Will It Run AI

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

YES — Runs Great

C48Usable
Estimated — low-sample bucket· few comparable runs

GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV needs ~14.5 GB VRAM. MacBook Pro M4 32GB has 23.0 GB. With Q4_K_M quantization, expect ~9 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth
Share:

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) — 14.5 GB, 8.9 tok/s, Runs well
14.5 GB required23.0 GB available
63% VRAM used

Fit status

Runs well

Decode

8.9 tok/s

TTFT

21745 ms

Safe context

99K

Memory

14.5 GB / 23.0 GB

Memory breakdown

Weights8.5 GB
KV Cache1.6 GB
Runtime0.9 GB
Headroom3.5 GB

See how fast it feels

See how fast it feelsGGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV on MacBook Pro M4 32GB
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: 8.9 tok/s decode · 21.7s TTFT (warm) · 22 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 well8.9 tok/s11861 ms99K
CodingCRuns well8.9 tok/s21745 ms99K
Agentic CodingCRuns well8.9 tok/s31630 ms99K
ReasoningCRuns well8.9 tok/s25699 ms99K
RAGCRuns well8.9 tok/s39537 ms99K

Quantization options

How GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV (14B params) fits at each quantization level on MacBook Pro M4 32GB (23.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
5.5 GB
LowC46
Q3_K_S
3
6.9 GB
LowC47
NVFP4
4
7.8 GB
MediumC47
Q4_K_M
4
8.5 GB
MediumC48
Q5_K_M
5
10.1 GB
HighC49
Q6_K
6
11.5 GB
HighC50
Q8_0Best for your GPU
8
15.0 GB
Very HighC50
F16
16
28.7 GB
MaximumF0

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

Opções de upgrade

Hardware que roda bem GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV

MacBook Pro M3 Pro 36GBOpção econômica
36 GB Unified (+4)150 GB/s (+30)
C
Raises estimated decode speed by about 44%.12.8 tok/s decodificação

Raises estimated decode speed by about 44%.

~$1,999 MSRP

MacBook Pro M4 Max 36GBMelhor custo-benefício
36 GB Unified (+4)410 GB/s (+290)
C
Raises estimated decode speed by about 239%.30.2 tok/s decodificação

Raises estimated decode speed by about 239%.

~$2,499 MSRP

MacBook Pro M4 Max 48GBUpgrade Apple
48 GB Unified (+16)546 GB/s (+426)
C
Raises estimated decode speed by about 298%.35.4 tok/s decodificação

Raises estimated decode speed by about 298%.

Adds memory headroom for longer context windows and future model growth.

~$2,499 MSRP

Frequently asked questions

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

Yes, MacBook Pro M4 32GB can run GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV with a C grade (Runs well). Expected decode speed: 8.9 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 14.5 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 32GB?

On MacBook Pro M4 32GB, GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV achieves approximately 8.9 tokens per second decode speed with a time-to-first-token of 21745ms using Q4_K_M quantization.

Can MacBook Pro M4 32GB 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 32GB receives a C grade with 8.9 tok/s and 99K context.

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

On MacBook Pro M4 32GB, GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV can safely use up to 99K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

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

Not always. MacBook Pro M4 32GB 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 32GBSee all hardware for GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV
Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/hf-srs6901--gguf-solarized-granistral-14b-2102-yeam-hct-32qkv-on-m4-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: