Will It Run AI

Can GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV run on Mac mini M4 64GB?

YES — Runs Great

C43Usable
Estimated — low-sample bucket· few comparable runs

GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV needs ~18.0 GB VRAM. Mac mini M4 64GB has 46.1 GB. With Q4_K_M quantization, expect ~9 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth
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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) — 18.0 GB, 8.9 tok/s, Runs well
18.0 GB required46.1 GB available
39% VRAM used

Fit status

Runs well

Decode

8.9 tok/s

TTFT

21745 ms

Safe context

290K

Memory

18.0 GB / 46.1 GB

Memory breakdown

Weights8.5 GB
KV Cache1.6 GB
Runtime0.9 GB
Headroom6.9 GB

See how fast it feels

See how fast it feelsGGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV on Mac mini M4 64GB
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 ms290K
CodingCRuns well8.9 tok/s21745 ms290K
Agentic CodingCRuns well8.9 tok/s31630 ms290K
ReasoningCRuns well8.9 tok/s25699 ms290K
RAGCRuns well8.9 tok/s39537 ms290K

Quantization options

How GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV (14B params) fits at each quantization level on Mac mini M4 64GB (46.1 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
5.5 GB
LowC42
Q3_K_S
3
6.9 GB
LowC42
NVFP4
4
7.8 GB
MediumC42
Q4_K_M
4
8.5 GB
MediumC42
Q5_K_M
5
10.1 GB
HighC43
Q6_K
6
11.5 GB
HighC43
Q8_0
8
15.0 GB
Very HighC44
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

升级选项

能流畅运行 GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV 的硬件

MacBook Pro M4 Max 96GB经济之选
96 GB Unified (+32)546 GB/s (+426)
C
Raises estimated decode speed by about 298%.35.4 tok/s 解码

Raises estimated decode speed by about 298%.

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

~$2,499 MSRP

MacBook Pro M3 Max 128GB最佳性价比
128 GB Unified (+64)400 GB/s (+280)
C
Raises estimated decode speed by about 216%.28.1 tok/s 解码

Raises estimated decode speed by about 216%.

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

~$2,499 MSRP

Mac Studio M3 Ultra 96GBApple 升级
96 GB Unified (+32)819 GB/s (+699)
C
Raises estimated decode speed by about 633%.65.2 tok/s 解码

Raises estimated decode speed by about 633%.

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

~$3,999 MSRP

Frequently asked questions

Can Mac mini M4 64GB run GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV?

Yes, Mac mini M4 64GB 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 18.0 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 Mac mini M4 64GB?

On Mac mini M4 64GB, 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 Mac mini M4 64GB run GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV for coding?

For coding workloads, GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV on Mac mini M4 64GB receives a C grade with 8.9 tok/s and 290K context.

What context window can GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV use on Mac mini M4 64GB?

On Mac mini M4 64GB, GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV can safely use up to 290K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on Mac mini M4 64GB as fast as VRAM for GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV?

Not always. Mac mini M4 64GB 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 Mac mini M4 64GBSee all hardware for GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV
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