How much VRAM does SmolLM3 3B need?

About 2.3 GB at Q4_K_M with 4K of context and batch 1, rising to roughly 4.4 GB at 32K. That figure is weights plus KV cache plus a 10% activation buffer, calculated from the model's architecture rather than measured on a card.

Will SmolLM3 3B run on a 8GB GPU?

Yes — at Q4_K_M and 4K context it needs about 2.3 GB, which leaves 3.7 GB spare on a Mac M1 8G, or on any of the other 8 GB cards here (10 in all). That is the smallest size in this index that clears it comfortably; 70 of 70 do.

Which quantization of SmolLM3 3B should I use?

No published quality comparison exists for this model, so pick by footprint: Q4_K_M is the level most people run, and a higher bits-per-weight level is more faithful. All 2 levels in the index are Q4_K_M, Q8_0.

SmolLM3 3B

Hugging Face SmolLM

Hugging Face's fully open 3B model, with its training data and recipe published alongside the weights. It has a reasoning mode you can switch on or off, and a 64K window it was actually trained to, which reaches 128K only by YaRN extension. Four KV heads keep the cache small, so the window is affordable: at Q4_K_M it fits a 4 GB card at short context and an 8 GB card at its full trained window. That also makes it one of the few models here that is realistic on a CPU-only machine. Sizes use the calculator's generic rates, since no GGUF file size could be checked, and no per-level quality loss has been published.

⬇ 617.0K HF downloads♥ 1042 likesHuggingFaceTB/SmolLM3-3B· stats from 10/3/2026

Consumer GPUMac / Apple SiliconCPU / VPS

66K

Max Context

Quant Variants

GGUF Q8_0

Best Quality

—

Accuracy Retained

Calculate VRAM Hugging Face Compare

Quantization Variants

Per-quant VRAM, quality loss, and inference speed on RTX 4090

Measured = run on this site · Estimated = not run here: calculated, or a figure this site has not verified · Community = public reports

Format	Level	BPW	VRAM	PPL Loss	Speed	Source	Actions
GGUF	Q4_K_M	4.85	2.3 GB	—	—	Estimated	Calc HF
GGUF	Q8_0	8.5	3.7 GB	—	—	Estimated	Calc HF

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Running SmolLM3 3B locally

At Q4_K_M and 4K of context, SmolLM3 3B needs about 2.3 GB — 1.8 GB of weights, 0.28 GB of KV cache and a 0.2 GB activation buffer. The smallest cards in this index that clear that comfortably are 8 GB ones — 10 of them, from the Mac M1 8G up — and 70 of the 70 cards here do. These are calculated figures, not measurements: the estimate stops counting a card as comfortable at 88% of its VRAM, which is roughly the room a desktop session needs.

What longer context costs

Going from 4K to 32K adds about 1.97 GB, taking the total to 4.4 GB. Weights do not move with context — only the KV cache does, and it grows linearly, so this is the number to watch when planning for long documents. The model's native window is 64K; holding all of it at Q4_K_M would need about 7 GB.

Which build to download

This index only tracks SmolLM3 3B in GGUF, across 2 levels (Q4_K_M, Q8_0). No per-level perplexity sweep has been published for this model, so the quality column is empty rather than estimated — within one model, more bits per weight is the only ordering the data supports. No throughput has been measured for this model on this site's hardware. GGUF runs on llama.cpp and Ollama across NVIDIA, AMD and Apple silicon; AWQ and GPTQ target vLLM on CUDA and ROCm; EXL2 is ExLlamaV2 — CUDA only, and now archived, so local use rather than serving.

Common questions

How much VRAM does SmolLM3 3B need?: About 2.3 GB at Q4_K_M with 4K of context and batch 1, rising to roughly 4.4 GB at 32K. That figure is weights plus KV cache plus a 10% activation buffer, calculated from the model's architecture rather than measured on a card.
Will SmolLM3 3B run on a 8GB GPU?: Yes — at Q4_K_M and 4K context it needs about 2.3 GB, which leaves 3.7 GB spare on a Mac M1 8G, or on any of the other 8 GB cards here (10 in all). That is the smallest size in this index that clears it comfortably; 70 of 70 do.
Which quantization of SmolLM3 3B should I use?: No published quality comparison exists for this model, so pick by footprint: Q4_K_M is the level most people run, and a higher bits-per-weight level is more faithful. All 2 levels in the index are Q4_K_M, Q8_0.

Where this model fits

Sized at Q4_K_M with a 4K context window, smallest card first. Comfortable means the estimate uses at most 88% of the memory.

Runs comfortably on: Mac M1 8G8GB · +3.7 Mac M3 8G8GB · +3.7 RTX 40608GB · +5.7 RTX 4060 Ti 8G8GB · +5.7 Radeon RX 9060 XT 8G8GB · +5.7
Ships in: GGUF
Try it yourself: Size it in the calculator Generate the run command

How to actually run this

Deployment guides for this model and this class of hardware.

This page's figures change when the model or the runtime does.Last updated 2026-10-03 RSS → /feed.xml