Can solar finalised finetuned Model 10.7B i1 run on Mac Studio M3 Ultra 256GB?

YES — Runs Great

C46Usable

Estimated from fit model

solar finalised finetuned Model 10.7B i1 needs ~36.3 GB VRAM. Mac Studio M3 Ultra 256GB has 184.3 GB. With Q4_K_M quantization, expect ~85 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: 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) — 36.3 GB, 85.3 tok/s, Runs well

36.3 GB required184.3 GB available

20% VRAM used

Fit status

Runs well

Decode

85.3 tok/s

TTFT

2269 ms

Safe context

1.9M

Memory

36.3 GB / 184.3 GB

Memory breakdown

Weights6.5 GB

KV Cache1.3 GB

Runtime0.9 GB

Headroom27.6 GB

See how fast it feels

See how fast it feelssolar finalised finetuned Model 10.7B i1 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: 85.3 tok/s decode · 2.3s TTFT (warm) · 213 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	85.3 tok/s	1238 ms	1.9M
Coding	C	Runs well	85.3 tok/s	2269 ms	1.9M
Agentic Coding	C	Runs well	85.3 tok/s	3300 ms	1.9M
Reasoning	C	Runs well	85.3 tok/s	2681 ms	1.9M
RAG	C	Runs well	85.3 tok/s	4125 ms	1.9M

Inference speed

solar finalised finetuned Model 10.7B i1 inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for solar finalised finetuned Model 10.7B i1 at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~150 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	149.8	Fits
RTX 4090 24GB	24 GB	Q4_K_M	117.4	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	105.9	Fits
RTX 3090 24GB	24 GB	Q4_K_M	100.4	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	93.6	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	85.3	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	71.1	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	67.4	Fits
RTX 4070 12GB	12 GB	Q4_K_M	60.8	Tight
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	46.4	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	46.4	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	36.8	Fits
RTX 3060 12GB	12 GB	Q4_K_M	36.4	Tight
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	33.7	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	28.3	Fits
RTX 4060 8GB	8 GB	Q4_K_M	16.8	Heavy offload

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 finalised finetuned Model 10.7B i1 (10.699999809265137B params) fits at each quantization level on Mac Studio M3 Ultra 256GB (184.3 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.2 GB	Low	D37
Q3_K_S	3	5.2 GB	Low	D37
NVFP4	4	6.0 GB	Medium	D37
Q4_K_M	4	6.5 GB	Medium	D37
Q5_K_M	5	7.7 GB	High	D37
Q6_K	6	8.8 GB	High	D37
Q8_0	8	11.4 GB	Very High	D37
F16Best for your GPU	16	21.9 GB	Maximum	D37

Get started

Copy-paste commands to run solar finalised finetuned Model 10.7B i1 on your machine.

Run

lms load hf-mradermacher--solar-finalised-finetuned-model-10-7b-i1-gguf && lms server start

Frequently asked questions

Can Mac Studio M3 Ultra 256GB run solar finalised finetuned Model 10.7B i1?

Yes, Mac Studio M3 Ultra 256GB can run solar finalised finetuned Model 10.7B i1 with a C grade (Runs well). Expected decode speed: 85.3 tok/s.

How much VRAM does solar finalised finetuned Model 10.7B i1 need?

solar finalised finetuned Model 10.7B i1 (10.699999809265137B parameters) requires approximately 36.3 GB of memory with Q4_K_M quantization.

What is the best quantization for solar finalised finetuned Model 10.7B i1?

The recommended quantization for solar finalised finetuned Model 10.7B i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will solar finalised finetuned Model 10.7B i1 run at on Mac Studio M3 Ultra 256GB?

On Mac Studio M3 Ultra 256GB, solar finalised finetuned Model 10.7B i1 achieves approximately 85.3 tokens per second decode speed with a time-to-first-token of 2269ms using Q4_K_M quantization.

Can Mac Studio M3 Ultra 256GB run solar finalised finetuned Model 10.7B i1 for coding?

For coding workloads, solar finalised finetuned Model 10.7B i1 on Mac Studio M3 Ultra 256GB receives a C grade with 85.3 tok/s and 1.9M context.

What context window can solar finalised finetuned Model 10.7B i1 use on Mac Studio M3 Ultra 256GB?

On Mac Studio M3 Ultra 256GB, solar finalised finetuned Model 10.7B i1 can safely use up to 1.9M 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 finalised finetuned Model 10.7B i1?

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 256GB See all hardware for solar finalised finetuned Model 10.7B i1

Embed this result▼

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

<iframe src="https://willitrunai.com/embed/hf-mradermacher--solar-finalised-finetuned-model-10-7b-i1-gguf-on-m3-ultra-256gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: