willitrun·ai

Can Solar Open 100B run on Mac Studio M3 Ultra 256GB?

YES — Runs Great

C46Usable
Estimated from fit model

Solar Open 100B needs ~101.3 GB VRAM. Mac Studio M3 Ultra 256GB has 184.3 GB. With Q4_K_M quantization, expect ~9 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: 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) — 101.3 GB, 9.1 tok/s, Runs well
101.3 GB required184.3 GB available
55% VRAM used

Fit status

Runs well

Decode

9.1 tok/s

TTFT

21205 ms

Safe context

129K

Memory

101.3 GB / 184.3 GB

Memory breakdown

Weights61.0 GB
KV Cache11.7 GB
Runtime0.9 GB
Headroom27.6 GB

See how fast it feels

See how fast it feelsSolar Open 100B on Mac Studio M3 Ultra 256GB
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: 9.1 tok/s decode · 21.2s TTFT (warm) · 23 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 well9.1 tok/s11566 ms129K
CodingCRuns well9.1 tok/s21205 ms129K
Agentic CodingCRuns well9.1 tok/s30844 ms129K
ReasoningCRuns well9.1 tok/s25061 ms129K
RAGCRuns well9.1 tok/s38555 ms129K

Inference speed

Solar Open 100B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Solar Open 100B at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is MacBook Pro M4 Max 128GB at ~10 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?
MacBook Pro M4 Max 128GB
128 GBQ4_K_M9.8Tight
Mac Studio M3 Ultra 256GB
256 GBQ4_K_M9.1Fits
Mac Studio M2 Ultra 128GB
128 GBQ4_K_M7.6Tight
Mac Studio M1 Ultra 128GB
128 GBQ4_K_M7.2Tight
2× RX 7900 XTX 24GB
48 GBQ4_K_M5.1Too big
MacBook Pro M4 Max 64GB
64 GBQ4_K_M4.9Too big
NVIDIARTX 5090 32GB
32 GBQ4_K_M3.0Too big
MacBook Pro M4 Pro 48GB
48 GBQ4_K_M2.8Too big
NVIDIA2× RTX 4090 24GB
48 GBQ4_K_M2.7Too big
NVIDIA2× RTX 3090 24GB
48 GBQ4_K_M2.5Too big
NVIDIA4× RTX 3060 12GB
48 GBQ4_K_M2.2Too big
NVIDIARTX 4090 24GB
24 GBQ4_K_M2.0Too big
NVIDIARTX 4080 Super 16GB
16 GBQ4_K_M2.0Too big
NVIDIARTX 3090 24GB
24 GBQ4_K_M2.0Too big
NVIDIARTX 4070 12GB
12 GBQ4_K_M2.0Too big
NVIDIARTX 3060 12GB
12 GBQ4_K_M2.0Too big
NVIDIARTX 4060 8GB
8 GBQ4_K_M2.0Too big
RX 7900 XTX 24GB
24 GBQ4_K_M2.0Too big
MacBook Pro M3 Max 64GB
64 GBQ4_K_M2.0Too big
MacBook Pro M1 Max 64GB
64 GBQ4_K_M2.0Too 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 Solar Open 100B (100B params) fits at each quantization level on Mac Studio M3 Ultra 256GB (184.3 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
39.0 GB
LowC40
Q3_K_S
3
49.0 GB
LowC41
NVFP4
4
56.0 GB
MediumC42
Q4_K_M
4
61.0 GB
MediumC43
Q5_K_M
5
72.0 GB
HighC44
Q6_K
6
82.0 GB
HighC45
Q8_0Best for your GPU
8
107.0 GB
Very HighC48
F16
16
205.0 GB
MaximumF0

Get started

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

Run

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

Opciones de mejora

Hardware que ejecuta bien Solar Open 100B

AMD Instinct MI350X 288GBOpción económica
288 GB VRAM (+32)8000 GB/s (+7181)
C
Sube la velocidad estimada de decodificación alrededor de un 952%.95.7 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 952%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$8,000 MSRP

AMD Instinct MI300X 192GBMejor relación calidad-precio
5300 GB/s (+4481)
C
Sube la velocidad estimada de decodificación alrededor de un 645%.67.8 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 645%.

~$15,000 MSRP

Frequently asked questions

Can Mac Studio M3 Ultra 256GB run Solar Open 100B?

Yes, Mac Studio M3 Ultra 256GB can run Solar Open 100B with a C grade (Runs well). Expected decode speed: 9.1 tok/s.

How much VRAM does Solar Open 100B need?

Solar Open 100B (100B parameters) requires approximately 101.3 GB of memory with Q4_K_M quantization.

What is the best quantization for Solar Open 100B?

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

What speed will Solar Open 100B run at on Mac Studio M3 Ultra 256GB?

On Mac Studio M3 Ultra 256GB, Solar Open 100B achieves approximately 9.1 tokens per second decode speed with a time-to-first-token of 21205ms using Q4_K_M quantization.

Can Mac Studio M3 Ultra 256GB run Solar Open 100B for coding?

For coding workloads, Solar Open 100B on Mac Studio M3 Ultra 256GB receives a C grade with 9.1 tok/s and 129K context.

What context window can Solar Open 100B use on Mac Studio M3 Ultra 256GB?

On Mac Studio M3 Ultra 256GB, Solar Open 100B can safely use up to 129K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on Mac Studio M3 Ultra 256GB as fast as VRAM for Solar Open 100B?

Not always. Mac Studio M3 Ultra 256GB 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 Mac Studio M3 Ultra 256GBSee all hardware for Solar Open 100B
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