SmolLM2 135M Instruct — Hardware Requirements & GPU Compatibility
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SmolLM2 135M Instruct is the instruction-tuned variant of Hugging Face's 135-million-parameter SmolLM2 model. Fine-tuned to follow user prompts and engage in basic conversational exchanges, it delivers surprisingly coherent responses given its minimal size, making it ideal for testing chat interfaces or running on extremely constrained devices. This model is a practical choice when you need an instruction-following model that fits comfortably in under 1 GB of memory. It works well for simple question answering, text reformatting, and lightweight assistant tasks where response quality can be traded for instant inference speed.
1.7M downloads
346 likesSpecifications
- Publisher
- Hugging Face
- Family
- SmolLM
- Parameters
- 135M
- Architecture
- LlamaForCausalLM
- Context Length
- 8,192 tokens
- Vocabulary Size
- 49,152
- Release Date
- 2024-10-31
- License
- Apache 2.0
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HuggingFace
How Much VRAM Does SmolLM2 135M Instruct Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 0.4 GB | 0.6 GB | 0.06 GB | 2-bit quantization with K-quant improvements |
| Q3_K_M | 3.90 | 0.4 GB | 0.6 GB | 0.07 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 0.4 GB | 0.6 GB | 0.07 GB | 4-bit legacy quantization |
| Q3_K_S | 3.50 | 0.4 GB | 0.6 GB | 0.06 GB | 3-bit small quantization |
| Q4_K_M | 4.80 | 0.4 GB | 0.6 GB | 0.08 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 0.4 GB | 0.6 GB | 0.10 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 0.5 GB | 0.6 GB | 0.11 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 0.5 GB | 0.6 GB | 0.13 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 135M Instruct?
Q4_K_M · 0.4 GBSmolLM2 135M Instruct (Q4_K_M) requires 0.4 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 0.1 GB, bringing total usage to 0.6 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 135M Instruct?
Q4_K_M · 0.4 GB59 devices with unified memory can run SmolLM2 135M Instruct, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomWhere to Download SmolLM2 135M 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 135M Instruct need?
SmolLM2 135M Instruct requires 0.4 GB of VRAM at Q4_K_M, or 0.6 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 135M × 4.8 bits ÷ 8 = 0.1 GB
KV Cache + Overhead ≈ 0.3 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 0.5 GB (at full 8K context)
VRAM usage by quantization
Q4_K_M0.4 GBQ4_K_M + full context0.6 GB- What's the best quantization for SmolLM2 135M Instruct?
For SmolLM2 135M Instruct, Q4_K_M (0.4 GB) offers the best balance of quality and VRAM usage. Q4_K_L (0.4 GB) provides better quality if you have the VRAM. The smallest option is IQ3_XS at 0.4 GB.
VRAM requirement by quantization
IQ3_XS0.4 GBIQ3_M0.4 GBQ4_K_S0.4 GBQ4_K_M ★0.4 GBQ5_K_S0.4 GBBF160.6 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run SmolLM2 135M Instruct on a Mac?
SmolLM2 135M Instruct requires at least 0.4 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 135M Instruct locally?
Yes — SmolLM2 135M Instruct can run locally on consumer hardware. At Q4_K_M quantization it needs 0.4 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is SmolLM2 135M Instruct?
At Q4_K_M, SmolLM2 135M Instruct can reach ~10233 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~1524 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 ÷ 0.4 × 0.65 = ~12093 tok/s
Estimated speed at Q4_K_M (0.4 GB)
~12093 tok/s~1524 tok/s~12093 tok/s~10233 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of SmolLM2 135M Instruct?
At Q4_K_M, the download is about 0.08 GB. The full-precision BF16 version is 0.27 GB. The smallest option (IQ3_XS) is 0.06 GB.
- Which GPUs can run SmolLM2 135M Instruct?
50 consumer GPUs can run SmolLM2 135M Instruct at Q4_K_M (0.4 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 135M Instruct?
59 devices with unified memory can run SmolLM2 135M Instruct at Q4_K_M (0.4 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.