MiniLLM Gpt2 340M — Hardware Requirements & GPU Compatibility
Chat
MiniLLM Gpt2 340M is a 340M-parameter open language model from MiniLLM. It supports a context window of up to 1,024 tokens. At Q4_K_M it needs about 0.22 GB of VRAM — see which GPUs and Macs can run it below.
59 downloads
6 likesSpecifications
- Publisher
- MiniLLM
- Parameters
- 340M
- Architecture
- GPT2LMHeadModel
- Context Length
- 1,024 tokens
- Vocabulary Size
- 50,257
- Release Date
- 2024-09-26
- 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 |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 0.2 GB | — | 0.14 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 0.2 GB | — | 0.17 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 0.2 GB | — | 0.20 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 0.3 GB | — | 0.24 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 0.3 GB | — | 0.28 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 0.4 GB | — | 0.34 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 0.8 GB | — | 0.68 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 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. 50 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 GB59 devices with unified memory can run MiniLLM Gpt2 340M, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomFrequently Asked Questions
- How much VRAM does MiniLLM Gpt2 340M need?
MiniLLM Gpt2 340M requires 0.2 GB of VRAM at Q4_K_M, or 0.8 GB at BF16.
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_K_M (0.3 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 0.2 GB.
VRAM requirement by quantization
Q2_K0.2 GBQ4_K_M ★0.2 GBQ5_K_M0.3 GBQ6_K0.3 GBQ8_00.4 GBBF160.8 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run MiniLLM Gpt2 340M on a Mac?
MiniLLM Gpt2 340M requires at least 0.2 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 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 ~20000 tok/s on AMD Instinct MI350X. 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: NVIDIA B200 → 8000 ÷ 0.2 × 0.65 = ~23636 tok/s
Estimated speed at Q4_K_M (0.2 GB)
~23636 tok/s~2978 tok/s~23636 tok/s~20000 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 BF16 version is 0.68 GB. The smallest option (Q2_K) is 0.14 GB.
- Which GPUs can run MiniLLM Gpt2 340M?
50 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. 50 GPUs have plenty of headroom for comfortable inference.
- Which devices can run MiniLLM Gpt2 340M?
59 devices with unified memory can run MiniLLM Gpt2 340M at Q4_K_M (0.2 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.