SmolLM2 70M — Hardware Requirements & GPU Compatibility
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SmolLM2 70M is a 69M-parameter open language model from codelion in the SmolLM family. It supports a context window of up to 8,192 tokens. At Q4_K_M it needs about 0.38 GB of VRAM — see which GPUs and Macs can run it below.
197 downloads
3 likesSpecifications
- Publisher
- codelion
- Family
- SmolLM
- Parameters
- 69M
- Architecture
- LlamaForCausalLM
- Context Length
- 8,192 tokens
- Vocabulary Size
- 49,152
- Release Date
- 2026-03-02
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does SmolLM2 70M Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 0.4 GB | 0.5 GB | 0.03 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 0.4 GB | 0.5 GB | 0.03 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 0.4 GB | 0.5 GB | 0.04 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 0.4 GB | 0.5 GB | 0.05 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 0.4 GB | 0.5 GB | 0.06 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 0.4 GB | 0.5 GB | 0.07 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 0.5 GB | 0.6 GB | 0.14 GB | Brain floating point 16 — preferred for training |
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 70M?
Q4_K_M · 0.4 GBSmolLM2 70M (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 0.5 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 70M?
Q4_K_M · 0.4 GB59 devices with unified memory can run SmolLM2 70M, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does SmolLM2 70M need?
SmolLM2 70M requires 0.4 GB of VRAM at Q4_K_M, or 0.5 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 69M × 4.8 bits ÷ 8 = 0 GB
KV Cache + Overhead ≈ 0.4 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 context0.5 GB- What's the best quantization for SmolLM2 70M?
For SmolLM2 70M, Q4_K_M (0.4 GB) offers the best balance of quality and VRAM usage. Q5_K_M (0.4 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 0.4 GB.
VRAM requirement by quantization
Q2_K0.4 GBQ4_K_M ★0.4 GBQ5_K_M0.4 GBQ6_K0.4 GBQ8_00.4 GBBF160.5 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run SmolLM2 70M on a Mac?
SmolLM2 70M requires at least 0.4 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 70M locally?
Yes — SmolLM2 70M 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 70M?
At Q4_K_M, SmolLM2 70M can reach ~11579 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~1724 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 = ~13684 tok/s
Estimated speed at Q4_K_M (0.4 GB)
~13684 tok/s~1724 tok/s~13684 tok/s~11579 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of SmolLM2 70M?
At Q4_K_M, the download is about 0.04 GB. The full-precision BF16 version is 0.14 GB. The smallest option (Q2_K) is 0.03 GB.
- Which GPUs can run SmolLM2 70M?
50 consumer GPUs can run SmolLM2 70M 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 70M?
59 devices with unified memory can run SmolLM2 70M 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.