EVA Qwen2.5 14B v0.2 — Hardware Requirements & GPU Compatibility
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138 downloads
29 likesSpecifications
- Publisher
- EVA-UNIT-01
- Family
- Qwen 2.5
- Parameters
- 14.8B
- Architecture
- Qwen2ForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 152,064
- Release Date
- 2024-11-14
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does EVA Qwen2.5 14B v0.2 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 7.0 GB | 32.4 GB | 6.28 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 7.2 GB | 32.5 GB | 6.46 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 7.9 GB | 33.3 GB | 7.20 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 8.1 GB | 33.5 GB | 7.39 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 9.6 GB | 34.9 GB | 8.86 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 11.2 GB | 36.6 GB | 10.52 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 12.9 GB | 38.3 GB | 12.19 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 15.5 GB | 40.8 GB | 14.77 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run EVA Qwen2.5 14B v0.2?
Q4_K_M · 9.6 GBEVA Qwen2.5 14B v0.2 (Q4_K_M) requires 9.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 13+ GB is recommended. Using the full 131K context window can add up to 25.4 GB, bringing total usage to 34.9 GB. 28 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3080 Ti.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run EVA Qwen2.5 14B v0.2?
Q4_K_M · 9.6 GB27 devices with unified memory can run EVA Qwen2.5 14B v0.2, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does EVA Qwen2.5 14B v0.2 need?
EVA Qwen2.5 14B v0.2 requires 9.6 GB of VRAM at Q4_K_M, or 15.5 GB at Q8_0. Full 131K context adds up to 25.4 GB (34.9 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 14.8B × 4.8 bits ÷ 8 = 8.9 GB
KV Cache + Overhead ≈ 0.7 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 26 GB (at full 131K context)
VRAM usage by quantization
Q4_K_M9.6 GBQ4_K_M + full context34.9 GB- What's the best quantization for EVA Qwen2.5 14B v0.2?
For EVA Qwen2.5 14B v0.2, Q4_K_M (9.6 GB) offers the best balance of quality and VRAM usage. Q5_0 (9.9 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 7.0 GB.
VRAM requirement by quantization
Q2_K7.0 GB~75%Q4_08.1 GB~85%Q4_K_M ★9.6 GB~89%Q5_09.9 GB~90%Q5_K_S10.9 GB~92%Q8_015.5 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run EVA Qwen2.5 14B v0.2 on a Mac?
EVA Qwen2.5 14B v0.2 requires at least 7.0 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 EVA Qwen2.5 14B v0.2 locally?
Yes — EVA Qwen2.5 14B v0.2 can run locally on consumer hardware. At Q4_K_M quantization it needs 9.6 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is EVA Qwen2.5 14B v0.2?
At Q4_K_M, EVA Qwen2.5 14B v0.2 can reach ~305 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~69 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 ÷ 9.6 × 0.55 = ~305 tok/s
Estimated speed at Q4_K_M (9.6 GB)
AMD Instinct MI300X~305 tok/sNVIDIA GeForce RTX 4090~69 tok/sNVIDIA H100 SXM~228 tok/sAMD Instinct MI250X~189 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of EVA Qwen2.5 14B v0.2?
At Q4_K_M, the download is about 8.86 GB. The full-precision Q8_0 version is 14.77 GB. The smallest option (Q2_K) is 6.28 GB.
- Which GPUs can run EVA Qwen2.5 14B v0.2?
28 consumer GPUs can run EVA Qwen2.5 14B v0.2 at Q4_K_M (9.6 GB). Top options include AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 6900 XT, AMD Radeon RX 6700 XT. 17 GPUs have plenty of headroom for comfortable inference.
- Which devices can run EVA Qwen2.5 14B v0.2?
27 devices with unified memory can run EVA Qwen2.5 14B v0.2 at Q4_K_M (9.6 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.