Nitral-AI·MistralForCausalLM

Captain Eris Violet V0.420 12B — Hardware Requirements & GPU Compatibility

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151 downloads 67 likes1024K context

Specifications

Publisher
Nitral-AI
Parameters
12.2B
Architecture
MistralForCausalLM
Context Length
1,024,000 tokens
Vocabulary Size
131,072
Release Date
2025-05-11
License
Other

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How Much VRAM Does Captain Eris Violet V0.420 12B Need?

Select a quantization to see compatible GPUs below.

QuantizationBitsVRAM
Q2_K3.405.9 GB
Q3_K_S3.506.1 GB
Q3_K_M3.906.7 GB
Q4_04.006.8 GB
Q4_K_M4.808.1 GB
Q5_K_M5.709.4 GB
Q6_K6.6010.8 GB
Q8_08.0013.0 GB

Which GPUs Can Run Captain Eris Violet V0.420 12B?

Q4_K_M · 8.1 GB

Captain Eris Violet V0.420 12B (Q4_K_M) requires 8.1 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 11+ GB is recommended. Using the full 1024K context window can add up to 209.3 GB, bringing total usage to 217.4 GB. 28 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3080 Ti.

Which Devices Can Run Captain Eris Violet V0.420 12B?

Q4_K_M · 8.1 GB

27 devices with unified memory can run Captain Eris Violet V0.420 12B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.

Related Models

Frequently Asked Questions

How much VRAM does Captain Eris Violet V0.420 12B need?

Captain Eris Violet V0.420 12B requires 8.1 GB of VRAM at Q4_K_M, or 13.0 GB at Q8_0. Full 1024K context adds up to 209.3 GB (217.4 GB total).

VRAM = Weights + KV Cache + Overhead

Weights = 12.2B × 4.8 bits ÷ 8 = 7.3 GB

KV Cache + Overhead 0.8 GB (at 2K context + ~0.3 GB framework)

KV Cache + Overhead 210.1 GB (at full 1024K context)

VRAM usage by quantization

8.1 GB
217.4 GB

Learn more about VRAM estimation →

What's the best quantization for Captain Eris Violet V0.420 12B?

For Captain Eris Violet V0.420 12B, Q4_K_M (8.1 GB) offers the best balance of quality and VRAM usage. Q4_K_L (8.2 GB) provides better quality if you have the VRAM. The smallest option is IQ2_S at 4.5 GB.

VRAM requirement by quantization

IQ2_S
4.5 GB
IQ3_M
6.2 GB
IQ4_XS
7.3 GB
Q4_K_M
8.1 GB
Q5_K_S
9.1 GB
Q8_0
13.0 GB

★ Recommended — best balance of quality and VRAM usage.

Learn more about quantization →

Can I run Captain Eris Violet V0.420 12B on a Mac?

Captain Eris Violet V0.420 12B requires at least 4.5 GB at IQ2_S, 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 Captain Eris Violet V0.420 12B locally?

Yes — Captain Eris Violet V0.420 12B can run locally on consumer hardware. At Q4_K_M quantization it needs 8.1 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.

How fast is Captain Eris Violet V0.420 12B?

At Q4_K_M, Captain Eris Violet V0.420 12B can reach ~361 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~81 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 MI300X5300 ÷ 8.1 × 0.55 = ~361 tok/s

Estimated speed at Q4_K_M (8.1 GB)

~361 tok/s
~81 tok/s
~270 tok/s
~223 tok/s

Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.

Learn more about tok/s estimation →

What's the download size of Captain Eris Violet V0.420 12B?

At Q4_K_M, the download is about 7.35 GB. The full-precision Q8_0 version is 12.25 GB. The smallest option (IQ2_S) is 3.83 GB.

Which GPUs can run Captain Eris Violet V0.420 12B?

28 consumer GPUs can run Captain Eris Violet V0.420 12B at Q4_K_M (8.1 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 Captain Eris Violet V0.420 12B?

27 devices with unified memory can run Captain Eris Violet V0.420 12B at Q4_K_M (8.1 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.