Can SOLAR 10.7B Instruct v1.0 uncensored run on MacBook Pro M2 Pro 32GB?

YES — Runs Great

C48Usable

Estimated from fit model

SOLAR 10.7B Instruct v1.0 uncensored needs ~12.1 GB VRAM. MacBook Pro M2 Pro 32GB has 23.0 GB. With Q4_K_M quantization, expect ~21 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth

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) — 12.1 GB, 21.4 tok/s, Runs well

12.1 GB required23.0 GB available

53% VRAM used

Fit status

Runs well

Decode

21.4 tok/s

TTFT

9026 ms

Safe context

155K

Memory

12.1 GB / 23.0 GB

Memory breakdown

Weights6.5 GB

KV Cache1.3 GB

Runtime0.9 GB

Headroom3.5 GB

See how fast it feels

See how fast it feelsSOLAR 10.7B Instruct v1.0 uncensored on MacBook Pro M2 Pro 32GB
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: 21.4 tok/s decode · 9.0s TTFT (warm) · 54 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	21.4 tok/s	4923 ms	155K
Coding	C	Runs well	21.4 tok/s	9026 ms	155K
Agentic Coding	C	Runs well	21.4 tok/s	13129 ms	155K
Reasoning	C	Runs well	21.4 tok/s	10667 ms	155K
RAG	C	Runs well	21.4 tok/s	16411 ms	155K

Inference speed

SOLAR 10.7B Instruct v1.0 uncensored inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for SOLAR 10.7B Instruct v1.0 uncensored 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 10.7B Instruct v1.0 uncensored (10.699999809265137B params) fits at each quantization level on MacBook Pro M2 Pro 32GB (23.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.2 GB	Low	C45
Q3_K_S	3	5.2 GB	Low	C46
NVFP4	4	6.0 GB	Medium	C46
Q4_K_M	4	6.5 GB	Medium	C47
Q5_K_M	5	7.7 GB	High	C47
Q6_K	6	8.8 GB	High	C48
Q8_0Best for your GPU	8	11.4 GB	Very High	C50
F16	16	21.9 GB	Maximum	F0

Get started

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

Run

lms load hf-thebloke--solar-10-7b-instruct-v1-0-uncensored-gguf && lms server start

升级选项

能流畅运行 SOLAR 10.7B Instruct v1.0 uncensored 的硬件

Intel Arc Pro B60 24GB最佳性价比

456 GB/s (+256)

Raises estimated decode speed by about 76%.37.7 tok/s 解码

Raises estimated decode speed by about 76%.

~$599 MSRP

MacBook Pro M4 Max 36GB经济之选

36 GB Unified (+4)410 GB/s (+210)

Raises estimated decode speed by about 85%.39.6 tok/s 解码

Raises estimated decode speed by about 85%.

~$2,499 MSRP

Frequently asked questions

Can MacBook Pro M2 Pro 32GB run SOLAR 10.7B Instruct v1.0 uncensored?

Yes, MacBook Pro M2 Pro 32GB can run SOLAR 10.7B Instruct v1.0 uncensored with a C grade (Runs well). Expected decode speed: 21.4 tok/s.

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

SOLAR 10.7B Instruct v1.0 uncensored (10.699999809265137B parameters) requires approximately 12.1 GB of memory with Q4_K_M quantization.

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

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

What speed will SOLAR 10.7B Instruct v1.0 uncensored run at on MacBook Pro M2 Pro 32GB?

On MacBook Pro M2 Pro 32GB, SOLAR 10.7B Instruct v1.0 uncensored achieves approximately 21.4 tokens per second decode speed with a time-to-first-token of 9026ms using Q4_K_M quantization.

Can MacBook Pro M2 Pro 32GB run SOLAR 10.7B Instruct v1.0 uncensored for coding?

For coding workloads, SOLAR 10.7B Instruct v1.0 uncensored on MacBook Pro M2 Pro 32GB receives a C grade with 21.4 tok/s and 155K context.

What context window can SOLAR 10.7B Instruct v1.0 uncensored use on MacBook Pro M2 Pro 32GB?

On MacBook Pro M2 Pro 32GB, SOLAR 10.7B Instruct v1.0 uncensored can safely use up to 155K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M2 Pro 32GB as fast as VRAM for SOLAR 10.7B Instruct v1.0 uncensored?

Not always. MacBook Pro M2 Pro 32GB 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 M2 Pro 32GB See all hardware for SOLAR 10.7B Instruct v1.0 uncensored

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