SmolLM2 360M Instruct — Hardware Requirements & GPU Compatibility
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SmolLM2 360M Instruct is an instruction-tuned model from Hugging Face that occupies the sweet spot between the 135M and 1.7B entries in the SmolLM2 lineup. At 360 million parameters, it offers noticeably better coherence and instruction-following ability than the smallest variants while still running comfortably on virtually any modern GPU or even on CPU. This model is well suited for on-device assistants, embedded applications, and rapid prototyping where you need real conversational ability without dedicating significant hardware resources. It handles short-form generation, summarization, and basic reasoning tasks with reasonable quality.
283.9K downloads
193 likesSpecifications
- Publisher
- Hugging Face
- Family
- SmolLM
- Parameters
- 362M
- Architecture
- LlamaForCausalLM
- Context Length
- 8,192 tokens
- Vocabulary Size
- 49,152
- Release Date
- 2024-10-31
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does SmolLM2 360M Instruct 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 |
| Q4_0 | 4.00 | 0.6 GB | 0.8 GB | 0.18 GB | 4-bit legacy 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 |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run SmolLM2 360M Instruct?
Q4_K_M · 0.6 GBSmolLM2 360M Instruct (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. 50 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run SmolLM2 360M Instruct?
Q4_K_M · 0.6 GB59 devices with unified memory can run SmolLM2 360M Instruct, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomWhere to Download SmolLM2 360M Instruct
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Related Models
Frequently Asked Questions
- How much VRAM does SmolLM2 360M Instruct need?
SmolLM2 360M Instruct requires 0.6 GB of VRAM at Q4_K_M, or 1.1 GB at BF16.
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 Instruct?
For SmolLM2 360M Instruct, Q4_K_M (0.6 GB) offers the best balance of quality and VRAM usage. Q4_K_L (0.6 GB) provides better quality if you have the VRAM. The smallest option is IQ3_XS at 0.5 GB.
VRAM requirement by quantization
IQ3_XS0.5 GBQ4_00.6 GBQ4_K_S0.6 GBQ4_K_M ★0.6 GBQ5_K_M0.6 GBBF161.1 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run SmolLM2 360M Instruct on a Mac?
SmolLM2 360M Instruct requires at least 0.5 GB at IQ3_XS, 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 Instruct locally?
Yes — SmolLM2 360M Instruct 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 Instruct?
At Q4_K_M, SmolLM2 360M Instruct can reach ~7333 tok/s on AMD Instinct MI350X. 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: NVIDIA B200 → 8000 ÷ 0.6 × 0.65 = ~8667 tok/s
Estimated speed at Q4_K_M (0.6 GB)
~8667 tok/s~1092 tok/s~8667 tok/s~7333 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 Instruct?
At Q4_K_M, the download is about 0.22 GB. The full-precision BF16 version is 0.72 GB. The smallest option (IQ3_XS) is 0.15 GB.
- Which GPUs can run SmolLM2 360M Instruct?
50 consumer GPUs can run SmolLM2 360M Instruct at Q4_K_M (0.6 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 50 GPUs have plenty of headroom for comfortable inference.
- Which devices can run SmolLM2 360M Instruct?
59 devices with unified memory can run SmolLM2 360M Instruct at Q4_K_M (0.6 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Apple iPhone 17 Pro, Asus ROG Flow Z13 (2025, 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.