MiniLLM Gpt2 340M — Hardware Requirements & GPU Compatibility
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59 downloads
6 likesSpecifications
- Publisher
- MiniLLM
- Parameters
- 340M
- Architecture
- GPT2LMHeadModel
- Context Length
- 1,024 tokens
- Vocabulary Size
- 50,257
- Release Date
- 2025-04-11
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does MiniLLM Gpt2 340M Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q3_K_S | 3.50 | 0.2 GB | — | 0.15 GB | 3-bit small quantization |
| Q2_K | 3.40 | 0.2 GB | — | 0.14 GB | 2-bit quantization with K-quant improvements |
| Q3_K_M | 3.90 | 0.2 GB | — | 0.17 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 0.2 GB | — | 0.17 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 0.2 GB | — | 0.20 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 0.3 GB | — | 0.24 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 0.3 GB | — | 0.28 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 0.4 GB | — | 0.34 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run MiniLLM Gpt2 340M?
Q4_K_M · 0.2 GBMiniLLM Gpt2 340M (Q4_K_M) requires 0.2 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 1+ GB is recommended. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run MiniLLM Gpt2 340M?
Q4_K_M · 0.2 GB33 devices with unified memory can run MiniLLM Gpt2 340M, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does MiniLLM Gpt2 340M need?
MiniLLM Gpt2 340M requires 0.2 GB of VRAM at Q4_K_M, or 0.4 GB at Q8_0.
VRAM = Weights + KV Cache + Overhead
Weights = 340M × 4.8 bits ÷ 8 = 0.2 GB
VRAM usage by quantization
Q4_K_M0.2 GB- What's the best quantization for MiniLLM Gpt2 340M?
For MiniLLM Gpt2 340M, Q4_K_M (0.2 GB) offers the best balance of quality and VRAM usage. Q5_0 (0.2 GB) provides better quality if you have the VRAM. The smallest option is IQ3_XS at 0.1 GB.
VRAM requirement by quantization
IQ3_XS0.1 GB~73%IQ3_M0.2 GB~78%Q4_K_S0.2 GB~88%Q4_K_M ★0.2 GB~89%Q5_10.3 GB~92%Q8_00.4 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run MiniLLM Gpt2 340M on a Mac?
MiniLLM Gpt2 340M requires at least 0.1 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 MiniLLM Gpt2 340M locally?
Yes — MiniLLM Gpt2 340M can run locally on consumer hardware. At Q4_K_M quantization it needs 0.2 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is MiniLLM Gpt2 340M?
At Q4_K_M, MiniLLM Gpt2 340M can reach ~13250 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~2978 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: AMD Instinct MI300X → 5300 ÷ 0.2 × 0.55 = ~13250 tok/s
Estimated speed at Q4_K_M (0.2 GB)
AMD Instinct MI300X~13250 tok/sNVIDIA GeForce RTX 4090~2978 tok/sNVIDIA H100 SXM~9904 tok/sAMD Instinct MI250X~8192 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of MiniLLM Gpt2 340M?
At Q4_K_M, the download is about 0.20 GB. The full-precision Q8_0 version is 0.34 GB. The smallest option (IQ3_XS) is 0.14 GB.
- Which GPUs can run MiniLLM Gpt2 340M?
35 consumer GPUs can run MiniLLM Gpt2 340M at Q4_K_M (0.2 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 35 GPUs have plenty of headroom for comfortable inference.
- Which devices can run MiniLLM Gpt2 340M?
33 devices with unified memory can run MiniLLM Gpt2 340M at Q4_K_M (0.2 GB), including Mac Mini M4 (16 GB), Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.