willitrun·ai

Can GGUF SOLARized GraniStral 14B 1902 YeAM HCT run on MacBook Pro M4 Pro 24GB?

YES — Runs Great

C52Usable
Estimated — low-sample bucket· few comparable runs

GGUF SOLARized GraniStral 14B 1902 YeAM HCT needs ~13.7 GB VRAM. MacBook Pro M4 Pro 24GB has 17.3 GB. With Q4_K_M quantization, expect ~22 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) — 13.7 GB, 21.7 tok/s, Runs well
13.7 GB required17.3 GB available
79% VRAM used

Fit status

Runs well

Decode

21.7 tok/s

TTFT

8938 ms

Safe context

51K

Memory

13.7 GB / 17.3 GB

Memory breakdown

Weights8.5 GB
KV Cache1.6 GB
Runtime0.9 GB
Headroom2.6 GB

See how fast it feels

See how fast it feelsGGUF SOLARized GraniStral 14B 1902 YeAM HCT on MacBook Pro M4 Pro 24GB
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: 21.7 tok/s decode · 8.9s TTFT (warm) · 54 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 well21.7 tok/s4875 ms51K
CodingCRuns well21.7 tok/s8938 ms51K
Agentic CodingCTight fit21.7 tok/s13000 ms51K
ReasoningCRuns well21.7 tok/s10563 ms51K
RAGCTight fit21.7 tok/s16250 ms51K

Inference speed

GGUF SOLARized GraniStral 14B 1902 YeAM HCT inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for GGUF SOLARized GraniStral 14B 1902 YeAM HCT at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~141 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / MacMemoryQuantSpeed (tok/s)Fits?
NVIDIARTX 5090 32GB
32 GBQ4_K_M140.6Fits
NVIDIARTX 4090 24GB
24 GBQ4_K_M89.7Fits
RX 7900 XTX 24GB
24 GBQ4_K_M80.9Fits
NVIDIARTX 3090 24GB
24 GBQ4_K_M76.7Fits
NVIDIARTX 4080 Super 16GB
16 GBQ4_K_M75.1Fits
Mac Studio M3 Ultra 256GB
256 GBQ4_K_M65.2Fits
Mac Studio M2 Ultra 128GB
128 GBQ4_K_M54.3Fits
Mac Studio M1 Ultra 128GB
128 GBQ4_K_M51.5Fits
MacBook Pro M4 Max 128GB
128 GBQ4_K_M35.4Fits
MacBook Pro M4 Max 64GB
64 GBQ4_K_M35.4Fits
NVIDIARTX 4070 12GB
12 GBQ4_K_M33.2Offloads
MacBook Pro M3 Max 64GB
64 GBQ4_K_M28.1Fits
MacBook Pro M1 Max 64GB
64 GBQ4_K_M25.8Fits
MacBook Pro M4 Pro 48GB
48 GBQ4_K_M21.7Fits
NVIDIARTX 3060 12GB
12 GBQ4_K_M19.5Offloads
NVIDIARTX 4060 8GB
8 GBQ4_K_M7.2Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How GGUF SOLARized GraniStral 14B 1902 YeAM HCT (14B params) fits at each quantization level on MacBook Pro M4 Pro 24GB (17.3 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
5.5 GB
LowC48
Q3_K_S
3
6.9 GB
LowC49
NVFP4
4
7.8 GB
MediumC50
Q4_K_M
4
8.5 GB
MediumC51
Q5_K_M
5
10.1 GB
HighC51
Q6_KBest for your GPU
6
11.5 GB
HighC50
Q8_0
8
15.0 GB
Very HighF0
F16
16
28.7 GB
MaximumF0

Get started

Copy-paste commands to run GGUF SOLARized GraniStral 14B 1902 YeAM HCT on your machine.

Run

lms load hf-srs6901--gguf-solarized-granistral-14b-1902-yeam-hct && lms server start

升级选项

能流畅运行 GGUF SOLARized GraniStral 14B 1902 YeAM HCT 的硬件

RX 7900 XT 20GB经济之选
800 GB/s (+527)
C
Raises estimated decode speed by about 159%.56.2 tok/s 解码

Raises estimated decode speed by about 159%.

~$899 MSRP

RX 7900 XTX 24GB最佳性价比
960 GB/s (+687)
C
Raises estimated decode speed by about 273%.80.9 tok/s 解码

Raises estimated decode speed by about 273%.

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

~$999 MSRP

Frequently asked questions

Can MacBook Pro M4 Pro 24GB run GGUF SOLARized GraniStral 14B 1902 YeAM HCT?

Yes, MacBook Pro M4 Pro 24GB can run GGUF SOLARized GraniStral 14B 1902 YeAM HCT with a C grade (Runs well). Expected decode speed: 21.7 tok/s.

How much VRAM does GGUF SOLARized GraniStral 14B 1902 YeAM HCT need?

GGUF SOLARized GraniStral 14B 1902 YeAM HCT (14B parameters) requires approximately 13.7 GB of memory with Q4_K_M quantization.

What is the best quantization for GGUF SOLARized GraniStral 14B 1902 YeAM HCT?

The recommended quantization for GGUF SOLARized GraniStral 14B 1902 YeAM HCT is Q4_K_M, which balances quality and memory efficiency.

What speed will GGUF SOLARized GraniStral 14B 1902 YeAM HCT run at on MacBook Pro M4 Pro 24GB?

On MacBook Pro M4 Pro 24GB, GGUF SOLARized GraniStral 14B 1902 YeAM HCT achieves approximately 21.7 tokens per second decode speed with a time-to-first-token of 8938ms using Q4_K_M quantization.

Can MacBook Pro M4 Pro 24GB run GGUF SOLARized GraniStral 14B 1902 YeAM HCT for coding?

For coding workloads, GGUF SOLARized GraniStral 14B 1902 YeAM HCT on MacBook Pro M4 Pro 24GB receives a C grade with 21.7 tok/s and 51K context.

What context window can GGUF SOLARized GraniStral 14B 1902 YeAM HCT use on MacBook Pro M4 Pro 24GB?

On MacBook Pro M4 Pro 24GB, GGUF SOLARized GraniStral 14B 1902 YeAM HCT can safely use up to 51K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M4 Pro 24GB as fast as VRAM for GGUF SOLARized GraniStral 14B 1902 YeAM HCT?

Not always. MacBook Pro M4 Pro 24GB 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 Pro 24GBSee all hardware for GGUF SOLARized GraniStral 14B 1902 YeAM HCT
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