Is unified memory on MacBook Pro M4 Pro 64GB as fast as VRAM for stabilityai japanese stablelm base gamma 7b?

Not always. MacBook Pro M4 Pro 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.

Can stabilityai japanese stablelm base gamma 7b run on MacBook Pro M4 Pro 64GB?

YES — Runs Great

C45Usable

Estimated — low-sample bucket· few comparable runs

stabilityai japanese stablelm base gamma 7b needs ~12.9 GB VRAM. MacBook Pro M4 Pro 64GB has 46.1 GB. With Q4_K_M quantization, expect ~45 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) — 12.9 GB, 45.3 tok/s, Runs well

12.9 GB required46.1 GB available

28% VRAM used

Fit status

Runs well

Decode

45.3 tok/s

TTFT

4275 ms

Safe context

663K

Memory

12.9 GB / 46.1 GB

Memory breakdown

Weights4.3 GB

KV Cache0.8 GB

Runtime0.9 GB

Headroom6.9 GB

See how fast it feels

See how fast it feelsstabilityai   japanese stablelm base gamma 7b on MacBook Pro M4 Pro 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: 45.3 tok/s decode · 4.3s TTFT (warm) · 113 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	45.3 tok/s	2332 ms	663K
Coding	C	Runs well	45.3 tok/s	4275 ms	663K
Agentic Coding	C	Runs well	45.3 tok/s	6218 ms	663K
Reasoning	C	Runs well	45.3 tok/s	5052 ms	663K
RAG	C	Runs well	45.3 tok/s	7772 ms	663K

Inference speed

stabilityai japanese stablelm base gamma 7b inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for stabilityai japanese stablelm base gamma 7b at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~98 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 / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	98.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	98.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	98.0

Quantization options

How stabilityai japanese stablelm base gamma 7b (7B params) fits at each quantization level on MacBook Pro M4 Pro 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C41
Q3_K_S	3	3.4 GB	Low	C41
NVFP4	4

Get started

Copy-paste commands to run stabilityai japanese stablelm base gamma 7b on your machine.

Run

lms load hf-richarderkhov--stabilityai---japanese-stablelm-base-gamma-7b-gguf && lms server start

Upgrade options

Hardware that runs stabilityai japanese stablelm base gamma 7b well

MacBook Pro M4 Max 96GBBudget pick

96 GB Unified (+32)546 GB/s (+273)

Raises estimated decode speed by about 94%.87.8 tok/s decode

Raises estimated decode speed by about 94%.

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

~$2,499 MSRP

Mac Studio M3 Ultra 96GBBest value

96 GB Unified (+32)819 GB/s (+546)

Raises estimated decode speed by about 116%.98 tok/s decode

Raises estimated decode speed by about 116%.

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

~$3,999 MSRP

Mac Studio M2 Ultra 128GBApple upgrade

128 GB Unified (+64)800 GB/s (+527)

Raises estimated decode speed by about 116%.98 tok/s decode

Raises estimated decode speed by about 116%.

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

~$3,999 MSRP

Frequently asked questions

Can MacBook Pro M4 Pro 64GB run stabilityai japanese stablelm base gamma 7b?

Yes, MacBook Pro M4 Pro 64GB can run stabilityai japanese stablelm base gamma 7b with a C grade (Runs well). Expected decode speed: 45.3 tok/s.

How much VRAM does stabilityai japanese stablelm base gamma 7b need?

stabilityai japanese stablelm base gamma 7b (7B parameters) requires approximately 12.9 GB of memory with Q4_K_M quantization.

What is the best quantization for stabilityai japanese stablelm base gamma 7b?

The recommended quantization for stabilityai japanese stablelm base gamma 7b is Q4_K_M, which balances quality and memory efficiency.

What speed will stabilityai japanese stablelm base gamma 7b run at on MacBook Pro M4 Pro 64GB?

On MacBook Pro M4 Pro 64GB, stabilityai japanese stablelm base gamma 7b achieves approximately 45.3 tokens per second decode speed with a time-to-first-token of 4275ms using Q4_K_M quantization.

Can MacBook Pro M4 Pro 64GB run stabilityai japanese stablelm base gamma 7b for coding?

For coding workloads, stabilityai japanese stablelm base gamma 7b on MacBook Pro M4 Pro 64GB receives a C grade with 45.3 tok/s and 663K context.

What context window can stabilityai japanese stablelm base gamma 7b use on MacBook Pro M4 Pro 64GB?

See all results for MacBook Pro M4 Pro 64GB See all hardware for stabilityai japanese stablelm base gamma 7b

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/hf-richarderkhov--stabilityai---japanese-stablelm-base-gamma-7b-gguf-on-m4-pro-64gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: