Can SOLAR 10.7B v1.0 run on MacBook Pro M4 Max 64GB?

Q: Is unified memory on MacBook Pro M4 Max 64GB as fast as VRAM for SOLAR 10.7B v1.0?

Not always. MacBook Pro M4 Max 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.

YES — Runs Great

C47Usable

Estimated from fit model

SOLAR 10.7B v1.0 needs ~15.6 GB VRAM. MacBook Pro M4 Max 64GB has 46.1 GB. With Q4_K_M quantization, expect ~46 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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) — 15.6 GB, 46.4 tok/s, Runs well

15.6 GB required46.1 GB available

34% VRAM used

Fit status

Runs well

Decode

46.4 tok/s

TTFT

4174 ms

Safe context

405K

Memory

15.6 GB / 46.1 GB

Memory breakdown

Weights6.5 GB

KV Cache1.3 GB

Runtime0.9 GB

Headroom6.9 GB

See how fast it feels

See how fast it feelsSOLAR 10.7B v1.0 on MacBook Pro M4 Max 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: 46.4 tok/s decode · 4.2s TTFT (warm) · 116 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	46.4 tok/s	2277 ms	405K
Coding	C	Runs well	46.4 tok/s	4174 ms	405K
Agentic Coding	C	Runs well	46.4 tok/s	6072 ms	405K
Reasoning	C	Runs well	46.4 tok/s	4933 ms	405K
RAG	C	Runs well	46.4 tok/s	7590 ms	405K

Quantization options

How SOLAR 10.7B v1.0 (10.699999809265137B params) fits at each quantization level on MacBook Pro M4 Max 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.2 GB	Low	C41
Q3_K_S	3	5.2 GB	Low	C41
NVFP4	4

Get started

Copy-paste commands to run SOLAR 10.7B v1.0 on your machine.

Run

lms load hf-mradermacher--solar-10-7b-v1-0-gguf && lms server start

Upgrade options

Hardware that runs SOLAR 10.7B v1.0 well

Mac Studio M3 Ultra 96GBBudget pick

96 GB Unified (+32)819 GB/s (+273)

Raises estimated decode speed by about 84%.85.3 tok/s decode

Raises estimated decode speed by about 84%.

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

~$3,999 MSRP

Radeon Pro W7900 48GBBest value

864 GB/s (+318)

Raises estimated decode speed by about 68%.78.1 tok/s decode

Raises estimated decode speed by about 68%.

~$3,999 MSRP

Frequently asked questions

Can MacBook Pro M4 Max 64GB run SOLAR 10.7B v1.0?

Yes, MacBook Pro M4 Max 64GB can run SOLAR 10.7B v1.0 with a C grade (Runs well). Expected decode speed: 46.4 tok/s.

How much VRAM does SOLAR 10.7B v1.0 need?

SOLAR 10.7B v1.0 (10.699999809265137B parameters) requires approximately 15.6 GB of memory with Q4_K_M quantization.

What is the best quantization for SOLAR 10.7B v1.0?

The recommended quantization for SOLAR 10.7B v1.0 is Q4_K_M, which balances quality and memory efficiency.

What speed will SOLAR 10.7B v1.0 run at on MacBook Pro M4 Max 64GB?

On MacBook Pro M4 Max 64GB, SOLAR 10.7B v1.0 achieves approximately 46.4 tokens per second decode speed with a time-to-first-token of 4174ms using Q4_K_M quantization.

Can MacBook Pro M4 Max 64GB run SOLAR 10.7B v1.0 for coding?

For coding workloads, SOLAR 10.7B v1.0 on MacBook Pro M4 Max 64GB receives a C grade with 46.4 tok/s and 405K context.

What context window can SOLAR 10.7B v1.0 use on MacBook Pro M4 Max 64GB?

On MacBook Pro M4 Max 64GB, SOLAR 10.7B v1.0 can safely use up to 405K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M4 Max 64GB as fast as VRAM for SOLAR 10.7B v1.0?

See all results for MacBook Pro M4 Max 64GB See all hardware for SOLAR 10.7B v1.0

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