SmolLM2 360M — Hardware Requirements & GPU Compatibility
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SmolLM2 360M is a 362M-parameter open language model from Hugging Face. It supports a context window of up to 8,192 tokens. At Q4_K_M it needs about 0.60 GB of VRAM — see which GPUs and Macs can run it below.
60.8K downloads
104 likesSpecifications
- Publisher
- Hugging Face
- Parameters
- 362M
- Architecture
- LlamaForCausalLM
- Context Length
- 8,192 tokens
- Vocabulary Size
- 49,152
- Release Date
- 2025-02-06
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does SmolLM2 360M Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 0.5 GB | 0.8 GB | 0.15 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 0.5 GB | 0.8 GB | 0.16 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 0.6 GB | 0.8 GB | 0.18 GB | 3-bit medium quantization |
| Q4_K_M | 4.80 | 0.6 GB | 0.8 GB | 0.22 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 0.6 GB | 0.9 GB | 0.26 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 0.7 GB | 0.9 GB | 0.30 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 0.8 GB | 1 GB | 0.36 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run SmolLM2 360M?
Q4_K_M · 0.6 GBSmolLM2 360M (Q4_K_M) requires 0.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 1+ GB is recommended. Using the full 8K context window can add up to 0.3 GB, bringing total usage to 0.8 GB. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run SmolLM2 360M?
Q4_K_M · 0.6 GB33 devices with unified memory can run SmolLM2 360M, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Derivatives (2)
Frequently Asked Questions
- How much VRAM does SmolLM2 360M need?
SmolLM2 360M requires 0.6 GB of VRAM at Q4_K_M, or 0.8 GB at Q8_0.
VRAM = Weights + KV Cache + Overhead
Weights = 362M × 4.8 bits ÷ 8 = 0.2 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 0.6 GB (at full 8K context)
VRAM usage by quantization
Q4_K_M0.6 GBQ4_K_M + full context0.8 GB- What's the best quantization for SmolLM2 360M?
For SmolLM2 360M, Q4_K_M (0.6 GB) offers the best balance of quality and VRAM usage. Q5_K_S (0.6 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 0.5 GB.
VRAM requirement by quantization
Q2_K0.5 GBQ3_K_L0.6 GBQ4_K_S0.6 GBQ4_K_M ★0.6 GBQ5_K_M0.6 GBQ8_00.8 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run SmolLM2 360M on a Mac?
SmolLM2 360M requires at least 0.5 GB at Q2_K, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run SmolLM2 360M locally?
Yes — SmolLM2 360M can run locally on consumer hardware. At Q4_K_M quantization it needs 0.6 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is SmolLM2 360M?
At Q4_K_M, SmolLM2 360M can reach ~4858 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~1092 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: AMD Instinct MI300X → 5300 ÷ 0.6 × 0.55 = ~4858 tok/s
Estimated speed at Q4_K_M (0.6 GB)
~4858 tok/s~1092 tok/s~3631 tok/s~3004 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of SmolLM2 360M?
At Q4_K_M, the download is about 0.22 GB. The full-precision Q8_0 version is 0.36 GB. The smallest option (Q2_K) is 0.15 GB.
- Which GPUs can run SmolLM2 360M?
35 consumer GPUs can run SmolLM2 360M at Q4_K_M (0.6 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 35 GPUs have plenty of headroom for comfortable inference.
- Which devices can run SmolLM2 360M?
33 devices with unified memory can run SmolLM2 360M at Q4_K_M (0.6 GB), including Mac Mini M4 (16 GB), Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.