SmolLM 135M Instruct FP32 — Hardware Requirements & GPU Compatibility
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SmolLM 135M Instruct FP32 is a 135M-parameter open language model from modularai in the SmolLM family. It supports a context window of up to 2,048 tokens. At Q4_K_M it needs about 0.43 GB of VRAM — see which GPUs and Macs can run it below.
38.5K downloadsSpecifications
- Publisher
- modularai
- Family
- SmolLM
- Parameters
- 135M
- Architecture
- LlamaForCausalLM
- Context Length
- 2,048 tokens
- Vocabulary Size
- 49,152
- Release Date
- 2025-07-31
Get Started
HuggingFace
Run in cloud
Fits on RTX 3060 12GB (11 GB headroom) · Q4_K_M
- Generation speed
- ~544 tok/s
- generation speed
- Cost per 1M output tokens
- $0.031
- per 1M output tokens
How Much VRAM Does SmolLM 135M Instruct FP32 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 0.4 GB | — | 0.06 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 0.4 GB | — | 0.07 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 0.4 GB | — | 0.08 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 0.4 GB | — | 0.10 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 0.5 GB | — | 0.11 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 0.5 GB | — | 0.13 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 0.6 GB | — | 0.27 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 SmolLM 135M Instruct FP32?
Q4_K_M · 0.4 GBSmolLM 135M Instruct FP32 (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. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run SmolLM 135M Instruct FP32?
Q4_K_M · 0.4 GB59 devices with unified memory can run SmolLM 135M Instruct FP32, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does SmolLM 135M Instruct FP32 need?
SmolLM 135M Instruct FP32 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)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
VRAM usage by quantization
Q4_K_M0.4 GB- What's the best quantization for SmolLM 135M Instruct FP32?
For SmolLM 135M Instruct FP32, 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_K0.5 GBQ8_00.5 GBBF160.6 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run SmolLM 135M Instruct FP32 on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 38 other Macs can run SmolLM 135M Instruct FP32. 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 SmolLM 135M Instruct FP32 locally?
Yes — SmolLM 135M Instruct FP32 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 SmolLM 135M Instruct FP32?
At Q4_K_M, SmolLM 135M Instruct FP32 can reach ~11163 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 ÷ 0.43 × 0.65 = ~12093 tok/s
Estimated speed at Q4_K_M (0.4 GB)
~12093 tok/s~1524 tok/s~12093 tok/s~11163 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of SmolLM 135M Instruct FP32?
At Q4_K_M, the download is about 0.08 GB. The full-precision BF16 version is 0.27 GB. The smallest option (Q2_K) is 0.06 GB.
- Which GPUs can run SmolLM 135M Instruct FP32?
52 consumer GPUs can run SmolLM 135M Instruct FP32 at Q4_K_M (0.4 GB). 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 SmolLM 135M Instruct FP32?
59 devices with unified memory can run SmolLM 135M Instruct FP32 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.