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.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 8K)Best on 24 GB (Q4_K_M · ~1724 tok/s · 8K)Macs from 8 GB (Q4_K_M · ~189 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run SmolLM2 70M
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve codelion/SmolLM2-70M --max-model-len 8192vLLM repo with its own quantisation
Download: Weights on Hugging Face
Other ways
each opens step-by-stepRun in cloud
Fits on RTX 3060 12GB (11 GB headroom) · Q4_K_M
- Generation speed
- ~616 tok/s
- generation speed
- Cost per 1M output tokens
- $0.037
- per 1M output tokens
How Much VRAM Does SmolLM2 70M Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 0.4 GB | — | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 8K context · ~1820 tok/s | |
| 0.4 GB | — | 3-bit medium — 3-bit medium quantization | Fits: 8K context · ~1771 tok/s | |
| 0.4 GB | — | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 8K context · ~1724 tok/s | |
| 0.4 GB | — | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 8K context · ~1724 tok/s | |
| 0.4 GB | — | 6-bit, very good — 6-bit quantization, very good quality | Fits: 8K context · ~1680 tok/s | |
| 0.4 GB | — | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 8K context · ~1638 tok/s | |
| 0.5 GB | — | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 8K context · ~1394 tok/s |
Selected Q4_K_M
4-bit medium quantization0.5GBat 8K context
- Weights
- 0.1 GB
- KV cache
- 0.1 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 23.5 GB free · ~1724 tok/s
≥50 tok/s needs ≥29 GB/s · RTX 5090 ~3065, RTX 3090 Ti ~1724
Which Hardware 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. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run SmolLM2 70M, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 7.5 GB free · up to 8K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~1301Simulate output on RTX 3080RTX 2080 Ti~1054Simulate output on RTX 2080 TiRTX 3070 Ti~1041Simulate output on RTX 3070 TiM3*8 GB~189Simulate output on iMac 24" M3 (8 GB)Orin Nano~175Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~126Simulate output on iPhone 1720 GB
fits · 19.5 GB free · up to 8K ctx
1 GPU
RX 7900 XT~1263Simulate output on RX 7900 XT32–40 GB
fits · 31.5 GB free · up to 8K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~3065Simulate output on RTX 5090M5 Max36 GB~847Simulate output on Mac Studio M5 Max (36 GB)M532 GB~283Simulate output on MacBook Air 13" M5 (32 GB)Orin~350Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~231Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to SmolLM2 70M
SmolLM family · 15 models →Similar models
1| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Llama 68MJackFram | — | 0 GB | ~16380 | same size |
Specifications
- 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
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)
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 ≈ 0.5 GB (at full 8K context)
VRAM usage by quantization
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
★ Recommended — best balance of quality and VRAM usage.
Can I run SmolLM2 70M on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run SmolLM2 70M. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 0.4 GB of usable unified memory (RAM minus macOS overhead).
Can I run SmolLM2 70M locally?
Yes — SmolLM2 70M can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is SmolLM2 70M?
At Q4_K_M, SmolLM2 70M can reach ~12632 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 ÷ 0.38 × 0.65 = ~13684 tok/s
Estimated speed at Q4_K_M (0.4 GB)
Real-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?
52 consumer GPUs can run SmolLM2 70M 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 SmolLM2 70M?
163 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, 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.