MN 12B Starcannon v3 — Hardware Requirements & GPU Compatibility
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45 downloads
26 likesSpecifications
- Publisher
- AuriAetherwiing
- Parameters
- 12B
- Architecture
- MistralForCausalLM
- Context Length
- 1,024,000 tokens
- Vocabulary Size
- 131,072
- Release Date
- 2024-08-06
Get Started
HuggingFace
How Much VRAM Does MN 12B Starcannon v3 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 24.7 GB | 234.0 GB | 24.00 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run MN 12B Starcannon v3?
BF16 · 24.7 GBMN 12B Starcannon v3 (BF16) requires 24.7 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 33+ GB is recommended. Using the full 1024K context window can add up to 209.3 GB, bringing total usage to 234.0 GB. 1 GPU can run it, including NVIDIA GeForce RTX 5090.
All compatible consumer-level GPUs are running near their VRAM limit. You may also want to consider professional GPUs (e.g., NVIDIA A100, H100) which offer significantly more VRAM. For more headroom and better throughput, consider a multi-GPU configuration with tensor parallelism (supported by tools like vLLM, llama.cpp, or text-generation-inference).
Decent
— Enough VRAM, may be tightWhich Devices Can Run MN 12B Starcannon v3?
BF16 · 24.7 GB15 devices with unified memory can run MN 12B Starcannon v3, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio M4 Max (36 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightRelated Models
Frequently Asked Questions
- How much VRAM does MN 12B Starcannon v3 need?
MN 12B Starcannon v3 requires 24.7 GB of VRAM at BF16. Full 1024K context adds up to 209.3 GB (234.0 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 12B × 16 bits ÷ 8 = 24 GB
KV Cache + Overhead ≈ 0.7 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 210 GB (at full 1024K context)
VRAM usage by quantization
BF1624.7 GBBF16 + full context234.0 GB- Can I run MN 12B Starcannon v3 on a Mac?
MN 12B Starcannon v3 requires at least 24.7 GB at BF16, 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 MN 12B Starcannon v3 locally?
Yes — MN 12B Starcannon v3 can run locally on consumer hardware. At BF16 quantization it needs 24.7 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is MN 12B Starcannon v3?
At BF16, MN 12B Starcannon v3 can reach ~118 tok/s on AMD Instinct MI300X. 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 ÷ 24.7 × 0.55 = ~118 tok/s
Estimated speed at BF16 (24.7 GB)
AMD Instinct MI300X~118 tok/sNVIDIA H100 SXM~88 tok/sAMD Instinct MI250X~73 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of MN 12B Starcannon v3?
At BF16, the download is about 24.00 GB.
- Which GPUs can run MN 12B Starcannon v3?
1 consumer GPU can run MN 12B Starcannon v3 at BF16 (24.7 GB). Top options include NVIDIA GeForce RTX 5090.
- Which devices can run MN 12B Starcannon v3?
15 devices with unified memory can run MN 12B Starcannon v3 at BF16 (24.7 GB), including Mac Mini M4 (32 GB), Mac Mini M4 Pro (48 GB), Mac Pro M2 Ultra (192 GB), Mac Studio M2 Ultra (192 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.