Mellum2 12B A2.5B Instruct — Hardware Requirements & GPU Compatibility
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Mellum2 12B A2.5B Instruct is a 12.1B-parameter open language model from JetBrains. It supports a context window of up to 131,072 tokens. At Q4_K_M it needs about 7.66 GB of VRAM — see which GPUs and Macs can run it below.
1.9K downloads
53 likesSpecifications
- Publisher
- JetBrains
- Parameters
- 12.1B
- Architecture
- MellumForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 98,304
- Release Date
- 2026-06-01
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Mellum2 12B A2.5B Instruct Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q4_0 | 4.00 | 6.4 GB | 10.6 GB | 6.07 GB | 4-bit legacy quantization |
| Q4_K_S | 4.50 | 7.2 GB | 11.4 GB | 6.83 GB | 4-bit small quantization |
| Q4_K_M | 4.80 | 7.7 GB | 11.8 GB | 7.29 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 9.0 GB | 13.2 GB | 8.66 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 10.4 GB | 14.6 GB | 10.02 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 12.5 GB | 16.7 GB | 12.15 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Mellum2 12B A2.5B Instruct?
Q4_K_M · 7.7 GBMellum2 12B A2.5B Instruct (Q4_K_M) requires 7.7 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 10+ GB is recommended. Using the full 131K context window can add up to 4.2 GB, bringing total usage to 11.8 GB. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3080.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run Mellum2 12B A2.5B Instruct?
Q4_K_M · 7.7 GB33 devices with unified memory can run Mellum2 12B A2.5B Instruct, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, MacBook Air 13" M3 (8 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightRelated Models
Derivatives (5)
Frequently Asked Questions
- How much VRAM does Mellum2 12B A2.5B Instruct need?
Mellum2 12B A2.5B Instruct requires 7.7 GB of VRAM at Q4_K_M, or 12.5 GB at Q8_0. Full 131K context adds up to 4.2 GB (11.8 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 12.1B × 4.8 bits ÷ 8 = 7.3 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 4.5 GB (at full 131K context)
VRAM usage by quantization
Q4_K_M7.7 GBQ4_K_M + full context11.8 GB- What's the best quantization for Mellum2 12B A2.5B Instruct?
For Mellum2 12B A2.5B Instruct, Q4_K_M (7.7 GB) offers the best balance of quality and VRAM usage. Q5_K_M (9.0 GB) provides better quality if you have the VRAM. The smallest option is Q4_0 at 6.4 GB.
VRAM requirement by quantization
Q4_06.4 GBQ4_K_S7.2 GBQ4_K_M ★7.7 GBQ5_K_M9.0 GBQ6_K10.4 GBQ8_012.5 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Mellum2 12B A2.5B Instruct on a Mac?
Mellum2 12B A2.5B Instruct requires at least 6.4 GB at Q4_0, 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 Mellum2 12B A2.5B Instruct locally?
Yes — Mellum2 12B A2.5B Instruct can run locally on consumer hardware. At Q4_K_M quantization it needs 7.7 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Mellum2 12B A2.5B Instruct?
At Q4_K_M, Mellum2 12B A2.5B Instruct can reach ~381 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~86 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 ÷ 7.7 × 0.55 = ~381 tok/s
Estimated speed at Q4_K_M (7.7 GB)
~381 tok/s~86 tok/s~284 tok/s~235 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Mellum2 12B A2.5B Instruct?
At Q4_K_M, the download is about 7.29 GB. The full-precision Q8_0 version is 12.15 GB. The smallest option (Q4_0) is 6.07 GB.
- Which GPUs can run Mellum2 12B A2.5B Instruct?
35 consumer GPUs can run Mellum2 12B A2.5B Instruct at Q4_K_M (7.7 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 26 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Mellum2 12B A2.5B Instruct?
33 devices with unified memory can run Mellum2 12B A2.5B Instruct at Q4_K_M (7.7 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.