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URL: https://willitrunai.com/can-run/hf-richarderkhov--stabilityai---japanese-stablelm-instruct-beta-70b-gguf-on-m4-pro-24gb

⇱ stabilityai japanese stablelm instruct beta 70b on MacBoo…

Can stabilityai japanese stablelm instruct beta 70b run on MacBook Pro M4 Pro 24GB?

NO — Won't Fit

F0Won't run
▲Estimated — low-sample bucket· few comparable runs

stabilityai japanese stablelm instruct beta 70b needs ~54.4 GB but MacBook Pro M4 Pro 24GB only has 17.3 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: LowStack: StandardBottleneck: Memory capacity
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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) — 54.4 GB, exceeds 17.3 GB available
54.4 GB required17.3 GB available
314% VRAM needed

37.1 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

4.0 tok/s

TTFT

48551 ms

Safe context

4K

Memory

54.4 GB / 17.3 GB

Offload

70%

Memory breakdown

Weights42.7 GB
KV Cache8.2 GB
Runtime0.9 GB
Headroom2.6 GB

See how fast it feels

With memory offload — actual speed may be lower
See how fast it feelsstabilityai japanese stablelm instruct beta 70b 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: 4.0 tok/s decode · 48.6s TTFT (warm) · 10 tok/s prefill

What limits this setup

Usable shared or unified memory is the main blocker for this model.

Not enough usable memory

The model needs 54.4 GB, but this setup only exposes 17.3 GB of usable shared or unified memory.

Best improvement path

Move to a larger memory pool

A larger unified-memory SKU or a discrete high-bandwidth GPU is the cleanest way to make this model practical.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatFToo heavy4.0 tok/s26482 ms4K
CodingFToo heavy4.0 tok/s48551 ms4K
Agentic CodingFToo heavy4.0 tok/s70619 ms4K
ReasoningFToo heavy4.0 tok/s57378 ms4K
RAGFToo heavy4.0 tok/s88274 ms4K

Quantization options

How stabilityai japanese stablelm instruct beta 70b (70B params) fits at each quantization level on MacBook Pro M4 Pro 24GB (17.3 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
27.3 GB
LowF0
Q3_K_S
3
34.3 GB
LowF0
NVFP4
4
39.2 GB
MediumF0
Q4_K_M
4
42.7 GB
MediumF0
Q5_K_M
5
50.4 GB
HighF0
Q6_K
6
57.4 GB
HighF0
Q8_0
8
74.9 GB
Very HighF0
F16
16
143.5 GB
MaximumF0

Upgrade options

Hardware that runs stabilityai japanese stablelm instruct beta 70b well

MacBook Pro M3 Max 128GBBudget pick
128 GB Unified (+104)400 GB/s (+127)
C
Makes the model fit on the accelerator instead of staying completely out of reach.5.6 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$2,499 MSRP

MacBook Pro M4 Max 96GBBest value
96 GB Unified (+72)546 GB/s (+273)
C
Makes the model fit on the accelerator instead of staying completely out of reach.14.1 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$2,499 MSRP

MacBook Pro M2 Max 96GBApple upgrade
96 GB Unified (+72)400 GB/s (+127)
C
Makes the model fit on the accelerator instead of staying completely out of reach.5.4 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$3,199 MSRP

👁 NVIDIA
NVIDIA H100 80GBBiggest leap
80 GB VRAM (+56)3350 GB/s (+3077)
B
Makes the model fit on the accelerator instead of staying completely out of reach.65.9 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$40,000 MSRP

Frequently asked questions

See all results for MacBook Pro M4 Pro 24GBSee all hardware for stabilityai japanese stablelm instruct beta 70b