Veyra 30M Base — Hardware Requirements & GPU Compatibility
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Veyra 30M Base is a 35M-parameter open language model from veyra-ai. It supports a context window of up to 1,024 tokens. At Q4_K_M it needs about 0.33 GB of VRAM — see which GPUs and Macs can run it below.
2.1K downloads
2 likesSpecifications
- Publisher
- veyra-ai
- Parameters
- 35M
- Architecture
- LlamaForCausalLM
- Context Length
- 1,024 tokens
- Vocabulary Size
- 8,192
- Release Date
- 2026-05-03
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Veyra 30M Base Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 0.3 GB | — | 0.01 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 0.3 GB | — | 0.02 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 0.3 GB | — | 0.02 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 0.3 GB | — | 0.02 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 0.3 GB | — | 0.03 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 0.3 GB | — | 0.03 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 0.4 GB | — | 0.07 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 Veyra 30M Base?
Q4_K_M · 0.3 GBVeyra 30M Base (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 Veyra 30M Base?
Q4_K_M · 0.3 GB33 devices with unified memory can run Veyra 30M Base, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomFrequently Asked Questions
- How much VRAM does Veyra 30M Base need?
Veyra 30M Base requires 0.3 GB of VRAM at Q4_K_M, or 0.4 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 35M × 4.8 bits ÷ 8 = 0 GB
KV Cache + Overhead ≈ 0.3 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M0.3 GB- What's the best quantization for Veyra 30M Base?
For Veyra 30M Base, Q4_K_M (0.3 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 0.3 GB.
VRAM requirement by quantization
Q2_K0.3 GBQ4_K_M ★0.3 GBQ5_K_M0.3 GBQ6_K0.3 GBQ8_00.3 GBBF160.4 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Veyra 30M Base on a Mac?
Veyra 30M Base 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 Veyra 30M Base locally?
Yes — Veyra 30M Base 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 Veyra 30M Base?
At Q4_K_M, Veyra 30M Base can reach ~8833 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~1986 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 = ~8833 tok/s
Estimated speed at Q4_K_M (0.3 GB)
~8833 tok/s~1986 tok/s~6602 tok/s~5461 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Veyra 30M Base?
At Q4_K_M, the download is about 0.02 GB. The full-precision BF16 version is 0.07 GB. The smallest option (Q2_K) is 0.01 GB.
- Which GPUs can run Veyra 30M Base?
35 consumer GPUs can run Veyra 30M Base 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 Veyra 30M Base?
33 devices with unified memory can run Veyra 30M Base 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.