Will It Run AI

Can GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV run on Mac Studio M3 Ultra 96GB?

YES — Runs Great

C47Usable
Estimated from fit model

GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV needs ~21.4 GB VRAM. Mac Studio M3 Ultra 96GB has 69.1 GB. With Q4_K_M quantization, expect ~65 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: 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) — 21.4 GB, 65.2 tok/s, Runs well
21.4 GB required69.1 GB available
31% VRAM used

Fit status

Runs well

Decode

65.2 tok/s

TTFT

2969 ms

Safe context

481K

Memory

21.4 GB / 69.1 GB

Memory breakdown

Weights8.5 GB
KV Cache1.6 GB
Runtime0.9 GB
Headroom10.4 GB

See how fast it feels

See how fast it feelsGGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV on Mac Studio M3 Ultra 96GB
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: 65.2 tok/s decode · 3.0s TTFT (warm) · 163 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 well65.2 tok/s1619 ms481K
CodingCRuns well65.2 tok/s2969 ms481K
Agentic CodingCRuns well65.2 tok/s4318 ms481K
ReasoningCRuns well65.2 tok/s3508 ms481K
RAGCRuns well65.2 tok/s5398 ms481K

Quantization options

How GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV (14B params) fits at each quantization level on Mac Studio M3 Ultra 96GB (69.1 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
5.5 GB
LowD40
Q3_K_S
3
6.9 GB
LowC40
NVFP4
4
7.8 GB
MediumC40
Q4_K_M
4
8.5 GB
MediumC40
Q5_K_M
5
10.1 GB
HighC41
Q6_K
6
11.5 GB
HighC41
Q8_0
8
15.0 GB
Very HighC41
F16Best for your GPU
16
28.7 GB
MaximumC44

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

Frequently asked questions

Can Mac Studio M3 Ultra 96GB run GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV?

Yes, Mac Studio M3 Ultra 96GB can run GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV with a C grade (Runs well). Expected decode speed: 65.2 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 21.4 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 Studio M3 Ultra 96GB?

On Mac Studio M3 Ultra 96GB, GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV achieves approximately 65.2 tokens per second decode speed with a time-to-first-token of 2969ms using Q4_K_M quantization.

Can Mac Studio M3 Ultra 96GB run GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV for coding?

For coding workloads, GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV on Mac Studio M3 Ultra 96GB receives a C grade with 65.2 tok/s and 481K context.

What context window can GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV use on Mac Studio M3 Ultra 96GB?

On Mac Studio M3 Ultra 96GB, GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV can safely use up to 481K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on Mac Studio M3 Ultra 96GB as fast as VRAM for GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV?

Not always. Mac Studio M3 Ultra 96GB 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 Studio M3 Ultra 96GBSee all hardware for GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV
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