SmolLM3 3B Instruct Heretic — Hardware Requirements & GPU Compatibility
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SmolLM3 3B Instruct Heretic is a 3.1B-parameter open language model from K0D3IN in the SmolLM family. It supports a context window of up to 65,536 tokens. At Q4_K_M it needs about 2.30 GB of VRAM — see which GPUs and Macs can run it below.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 64K)Best on 24 GB (Q4_K_M · ~285 tok/s · 64K)Macs from 8 GB (Q4_K_M · ~31 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run SmolLM3 3B Instruct Heretic
One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.
Recommended
vllm serve K0D3IN/SmolLM3-3B-Instruct-heretic --max-model-len 16384vLLM repo with its own quantisation
Download: Weights on Hugging Face
Ways to runOther ways
each opens step-by-stepRun in cloud
Runs on RTX 3060 12GB (8 GB headroom) · Q4_K_M
- Generation speed
- ~102 tok/s
- generation speed
- Cost per 1M output tokens
- $0.16
- per 1M output tokens
How Much VRAM Does SmolLM3 3B Instruct Heretic Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 1.8 GB | 4 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 64K context · ~372 tok/s | |
| 1.9 GB | 4.2 GB | 3-bit medium — 3-bit medium quantization | Fits: 64K context · ~336 tok/s | |
| 2.3 GB | 4.6 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 64K context · ~285 tok/s | |
| 2.6 GB | 4.9 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 64K context · ~248 tok/s | |
| 3.0 GB | 5.3 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 64K context · ~219 tok/s | |
| 3.5 GB | 5.8 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 64K context · ~186 tok/s | |
| 6.6 GB | 8.9 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 64K context · ~99 tok/s |
Selected Q4_K_M
4-bit medium quantization3.4GBat 16K context
- Weights
- 1.9 GB
- KV cache
- 1.2 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 20.6 GB free · ~285 tok/s
≥50 tok/s needs ≥177 GB/s · RTX 5090 ~506, RTX 3090 Ti ~285
Which Hardware Can Run SmolLM3 3B Instruct Heretic?
Q4_K_M · 2.3 GBSmolLM3 3B Instruct Heretic (Q4_K_M) requires 2.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 3+ GB is recommended. Using the full 66K context window can add up to 4.7 GB, bringing total usage to 7.0 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run SmolLM3 3B Instruct Heretic, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 4.6 GB free · up to 64K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~215Simulate output on RTX 3080RTX 2080 Ti~174Simulate output on RTX 2080 TiRTX 3070 Ti~172Simulate output on RTX 3070 TiM3*8 GB~31Simulate output on iMac 24" M3 (8 GB)Orin Nano~29Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~21Simulate output on iPhone 1720 GB
fits · 16.6 GB free · up to 64K ctx
1 GPU
RX 7900 XT~209Simulate output on RX 7900 XT32–40 GB
fits · 28.6 GB free · up to 64K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~506Simulate output on RTX 5090M5 Max36 GB~140Simulate output on Mac Studio M5 Max (36 GB)M532 GB~47Simulate output on MacBook Air 13" M5 (32 GB)Orin~58Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~38Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to SmolLM3 3B Instruct Heretic
SmolLM family · 15 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| D1 3BLiquid AI | — | 2.6 GB | ~285 | same size |
| Llama 3.2 3B InstructMeta | — | 4.1 GB | ~309 | same size |
| AI21 Jamba Reasoning 3BAI21 Labs | — | 2.3 GB | ~291 | same size |
| Granite 3.1 3B A800m InstructIBM | — | 3.4 GB | ~364 | same size |
| Qwen2.5 3B InstructAlibaba | — | 2.8 GB | ~294 | same size |
| MiniMax M3 EAGLE3Inferact | — | 2.7 GB | ~284 | same size |
Specifications
- Publisher
- K0D3IN
- Family
- SmolLM
- Parameters
- 3.1B
- Architecture
- SmolLM3ForCausalLM
- Context Length
- 65,536 tokens
- Vocabulary Size
- 128,256
- Release Date
- 2026-06-04
- License
- Apache 2.0
Frequently Asked Questions
How much VRAM does SmolLM3 3B Instruct Heretic need?
SmolLM3 3B Instruct Heretic requires 2.3 GB of VRAM at Q4_K_M, or 6.6 GB at BF16. Full 66K context adds up to 4.7 GB (7.0 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 3.1B × 4.8 bits ÷ 8 = 1.8 GB
KV Cache + Overhead ≈ 0.5 GB (at 2K context + ~0.3 GB framework)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
KV Cache + Overhead ≈ 5.2 GB (at full 66K context)
VRAM usage by quantization
What's the best quantization for SmolLM3 3B Instruct Heretic?
For SmolLM3 3B Instruct Heretic, Q4_K_M (2.3 GB) offers the best balance of quality and VRAM usage. Q5_K_M (2.6 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 1.8 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run SmolLM3 3B Instruct Heretic on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run SmolLM3 3B Instruct Heretic. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 2.3 GB of usable unified memory (RAM minus macOS overhead).
Can I run SmolLM3 3B Instruct Heretic locally?
Yes — SmolLM3 3B Instruct Heretic can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is SmolLM3 3B Instruct Heretic?
At Q4_K_M, SmolLM3 3B Instruct Heretic can reach ~2087 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~285 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 2.3 × 0.65 = ~2261 tok/s
Estimated speed at Q4_K_M (2.3 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of SmolLM3 3B Instruct Heretic?
At Q4_K_M, the download is about 1.85 GB. The full-precision BF16 version is 6.15 GB. The smallest option (Q2_K) is 1.31 GB.
Which GPUs can run SmolLM3 3B Instruct Heretic?
52 consumer GPUs can run SmolLM3 3B Instruct Heretic at Q4_K_M. Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 52 GPUs have plenty of headroom for comfortable inference.
Which devices can run SmolLM3 3B Instruct Heretic?
163 devices with unified memory can run SmolLM3 3B Instruct Heretic at Q4_K_M (2.3 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.