Will It Run AI

Can GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV run on MacBook Pro M4 Pro 48GB?

YES — Runs Great

C47Usable
Estimated — low-sample bucket· few comparable runs

GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV needs ~16.3 GB VRAM. MacBook Pro M4 Pro 48GB has 34.6 GB. With Q4_K_M quantization, expect ~22 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: 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) 16.3 GB, 21.7 tok/s, Runs well
16.3 GB required34.6 GB available
47% VRAM used

Fit status

Runs well

Decode

21.7 tok/s

TTFT

8938 ms

Safe context

194K

Memory

16.3 GB / 34.6 GB

Memory breakdown

Weights8.5 GB
KV Cache1.6 GB
Runtime0.9 GB
Headroom5.2 GB

See how fast it feels

See how fast it feelsGGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV on MacBook Pro M4 Pro 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: 21.7 tok/s decode · 8.9s TTFT (warm) · 54 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 well21.7 tok/s4875 ms194K
CodingCRuns well21.7 tok/s8938 ms194K
Agentic CodingCRuns well21.7 tok/s13000 ms194K
ReasoningCRuns well21.7 tok/s10563 ms194K
RAGCRuns well21.7 tok/s16250 ms194K

Quantization options

How GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV (14B params) fits at each quantization level on MacBook Pro M4 Pro 48GB (34.6 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
5.5 GB
LowC43
Q3_K_S
3
6.9 GB
LowC44
NVFP4
4
7.8 GB
MediumC44
Q4_K_M
4
8.5 GB
MediumC44
Q5_K_M
5
10.1 GB
HighC45
Q6_K
6
11.5 GB
HighC45
Q8_0
8
15.0 GB
Very HighC47
F16Best for your GPU
16
28.7 GB
MaximumC48

Get started

Copy-paste commands to run GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV on your machine.

Run

lms load hf-srs6901--gguf-solarized-granistral-14b-2102-yeam-hct-32qkv && lms server start

Opciones de mejora

Hardware que ejecuta bien GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV

Frequently asked questions

Can MacBook Pro M4 Pro 48GB run GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV?

Yes, MacBook Pro M4 Pro 48GB can run GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV with a C grade (Runs well). Expected decode speed: 21.7 tok/s.

How much VRAM does GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV need?

GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV (14B parameters) requires approximately 16.3 GB of memory with Q4_K_M quantization.

What is the best quantization for GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV?

The recommended quantization for GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV is Q4_K_M, which balances quality and memory efficiency.

What speed will GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV run at on MacBook Pro M4 Pro 48GB?

On MacBook Pro M4 Pro 48GB, GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV achieves approximately 21.7 tokens per second decode speed with a time-to-first-token of 8938ms using Q4_K_M quantization.

Can MacBook Pro M4 Pro 48GB run GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV for coding?

For coding workloads, GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV on MacBook Pro M4 Pro 48GB receives a C grade with 21.7 tok/s and 194K context.

What context window can GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV use on MacBook Pro M4 Pro 48GB?

On MacBook Pro M4 Pro 48GB, GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV can safely use up to 194K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M4 Pro 48GB as fast as VRAM for GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV?

Not always. MacBook Pro M4 Pro 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 Pro 48GBSee all hardware for GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV
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