Can GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV run on MacBook Pro M2 Max 96GB?

YES — Runs Great

C44Usable

Estimated from fit model

GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV needs ~21.4 GB VRAM. MacBook Pro M2 Max 96GB has 69.1 GB. With Q4_K_M quantization, expect ~27 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: StandardBottleneck: Balanced

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, 27.2 tok/s, Runs well

21.4 GB required69.1 GB available

31% VRAM used

Fit status

Runs well

Decode

27.2 tok/s

TTFT

7126 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 MacBook Pro M2 Max 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: 27.2 tok/s decode · 7.1s TTFT (warm) · 68 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

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	27.2 tok/s	3887 ms	481K
Coding	C	Runs well	27.2 tok/s	7126 ms	481K
Agentic Coding	C	Runs well	27.2 tok/s	10366 ms	481K
Reasoning	C	Runs well	27.2 tok/s	8422 ms	481K
RAG	C	Runs well	27.2 tok/s	12957 ms	481K

Quantization options

How GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV (14B params) fits at each quantization level on MacBook Pro M2 Max 96GB (69.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	D40
Q3_K_S	3	6.9 GB	Low	C40
NVFP4	4	7.8 GB	Medium	C40
Q4_K_M	4	8.5 GB	Medium	C40
Q5_K_M	5	10.1 GB	High	C41
Q6_K	6	11.5 GB	High	C41
Q8_0	8	15.0 GB	Very High	C41
F16Best for your GPU	16	28.7 GB	Maximum	C44

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 options

Hardware that runs GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV well

Mac Studio M2 Ultra 128GBBudget pick

128 GB Unified (+32)800 GB/s (+400)

Raises estimated decode speed by about 100%.54.3 tok/s decode

Raises estimated decode speed by about 100%.

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

~$3,999 MSRP

Mac Studio M1 Ultra 128GBBest value

128 GB Unified (+32)800 GB/s (+400)

Raises estimated decode speed by about 89%.51.5 tok/s decode

Raises estimated decode speed by about 89%.

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

~$3,999 MSRP

Mac Studio M3 Ultra 256GBApple upgrade

256 GB Unified (+160)819 GB/s (+419)

Raises estimated decode speed by about 140%.65.2 tok/s decode

Raises estimated decode speed by about 140%.

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

~$6,999 MSRP

Frequently asked questions

Can MacBook Pro M2 Max 96GB run GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV?

Yes, MacBook Pro M2 Max 96GB can run GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV with a C grade (Runs well). Expected decode speed: 27.2 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 MacBook Pro M2 Max 96GB?

On MacBook Pro M2 Max 96GB, GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV achieves approximately 27.2 tokens per second decode speed with a time-to-first-token of 7126ms using Q4_K_M quantization.

Can MacBook Pro M2 Max 96GB 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 M2 Max 96GB receives a C grade with 27.2 tok/s and 481K context.

What context window can GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV use on MacBook Pro M2 Max 96GB?

On MacBook Pro M2 Max 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 MacBook Pro M2 Max 96GB as fast as VRAM for GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV?

Not always. MacBook Pro M2 Max 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 MacBook Pro M2 Max 96GB See all hardware for GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

27.2 tok/s

3887 ms

481K

Coding

Runs well

27.2 tok/s

7126 ms

481K

Agentic Coding

Runs well

27.2 tok/s

10366 ms

481K

Reasoning

Runs well

27.2 tok/s

8422 ms

481K

RAG

Runs well

27.2 tok/s

12957 ms

481K

Quantization options

How GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV (14B params) fits at each quantization level on MacBook Pro M2 Max 96GB (69.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	D40
Q3_K_S	3	6.9 GB	Low	C40
NVFP4	4	7.8 GB	Medium	C40
Q4_K_M	4	8.5 GB	Medium	C40
Q5_K_M	5	10.1 GB	High	C41
Q6_K	6	11.5 GB	High	C41
Q8_0	8	15.0 GB	Very High	C41
F16Best for your GPU	16	28.7 GB	Maximum	C44