Will It Run AI

Can GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV run on MacBook Pro M1 Max 32GB?

YES — Runs Great

C51Usable
Estimated from fit model

GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV needs ~14.5 GB VRAM. MacBook Pro M1 Max 32GB has 23.0 GB. With Q4_K_M quantization, expect ~26 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) — 14.5 GB, 25.8 tok/s, Runs well
14.5 GB required23.0 GB available
63% VRAM used

Fit status

Runs well

Decode

25.8 tok/s

TTFT

7515 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 M1 Max 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: 25.8 tok/s decode · 7.5s TTFT (warm) · 64 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 well25.8 tok/s4099 ms99K
CodingCRuns well25.8 tok/s7515 ms99K
Agentic CodingCRuns well25.8 tok/s10931 ms99K
ReasoningCRuns well25.8 tok/s8882 ms99K
RAGCRuns well25.8 tok/s13664 ms99K

Quantization options

How GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV (14B params) fits at each quantization level on MacBook Pro M1 Max 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

Upgrade-Optionen

Hardware, die GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV gut ausführt

RX 7900 XTX 24GBGünstige Wahl
960 GB/s (+560)
C
Raises estimated decode speed by about 214%.80.9 tok/s Dekodierung

Raises estimated decode speed by about 214%.

ca. $999 MSRP

NVIDIARTX 3090 24GBBestes Preis-Leistungs-Verhältnis
936 GB/s (+536)
C
Raises estimated decode speed by about 191%.75.2 tok/s Dekodierung

Raises estimated decode speed by about 191%.

ca. $1,499 MSRP

Frequently asked questions

Can MacBook Pro M1 Max 32GB run GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV?

Yes, MacBook Pro M1 Max 32GB can run GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV with a C grade (Runs well). Expected decode speed: 25.8 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 M1 Max 32GB?

On MacBook Pro M1 Max 32GB, GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV achieves approximately 25.8 tokens per second decode speed with a time-to-first-token of 7515ms using Q4_K_M quantization.

Can MacBook Pro M1 Max 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 M1 Max 32GB receives a C grade with 25.8 tok/s and 99K context.

What context window can GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV use on MacBook Pro M1 Max 32GB?

On MacBook Pro M1 Max 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 M1 Max 32GB as fast as VRAM for GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV?

Not always. MacBook Pro M1 Max 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 M1 Max 32GBSee all hardware for GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV
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