Tiny Qwen2ForCausalLM 2.5 — Hardware Requirements & GPU Compatibility
Chat
Tiny Qwen2ForCausalLM 2.5 is a 2M-parameter open language model from trl-internal-testing in the Qwen 2 family. It supports a context window of up to 32,768 tokens. At Q4_K_M it needs about 0.30 GB of VRAM — see which GPUs and Macs can run it below.
5.5M downloads
7 likesSpecifications
- Publisher
- trl-internal-testing
- Family
- Qwen 2
- Parameters
- 2M
- Architecture
- Qwen2ForCausalLM
- Context Length
- 32,768 tokens
- Vocabulary Size
- 151,665
- Release Date
- 2025-12-19
Get Started
How Much VRAM Does Tiny Qwen2ForCausalLM 2.5 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 0.3 GB | 0.3 GB | 0.00 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 0.3 GB | 0.3 GB | 0.00 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 0.3 GB | 0.3 GB | 0.00 GB | 3-bit medium quantization |
| Q4_K_M | 4.80 | 0.3 GB | 0.3 GB | 0.00 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 0.3 GB | 0.3 GB | 0.00 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 0.3 GB | 0.3 GB | 0.00 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 0.3 GB | 0.3 GB | 0.00 GB | 8-bit quantization, near-lossless |
| Q4_0 | 4.00 | 0.3 GB | 0.3 GB | 0.00 GB | 4-bit legacy quantization |
Which GPUs Can Run Tiny Qwen2ForCausalLM 2.5?
Q4_K_M · 0.3 GBTiny Qwen2ForCausalLM 2.5 (Q4_K_M) requires 0.3 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 Tiny Qwen2ForCausalLM 2.5?
Q4_K_M · 0.3 GB33 devices with unified memory can run Tiny Qwen2ForCausalLM 2.5, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Tiny Qwen2ForCausalLM 2.5 need?
Tiny Qwen2ForCausalLM 2.5 requires 0.3 GB of VRAM at Q4_K_M, or 0.3 GB at Q5_1.
VRAM = Weights + KV Cache + Overhead
Weights = 2M × 4.8 bits ÷ 8 = 0 GB
KV Cache + Overhead ≈ 0.3 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 0.3 GB (at full 33K context)
VRAM usage by quantization
Q4_K_M0.3 GBQ4_K_M + full context0.3 GB- What's the best quantization for Tiny Qwen2ForCausalLM 2.5?
For Tiny Qwen2ForCausalLM 2.5, Q4_K_M (0.3 GB) offers the best balance of quality and VRAM usage. Q5_K_S (0.3 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 0.3 GB.
VRAM requirement by quantization
Q2_K0.3 GBQ4_K_S0.3 GBQ4_K_M ★0.3 GBQ6_K0.3 GBIQ4_NL0.3 GBQ5_10.3 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Tiny Qwen2ForCausalLM 2.5 on a Mac?
Tiny Qwen2ForCausalLM 2.5 requires at least 0.3 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 Tiny Qwen2ForCausalLM 2.5 locally?
Yes — Tiny Qwen2ForCausalLM 2.5 can run locally on consumer hardware. At Q4_K_M quantization it needs 0.3 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Tiny Qwen2ForCausalLM 2.5?
At Q4_K_M, Tiny Qwen2ForCausalLM 2.5 can reach ~9717 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~2184 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 ÷ 0.3 × 0.55 = ~9717 tok/s
Estimated speed at Q4_K_M (0.3 GB)
~9717 tok/s~2184 tok/s~7263 tok/s~6008 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Tiny Qwen2ForCausalLM 2.5?
At Q4_K_M, the download is about 0.00 GB. The full-precision Q5_1 version is 0.00 GB. The smallest option (Q2_K) is 0.00 GB.
- Which GPUs can run Tiny Qwen2ForCausalLM 2.5?
35 consumer GPUs can run Tiny Qwen2ForCausalLM 2.5 at Q4_K_M (0.3 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 Tiny Qwen2ForCausalLM 2.5?
33 devices with unified memory can run Tiny Qwen2ForCausalLM 2.5 at Q4_K_M (0.3 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.