willitrun·ai

Can SmolLM3 3B run on Mac Studio M2 Ultra 128GB?

YES — Runs Great

C51Usable
Estimated from fit model

SmolLM3 3B needs ~18.5 GB VRAM. Mac Studio M2 Ultra 128GB has 92.2 GB. With Q4_K_M quantization, expect ~42 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) 18.5 GB, 42.0 tok/s, Runs well
18.5 GB required92.2 GB available
20% VRAM used

Fit status

Runs well

Decode

42.0 tok/s

TTFT

4610 ms

Safe context

128K

Memory

18.5 GB / 92.2 GB

Memory breakdown

Weights1.8 GB
KV Cache2.0 GB
Runtime0.9 GB
Headroom13.8 GB

See how fast it feels

See how fast it feelsSmolLM3 3B on Mac Studio M2 Ultra 128GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step. Given information: - Speed = 60 mph (miles per hour) - Time = 2.5 hours Formula: Distance = Speed x Time Calculation: Distance = 60 mph x 2.5 h = 150 miles The train travels 150 miles. Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time. Breakdown: - First half: 1.25 hours at 60 mph = 75 miles - Stop: 20 minutes (no distance) - Second half: 1.25 hours at 60 mph = 75 miles Total distance: 150 miles (same) Total time: 2.5 h + 0.33 h = 2.83 hours Average speed: 150 / 2.83 = 53 mph The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 42.0 tok/s decode · 4.6s TTFT (warm) · 105 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 well42.0 tok/s2514 ms128K
CodingCRuns well42.0 tok/s4610 ms128K
Agentic CodingCRuns well42.0 tok/s6705 ms128K
ReasoningCRuns well42.0 tok/s5448 ms128K
RAGCRuns well42.0 tok/s8381 ms128K

Inference speed

SmolLM3 3B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for SmolLM3 3B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~57 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?
NVIDIARTX 5090 32GB
32 GBQ4_K_M57.0Fits
NVIDIARTX 4090 24GB
24 GBQ4_K_M48.0Fits
NVIDIARTX 4080 Super 16GB
16 GBQ4_K_M48.0Fits
NVIDIARTX 4060 8GB
8 GBQ4_K_M48.0Fits
NVIDIARTX 3090 24GB
24 GBQ4_K_M42.0Fits
NVIDIARTX 4070 12GB
12 GBQ4_K_M42.0Fits
NVIDIARTX 3060 12GB
12 GBQ4_K_M42.0Fits
RX 7900 XTX 24GB
24 GBQ4_K_M42.0Fits
MacBook Pro M4 Max 128GB
128 GBQ4_K_M42.0Fits
Mac Studio M3 Ultra 256GB
256 GBQ4_K_M42.0Fits
Mac Studio M2 Ultra 128GB
128 GBQ4_K_M42.0Fits
Mac Studio M1 Ultra 128GB
128 GBQ4_K_M42.0Fits
MacBook Pro M4 Max 64GB
64 GBQ4_K_M42.0Fits
MacBook Pro M3 Max 64GB
64 GBQ4_K_M42.0Fits
MacBook Pro M1 Max 64GB
64 GBQ4_K_M42.0Fits
MacBook Pro M4 Pro 48GB
48 GBQ4_K_M42.0Fits

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 SmolLM3 3B (3B params) fits at each quantization level on Mac Studio M2 Ultra 128GB (92.2 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
1.2 GB
LowC47
Q3_K_S
3
1.5 GB
LowC47
NVFP4
4
1.7 GB
MediumC47
Q4_K_M
4
1.8 GB
MediumC47
Q5_K_M
5
2.2 GB
HighC47
Q6_K
6
2.5 GB
HighC47
Q8_0
8
3.2 GB
Very HighC47
F16Best for your GPU
16
6.1 GB
MaximumC47

Get started

Copy-paste commands to run SmolLM3 3B on your machine.

Run

lms load SmolLM3-3B && lms server start

Frequently asked questions

Can Mac Studio M2 Ultra 128GB run SmolLM3 3B?

Yes, Mac Studio M2 Ultra 128GB can run SmolLM3 3B with a C grade (Runs well). Expected decode speed: 42.0 tok/s.

How much VRAM does SmolLM3 3B need?

SmolLM3 3B (3B parameters) requires approximately 18.5 GB of memory with Q4_K_M quantization.

What is the best quantization for SmolLM3 3B?

The recommended quantization for SmolLM3 3B is Q4_K_M, which balances quality and memory efficiency.

What speed will SmolLM3 3B run at on Mac Studio M2 Ultra 128GB?

On Mac Studio M2 Ultra 128GB, SmolLM3 3B achieves approximately 42.0 tokens per second decode speed with a time-to-first-token of 4610ms using Q4_K_M quantization.

Can Mac Studio M2 Ultra 128GB run SmolLM3 3B for coding?

For coding workloads, SmolLM3 3B on Mac Studio M2 Ultra 128GB receives a C grade with 42.0 tok/s and 128K context.

What context window can SmolLM3 3B use on Mac Studio M2 Ultra 128GB?

On Mac Studio M2 Ultra 128GB, SmolLM3 3B can safely use up to 128K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

Is unified memory on Mac Studio M2 Ultra 128GB as fast as VRAM for SmolLM3 3B?

Not always. Mac Studio M2 Ultra 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 Mac Studio M2 Ultra 128GBSee all hardware for SmolLM3 3B
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<iframe src="https://willitrunai.com/embed/smollm3-3b-on-m2-ultra-128gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

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