Will It Run AI

Can Solar Open 100B i1 run on MacBook Pro M4 Max 128GB?

YES — Tight Fit

C47Usable
Estimated from fit model

Solar Open 100B i1 needs ~87.4 GB VRAM. MacBook Pro M4 Max 128GB has 92.2 GB. With Q4_K_M quantization, expect ~10 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: MediumStack: 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) — 87.4 GB, 9.8 tok/s, Tight fit
87.4 GB required92.2 GB available
95% VRAM used

Fit status

Tight fit

Decode

9.8 tok/s

TTFT

19663 ms

Safe context

22K

Memory

87.4 GB / 92.2 GB

Memory breakdown

Weights61.0 GB
KV Cache11.7 GB
Runtime0.9 GB
Headroom13.8 GB

See how fast it feels

See how fast it feelsSolar Open 100B i1 on MacBook Pro M4 Max 128GB
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: 9.8 tok/s decode · 19.7s TTFT (warm) · 25 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

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

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatCTight fit9.8 tok/s10725 ms22K
CodingCTight fit9.8 tok/s19663 ms22K
Agentic CodingDRuns with offload (needs ~4.3 GB host RAM)8.7 tok/s32500 ms22K
ReasoningCTight fit9.8 tok/s23239 ms22K
RAGDRuns with offload (needs ~4.3 GB host RAM)8.7 tok/s40625 ms22K

Quantization options

How Solar Open 100B i1 (100B params) fits at each quantization level on MacBook Pro M4 Max 128GB (92.2 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
39.0 GB
LowC45
Q3_K_S
3
49.0 GB
LowC48
NVFP4
4
56.0 GB
MediumC48
Q4_K_M
4
61.0 GB
MediumC48
Q5_K_MBest for your GPU
5
72.0 GB
HighC48
Q6_K
6
82.0 GB
HighF0
Q8_0
8
107.0 GB
Very HighF0
F16
16
205.0 GB
MaximumF0

Get started

Copy-paste commands to run Solar Open 100B i1 on your machine.

Run

lms load hf-mradermacher--solar-open-100b-i1-gguf && lms server start

Upgrade-Optionen

Hardware, die Solar Open 100B i1 gut ausführt

AMD Instinct MI350X 288GBGünstige Wahl
288 GB VRAM (+160)8000 GB/s (+7454)
C
Raises estimated decode speed by about 877%.95.7 tok/s Dekodierung

Raises estimated decode speed by about 877%.

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

ca. $8,000 MSRP

NVIDIARTX PRO 6000 Blackwell Workstation Edition 96GBBestes Preis-Leistungs-Verhältnis
1792 GB/s (+1246)
C
Raises estimated decode speed by about 152%.24.7 tok/s Dekodierung

Raises estimated decode speed by about 152%.

ca. $9,999 MSRP

AMD Instinct MI300A 128GBGrößter Sprung
5300 GB/s (+4754)
C
Raises estimated decode speed by about 520%.60.8 tok/s Dekodierung

Raises estimated decode speed by about 520%.

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

ca. $12,000 MSRP

Frequently asked questions

Can MacBook Pro M4 Max 128GB run Solar Open 100B i1?

Yes, MacBook Pro M4 Max 128GB can run Solar Open 100B i1 with a C grade (Tight fit). Expected decode speed: 9.8 tok/s.

How much VRAM does Solar Open 100B i1 need?

Solar Open 100B i1 (100B parameters) requires approximately 87.4 GB of memory with Q4_K_M quantization.

What is the best quantization for Solar Open 100B i1?

The recommended quantization for Solar Open 100B i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will Solar Open 100B i1 run at on MacBook Pro M4 Max 128GB?

On MacBook Pro M4 Max 128GB, Solar Open 100B i1 achieves approximately 9.8 tokens per second decode speed with a time-to-first-token of 19663ms using Q4_K_M quantization.

Can MacBook Pro M4 Max 128GB run Solar Open 100B i1 for coding?

For coding workloads, Solar Open 100B i1 on MacBook Pro M4 Max 128GB receives a C grade with 9.8 tok/s and 22K context.

What context window can Solar Open 100B i1 use on MacBook Pro M4 Max 128GB?

On MacBook Pro M4 Max 128GB, Solar Open 100B i1 can safely use up to 22K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if Solar Open 100B i1 feels slow on MacBook Pro M4 Max 128GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Is unified memory on MacBook Pro M4 Max 128GB as fast as VRAM for Solar Open 100B i1?

Not always. MacBook Pro M4 Max 128GB 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 Max 128GBSee all hardware for Solar Open 100B i1
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