Can SmolLM3 3B run on RTX 4070 Laptop 8GB?

YES — Runs Great

B62Good

Estimated from fit model

SmolLM3 3B needs ~5.5 GB VRAM. RTX 4070 Laptop 8GB has 8.0 GB. With Q4_K_M quantization, expect ~48 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: 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) — 5.5 GB, 48.0 tok/s, Runs well

5.5 GB required8.0 GB available

69% VRAM used

Fit status

Runs well

Decode

48.0 tok/s

TTFT

4033 ms

Safe context

37K

Memory

5.5 GB / 8.0 GB

Memory breakdown

Weights1.8 GB

KV Cache2.0 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsSmolLM3 3B on RTX 4070 Laptop 8GB
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: 48.0 tok/s decode · 4.0s TTFT (warm) · 120 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Runs well	48.0 tok/s	2200 ms	37K
Coding	B	Runs well	48.0 tok/s	4033 ms	37K
Agentic Coding	B	Tight fit	48.0 tok/s	5867 ms	37K
Reasoning	B	Runs well	48.0 tok/s	4767 ms	37K
RAG	B	Tight fit	48.0 tok/s	7333 ms	37K

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 / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	57.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	48.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	48.0	Fits
RTX 4060 8GB	8 GB	Q4_K_M	48.0	Fits
RTX 3090 24GB	24 GB	Q4_K_M	42.0	Fits
RTX 4070 12GB	12 GB	Q4_K_M	42.0	Fits
RTX 3060 12GB	12 GB	Q4_K_M	42.0	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	42.0	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	42.0	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	42.0	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	42.0	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	42.0	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	42.0	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	42.0	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	42.0	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	42.0	Fits

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 RTX 4070 Laptop 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.2 GB	Low	B58
Q3_K_S	3	1.5 GB	Low	B59
NVFP4	4	1.7 GB	Medium	B59
Q4_K_M	4	1.8 GB	Medium	B59
Q5_K_M	5	2.2 GB	High	B60
Q6_K	6	2.5 GB	High	B61
Q8_0Best for your GPU	8	3.2 GB	Very High	B61
F16	16	6.1 GB	Maximum	F0

Get started

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

Run

lms load SmolLM3-3B && lms server start

Frequently asked questions

Can RTX 4070 Laptop 8GB run SmolLM3 3B?

Yes, RTX 4070 Laptop 8GB can run SmolLM3 3B with a B grade (Runs well). Expected decode speed: 48.0 tok/s.

How much VRAM does SmolLM3 3B need?

SmolLM3 3B (3B parameters) requires approximately 5.5 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 RTX 4070 Laptop 8GB?

On RTX 4070 Laptop 8GB, SmolLM3 3B achieves approximately 48.0 tokens per second decode speed with a time-to-first-token of 4033ms using Q4_K_M quantization.

Can RTX 4070 Laptop 8GB run SmolLM3 3B for coding?

For coding workloads, SmolLM3 3B on RTX 4070 Laptop 8GB receives a B grade with 48.0 tok/s and 37K context.

What context window can SmolLM3 3B use on RTX 4070 Laptop 8GB?

On RTX 4070 Laptop 8GB, SmolLM3 3B can safely use up to 37K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

See all results for RTX 4070 Laptop 8GB See all hardware for SmolLM3 3B

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