSee all hardware for SOLAR 10.7B Instruct v1.0 uncensored
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